Covid-19 Policy Helper
Xinyi Lai, Yulin Li
Aug 2020
Introduction
This is part 3 of the project “Covid-19 Policy Decision Helper”. In this part, we try to model the relationship between the SEIR model parameters and a variety of social factors, including demographic, medical and policy factors. We aim at obtaining models that are interpretive as well as predictive; in order words, we are hoping to find models that are simple, accessible and easy to be interpreted and understood, so that people can gain some insights of what is significant to the way a pandemic develops, but at the same time, we are also striving to find models that are most helpful making predictions among those explainable models.
With such goals in mind, we engaged a relatively small number of variables in our study–variables that seem significant to us intuitively, from the most accessible open data source.
The modeling methods we applied include the following:
- Data cleaning as necessary to address observations with missing or extreme values.
- Multiple linear regression
- ANOVA (codes omitted)
- Interaction (codes omitted)
- Residual diagnostics
- Transformations
- Polynomial regression
- Stepwise model selection (AIC & BIC) (codes omitted)
- Variable selection (codes omitted)
- Test/train splitting
Data source:
- Demographic, medical and policy data: COVID-19 Prediction Models Counties & Hospitals | Yu Group (UC Berkeley)
- Infection data and SEIR model estimated parameters: project
part 1andpart 2.
Data Preprocessing
StateOfInterest = c("Arizona", "California", "Minnesota", "New Mexico", "New York",
"Oklahoma", "South Carolina", "Tennessee", "Utah", "Virginia",
"West Virginia", "Wisconsin")Demographic & Policy Data (County-Level)
library(readr)
county_data_abridged = read_csv("county_data_abridged.csv")dim(county_data_abridged)## [1] 3244 87names(county_data_abridged)## [1] "countyFIPS" "STATEFP"
## [3] "COUNTYFP" "CountyName"
## [5] "StateName" "State"
## [7] "lat" "lon"
## [9] "POP_LATITUDE" "POP_LONGITUDE"
## [11] "CensusRegionName" "CensusDivisionName"
## [13] "Rural-UrbanContinuumCode2013" "PopulationEstimate2018"
## [15] "PopTotalMale2017" "PopTotalFemale2017"
## [17] "FracMale2017" "PopulationEstimate65+2017"
## [19] "PopulationDensityperSqMile2010" "CensusPopulation2010"
## [21] "MedianAge2010" "#EligibleforMedicare2018"
## [23] "MedicareEnrollment,AgedTot2017" "3-YrDiabetes2015-17"
## [25] "DiabetesPercentage" "HeartDiseaseMortality"
## [27] "StrokeMortality" "Smokers_Percentage"
## [29] "RespMortalityRate2014" "#FTEHospitalTotal2017"
## [31] "TotalM.D.'s,TotNon-FedandFed2017" "#HospParticipatinginNetwork2017"
## [33] "#Hospitals" "#ICU_beds"
## [35] "dem_to_rep_ratio" "PopMale<52010"
## [37] "PopFmle<52010" "PopMale5-92010"
## [39] "PopFmle5-92010" "PopMale10-142010"
## [41] "PopFmle10-142010" "PopMale15-192010"
## [43] "PopFmle15-192010" "PopMale20-242010"
## [45] "PopFmle20-242010" "PopMale25-292010"
## [47] "PopFmle25-292010" "PopMale30-342010"
## [49] "PopFmle30-342010" "PopMale35-442010"
## [51] "PopFmle35-442010" "PopMale45-542010"
## [53] "PopFmle45-542010" "PopMale55-592010"
## [55] "PopFmle55-592010" "PopMale60-642010"
## [57] "PopFmle60-642010" "PopMale65-742010"
## [59] "PopFmle65-742010" "PopMale75-842010"
## [61] "PopFmle75-842010" "PopMale>842010"
## [63] "PopFmle>842010" "3-YrMortalityAge<1Year2015-17"
## [65] "3-YrMortalityAge1-4Years2015-17" "3-YrMortalityAge5-14Years2015-17"
## [67] "3-YrMortalityAge15-24Years2015-17" "3-YrMortalityAge25-34Years2015-17"
## [69] "3-YrMortalityAge35-44Years2015-17" "3-YrMortalityAge45-54Years2015-17"
## [71] "3-YrMortalityAge55-64Years2015-17" "3-YrMortalityAge65-74Years2015-17"
## [73] "3-YrMortalityAge75-84Years2015-17" "3-YrMortalityAge85+Years2015-17"
## [75] "mortality2015-17Estimated" "stay at home"
## [77] ">50 gatherings" ">500 gatherings"
## [79] "public schools" "restaurant dine-in"
## [81] "entertainment/gym" "federal guidelines"
## [83] "foreign travel ban" "SVIPercentile"
## [85] "HPSAShortage" "HPSAServedPop"
## [87] "HPSAUnderservedPop"colnames(county_data_abridged)[colnames(county_data_abridged)
== "#EligibleforMedicare2018"] = "EligibleforMedicare2018"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "#FTEHospitalTotal2017"] = "FTEHospitalTotal2017"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "#HospParticipatinginNetwork2017"] = "HospParticipatinginNetwork2017"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "#Hospitals"] = "Hospitals"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "#ICU_beds"] = "ICU_beds"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "PopulationEstimate65+2017"] = "PopulationEstimate_above65_2017"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "stay at home"] = "stay_at_home"
colnames(county_data_abridged)[colnames(county_data_abridged)
== ">50 gatherings"] = "above_50_gatherings"
colnames(county_data_abridged)[colnames(county_data_abridged)
== ">500 gatherings"] = "above_500_gatherings"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "public schools"] = "public_schools"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "restaurant dine-in"] = "restaurant_dine_in"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "entertainment/gym"] = "entertainment_gym"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "federal guidelines"] = "federal_guidelines"
colnames(county_data_abridged)[colnames(county_data_abridged)
== "foreign travel ban"] = "foreign_travel_ban"data = subset(county_data_abridged,
select = c(State, CountyName, POP_LATITUDE, POP_LONGITUDE,
PopulationEstimate2018, PopTotalMale2017,
PopulationEstimate_above65_2017, PopulationDensityperSqMile2010,
DiabetesPercentage, Smokers_Percentage,
HeartDiseaseMortality, StrokeMortality,
Hospitals, ICU_beds, HospParticipatinginNetwork2017,
stay_at_home, above_50_gatherings, above_500_gatherings,
restaurant_dine_in, entertainment_gym))
data = na.omit(data)
data = droplevels(data)
data$stay_at_home = data$stay_at_home - range(data$stay_at_home)[1]
data$above_50_gatherings = data$above_50_gatherings - range(data$above_50_gatherings)[1]
data$above_500_gatherings = data$above_500_gatherings - range(data$above_500_gatherings)[1]
data$restaurant_dine_in = data$restaurant_dine_in - range(data$restaurant_dine_in)[1]
data$entertainment_gym = data$entertainment_gym - range(data$entertainment_gym)[1]
str(data)## tibble [2,585 × 20] (S3: tbl_df/tbl/data.frame)
## $ State : chr [1:2585] "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ CountyName : chr [1:2585] "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ POP_LATITUDE : num [1:2585] 32.5 30.5 31.8 33 34 ...
## $ POP_LONGITUDE : num [1:2585] -86.5 -87.8 -85.3 -87.1 -86.6 ...
## $ PopulationEstimate2018 : num [1:2585] 55601 218022 24881 22400 57840 ...
## $ PopTotalMale2017 : num [1:2585] 27007 103225 13335 12138 28607 ...
## $ PopulationEstimate_above65_2017: num [1:2585] 8392 42413 4757 3632 10351 ...
## $ PopulationDensityperSqMile2010 : num [1:2585] 91.8 114.7 31 36.8 88.9 ...
## $ DiabetesPercentage : num [1:2585] 9.9 8.5 15.7 13.3 14.9 22.4 16.9 15.6 17.5 12.2 ...
## $ Smokers_Percentage : num [1:2585] 18.1 17.5 22 19.1 19.2 ...
## $ HeartDiseaseMortality : num [1:2585] 204 183 220 226 225 ...
## $ StrokeMortality : num [1:2585] 56.1 41.9 49 57.2 52.8 54.1 59 44 45.2 47.3 ...
## $ Hospitals : num [1:2585] 1 3 1 1 1 1 1 2 0 1 ...
## $ ICU_beds : num [1:2585] 6 51 5 0 6 0 7 24 0 0 ...
## $ HospParticipatinginNetwork2017 : num [1:2585] 0 0 0 0 1 0 0 0 0 0 ...
## $ stay_at_home : num [1:2585] 16 16 16 16 16 16 16 16 16 16 ...
## $ above_50_gatherings : num [1:2585] 5 5 5 5 5 5 5 5 5 5 ...
## $ above_500_gatherings : num [1:2585] 2 2 2 2 2 2 2 2 2 2 ...
## $ restaurant_dine_in : num [1:2585] 7 7 7 7 7 7 7 7 7 7 ...
## $ entertainment_gym : num [1:2585] 16 16 16 16 16 16 16 16 16 16 ...
## - attr(*, "na.action")= 'omit' Named int [1:659] 68 69 70 71 72 73 74 75 76 77 ...
## ..- attr(*, "names")= chr [1:659] "68" "69" "70" "71" ...data_demographic_county = dataInfection Data (State-Level)
Covid-19 Cases
timeseries = read_csv("../datasets/timeseries.csv")data = timeseries
data = subset(data, country == "United States" & level == "state")
data = subset(data, !(name %in% c("Unassigned cases, Arkansas, US",
"Unassigned cases, Georgia, US", "Unassigned cases, Illinois, US",
"Unassigned cases, Iowa, US", "Unassigned cases, Maine, US",
"Unassigned cases, Massachusetts, US", "Unassigned cases, North Dakota, US",
"Washington, D.C., US")))
data$state = matrix(unlist(strsplit(as.character(data$name), ", ")), ncol = 2, byrow = TRUE)[, 1]
data = subset(data,
select = c(state, date, cases, deaths, recovered))
data = subset(data, state %in% StateOfInterest)
data$state = as.factor(data$state)
data$cases = as.numeric(data$cases)
data$deaths = as.numeric(data$deaths)
data$recovered = as.numeric(data$recovered)
data = na.omit(data)
data = droplevels(data)
str(data)## tibble [1,578 × 5] (S3: tbl_df/tbl/data.frame)
## $ state : Factor w/ 12 levels "Arizona","California",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : Date[1:1578], format: "2020-04-14" "2020-04-15" ...
## $ cases : num [1:1578] 3806 3962 4234 4507 4719 ...
## $ deaths : num [1:1578] 131 142 150 169 177 184 187 208 229 249 ...
## $ recovered: num [1:1578] 249 385 460 539 539 ...
## - attr(*, "problems")= tibble [1,666,558 × 5] (S3: tbl_df/tbl/data.frame)
## ..$ row : int [1:1666558] 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 ...
## ..$ col : chr [1:1666558] "state" "state" "state" "state" ...
## ..$ expected: chr [1:1666558] "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" ...
## ..$ actual : chr [1:1666558] "Burgenland" "Burgenland" "Burgenland" "Burgenland" ...
## ..$ file : chr [1:1666558] "'../datasets/timeseries.csv'" "'../datasets/timeseries.csv'" "'../datasets/timeseries.csv'" "'../datasets/timeseries.csv'" ...
## - attr(*, "na.action")= 'omit' Named int [1:894] 1 2 3 4 5 6 7 8 9 10 ...
## ..- attr(*, "names")= chr [1:894] "1" "2" "3" "4" ...range(data$cases)## [1] 237 613076range(data$deaths)## [1] 1 25250range(data$recovered)## [1] 2 266287data_cases_state = dataSEIR Model Parameters
model_out = read_csv("../datasets/model_out.csv")str(model_out)## tibble [73 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ state : chr [1:73] "Arizona" "Arizona" "Arizona" "Arizona" ...
## $ startdate: Date[1:73], format: "2020-04-16" "2020-05-01" ...
## $ enddate : Date[1:73], format: "2020-04-30" "2020-05-15" ...
## $ k : num [1:73] 0.783 0.863 0.865 0.949 0.986 ...
## $ sigma : num [1:73] 0.0714 0.0714 0.0714 0.0714 0.0714 ...
## $ lamda : num [1:73] 0.0563 0.07 0.1266 0.0784 0.0925 ...
## $ c : num [1:73] 1 0 0.127 1 0 ...
## $ alpha : num [1:73] 0.06825 0.00721 0.06765 0.10224 0.05641 ...
## $ omega : num [1:73] 0.00275 0.003091 0.001429 0.001371 0.000698 ...
## $ miu : num [1:73] 0.0384 0.0175 0.0338 0.036 0.014 ...
## - attr(*, "spec")=
## .. cols(
## .. state = col_character(),
## .. startdate = col_date(format = ""),
## .. enddate = col_date(format = ""),
## .. k = col_double(),
## .. sigma = col_double(),
## .. lamda = col_double(),
## .. c = col_double(),
## .. alpha = col_double(),
## .. omega = col_double(),
## .. miu = col_double()
## .. )data_parm = model_outCombined Data of Intersts (State-Level)
data = data_parm
n = length(data$state)
data$cases = rep(0, n)
data$deaths = rep(0, n)
data$recovered = rep(0, n)
for (i in 1:n){
j = data_cases_state$state == data$state[i] & data_cases_state$date == data$startdate[i]
if (sum(j) == 0){
data[i, ] = NA
}else{
data[i, c("cases", "deaths", "recovered")] = data_cases_state[j, c("cases", "deaths", "recovered")]
}
}
data = na.omit(data)
data_demographic_state = as.data.frame(matrix(nrow = length(data$state), ncol = length(colnames(data_demographic_county)) - 1))
colnames(data_demographic_state) = colnames(data_demographic_county)[colnames(data_demographic_county) != "CountyName"]
data_demographic_state$State = data$state
for (s in data_demographic_state$State){
for (k in 2:ncol(data_demographic_state)){
data_demographic_state[data_demographic_state$State == s, k] =
mean(unlist(data_demographic_county[data_demographic_county$State == s, k+1]))
}
}
data = cbind(data,
subset(data_demographic_state[, colnames(data_demographic_state)[colnames(data_demographic_state) != "State"]]))
data = na.omit(data)data$days = rep(0, nrow(data))
data$temp = rep(0, nrow(data))
for (i in 1:nrow(data)){
data$temp = diff.Date(c(data[i, ]$startdate, data[i, ]$enddate))
}
for (s in data$state){
for (d in 1:nrow(subset(data, state == s))){
data[data$state == s, "days"][d] = sum(subset(data, state == s)[1:d, "temp"])
}
}
data = data[, colnames(data)[colnames(data) != "temp"]]
data_unamed = data[, colnames(data)[!(colnames(data) %in% c("state", "startdate", "enddate"))]]# head(data_demographic_county, 10)
# head(data_demographic_state, 10)
# head(data[, c("state", "startdate", "enddate", "days")], 10)
head(data, 10)## state startdate enddate k sigma lamda c alpha
## 1 Arizona 2020-04-16 2020-04-30 0.7834 0.07143 0.05633 1.00000 0.06825
## 2 Arizona 2020-05-01 2020-05-15 0.8631 0.07143 0.07001 0.00000 0.00721
## 3 Arizona 2020-05-16 2020-05-30 0.8653 0.07143 0.12657 0.12709 0.06765
## 4 Arizona 2020-06-01 2020-06-15 0.9492 0.07143 0.07839 1.00000 0.10224
## 5 Arizona 2020-06-16 2020-06-30 0.9864 0.07143 0.09253 0.00000 0.05641
## 6 Arizona 2020-07-01 2020-07-15 0.9732 0.07143 0.06093 0.00000 0.00000
## 7 California 2020-04-16 2020-04-30 0.8574 0.07143 0.07211 0.07752 0.02728
## 8 California 2020-05-01 2020-05-15 0.9481 0.07143 0.02832 1.00000 0.01925
## 9 California 2020-05-16 2020-05-30 0.9598 0.07143 0.04972 0.40540 0.03497
## 10 California 2020-06-01 2020-06-15 1.1270 0.07143 0.10206 0.01176 0.03073
## omega miu cases deaths recovered POP_LATITUDE POP_LONGITUDE
## 1 0.0027505 0.038429 4234 150 460 33.58 -111.5
## 2 0.0030913 0.017520 7962 330 1528 33.58 -111.5
## 3 0.0014293 0.033751 13631 679 3357 33.58 -111.5
## 4 0.0013712 0.036006 20123 917 4869 33.58 -111.5
## 5 0.0006979 0.013973 39097 1219 6598 33.58 -111.5
## 6 0.0000000 0.003805 84092 1720 9715 33.58 -111.5
## 7 0.0023628 0.007923 28035 970 1753 37.82 -120.9
## 8 0.0013106 0.006909 52152 2131 5130 37.82 -120.9
## 9 0.0009157 0.012128 78704 3207 9098 37.82 -120.9
## 10 0.0006310 0.012709 115032 4219 17585 37.82 -120.9
## PopulationEstimate2018 PopTotalMale2017 PopulationEstimate_above65_2017
## 1 478110 232553 80116
## 2 478110 232553 80116
## 3 478110 232553 80116
## 4 478110 232553 80116
## 5 478110 232553 80116
## 6 478110 232553 80116
## 7 682018 338751 94920
## 8 682018 338751 94920
## 9 682018 338751 94920
## 10 682018 338751 94920
## PopulationDensityperSqMile2010 DiabetesPercentage Smokers_Percentage
## 1 52.05 10.060 16.48
## 2 52.05 10.060 16.48
## 3 52.05 10.060 16.48
## 4 52.05 10.060 16.48
## 5 52.05 10.060 16.48
## 6 52.05 10.060 16.48
## 7 663.26 8.505 12.09
## 8 663.26 8.505 12.09
## 9 663.26 8.505 12.09
## 10 663.26 8.505 12.09
## HeartDiseaseMortality StrokeMortality Hospitals ICU_beds
## 1 148.8 30.90 5.067 103.9
## 2 148.8 30.90 5.067 103.9
## 3 148.8 30.90 5.067 103.9
## 4 148.8 30.90 5.067 103.9
## 5 148.8 30.90 5.067 103.9
## 6 148.8 30.90 5.067 103.9
## 7 153.9 37.89 5.672 126.5
## 8 153.9 37.89 5.672 126.5
## 9 153.9 37.89 5.672 126.5
## 10 153.9 37.89 5.672 126.5
## HospParticipatinginNetwork2017 stay_at_home above_50_gatherings
## 1 1.800 12 2
## 2 1.800 12 2
## 3 1.800 12 2
## 4 1.800 12 2
## 5 1.800 12 2
## 6 1.800 12 2
## 7 1.845 0 4
## 8 1.845 0 4
## 9 1.845 0 4
## 10 1.845 0 4
## above_500_gatherings restaurant_dine_in entertainment_gym days
## 1 6 7 7 14
## 2 6 7 7 28
## 3 6 7 7 42
## 4 6 7 7 56
## 5 6 7 7 70
## 6 6 7 7 84
## 7 8 3 3 14
## 8 8 3 3 28
## 9 8 3 3 42
## 10 8 3 3 56Methods: Modeling & Testing Procedures (abridged)
Data Points pairs() Plot
set.seed(42)
num_obs = nrow(data_unamed) # total number of observations
num_trn = round(num_obs * 0.90) # number of observations for the training data
trn_idx = sample(num_obs, num_trn) # randomly generate the index for the training data
data_trn = data_unamed[trn_idx, ] # training data
data_tst = data_unamed[-trn_idx, ] # testing dataModeling k
# full additive model
mod_k_full = lm(k ~ ., data = data_trn)
test_mod(mod_k_full, k = 1)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.221852 0.475015 0.016221 0.000191 29.000000 0.149099 14.731106summary(mod_k_full)##
## Call:
## lm(formula = k ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4647 -0.0452 0.0073 0.0503 0.2077
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.62e+01 3.93e+00 4.13 0.00019 ***
## sigma NA NA NA NA
## lamda -6.46e+00 1.20e+00 -5.41 3.5e-06 ***
## c -3.05e-01 7.17e-02 -4.26 0.00012 ***
## alpha 3.08e+00 1.02e+00 3.00 0.00465 **
## omega 2.32e+01 2.59e+01 0.89 0.37630
## miu 4.41e+00 1.99e+00 2.21 0.03272 *
## cases -4.57e-07 1.64e-06 -0.28 0.78232
## deaths 1.32e-05 1.91e-05 0.69 0.49356
## recovered -1.54e-06 6.60e-06 -0.23 0.81645
## POP_LATITUDE -2.01e-01 5.03e-02 -3.99 0.00028 ***
## POP_LONGITUDE 6.94e-02 1.69e-02 4.11 0.00019 ***
## PopulationEstimate2018 -1.13e-04 2.90e-05 -3.91 0.00036 ***
## PopTotalMale2017 2.15e-04 5.58e-05 3.85 0.00043 ***
## PopulationEstimate_above65_2017 7.20e-05 1.76e-05 4.10 0.00020 ***
## PopulationDensityperSqMile2010 2.45e-05 1.41e-04 0.17 0.86330
## DiabetesPercentage -8.36e-01 2.00e-01 -4.19 0.00015 ***
## Smokers_Percentage 3.32e-01 8.49e-02 3.91 0.00036 ***
## HeartDiseaseMortality -2.90e-02 9.27e-03 -3.13 0.00331 **
## StrokeMortality 1.61e-01 4.45e-02 3.62 0.00083 ***
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days 3.16e-03 1.46e-03 2.17 0.03613 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.136 on 39 degrees of freedom
## Multiple R-squared: 0.647, Adjusted R-squared: 0.475
## F-statistic: 3.76 on 19 and 39 DF, p-value: 0.000227# small additive model
mod_k_1 = lm(k ~ days, data = data_trn)
# large additive model
mod_k_2 = lm(k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_k_1, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 1.867e-01 2.328e-02 9.083e-01 4.534e-13 2.000e+00 5.830e-02 5.580e+00test_mod(mod_k_2, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.221852 0.475015 0.016221 0.000191 20.000000 0.149099 14.731106summary(mod_k_1)##
## Call:
## lm(formula = k ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2560 -0.0326 0.0395 0.0661 0.2381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.805555 0.054607 14.75 <2e-16 ***
## days 0.001488 0.000964 1.54 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.186 on 57 degrees of freedom
## Multiple R-squared: 0.0401, Adjusted R-squared: 0.0233
## F-statistic: 2.38 on 1 and 57 DF, p-value: 0.128summary(mod_k_2)##
## Call:
## lm(formula = k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4647 -0.0452 0.0073 0.0503 0.2077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.62e+01 3.93e+00 4.13 0.00019 ***
## lamda -6.46e+00 1.20e+00 -5.41 3.5e-06 ***
## c -3.05e-01 7.17e-02 -4.26 0.00012 ***
## alpha 3.08e+00 1.02e+00 3.00 0.00465 **
## omega 2.32e+01 2.59e+01 0.89 0.37630
## miu 4.41e+00 1.99e+00 2.21 0.03272 *
## cases -4.57e-07 1.64e-06 -0.28 0.78232
## deaths 1.32e-05 1.91e-05 0.69 0.49356
## recovered -1.54e-06 6.60e-06 -0.23 0.81645
## POP_LATITUDE -2.01e-01 5.03e-02 -3.99 0.00028 ***
## POP_LONGITUDE 6.94e-02 1.69e-02 4.11 0.00019 ***
## PopulationEstimate2018 -1.13e-04 2.90e-05 -3.91 0.00036 ***
## PopTotalMale2017 2.15e-04 5.58e-05 3.85 0.00043 ***
## PopulationEstimate_above65_2017 7.20e-05 1.76e-05 4.10 0.00020 ***
## PopulationDensityperSqMile2010 2.45e-05 1.41e-04 0.17 0.86330
## DiabetesPercentage -8.36e-01 2.00e-01 -4.19 0.00015 ***
## Smokers_Percentage 3.32e-01 8.49e-02 3.91 0.00036 ***
## HeartDiseaseMortality -2.90e-02 9.27e-03 -3.13 0.00331 **
## StrokeMortality 1.61e-01 4.45e-02 3.62 0.00083 ***
## days 3.16e-03 1.46e-03 2.17 0.03613 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.136 on 39 degrees of freedom
## Multiple R-squared: 0.647, Adjusted R-squared: 0.475
## F-statistic: 3.76 on 19 and 39 DF, p-value: 0.000227# intermediate model
mod_k_3 = lm(k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_k_4 = lm(k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_k_1, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 1.867e-01 2.328e-02 9.083e-01 4.534e-13 2.000e+00 5.830e-02 5.580e+00test_mod(mod_k_2, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.221852 0.475015 0.016221 0.000191 20.000000 0.149099 14.731106test_mod(mod_k_3, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.2054978 0.5247754 0.0017914 0.0001737 16.0000000 0.1552475 14.6026990test_mod(mod_k_4, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.2042750 0.5311550 0.0009837 0.0004907 15.0000000 0.1546657 14.4151641summary(mod_k_3)##
## Call:
## lm(formula = k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4742 -0.0548 0.0072 0.0526 0.2431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.13e+03 4.10e+02 2.75 0.0087 **
## lamda -6.79e+00 1.13e+00 -6.02 3.4e-07 ***
## c -3.12e-01 6.69e-02 -4.67 2.9e-05 ***
## alpha 3.83e+00 8.45e-01 4.54 4.5e-05 ***
## miu 4.58e+00 1.71e+00 2.69 0.0102 *
## POP_LATITUDE -2.11e+01 7.52e+00 -2.80 0.0076 **
## POP_LONGITUDE 1.51e+01 5.58e+00 2.70 0.0098 **
## PopulationEstimate_above65_2017 -1.64e-04 6.58e-05 -2.50 0.0163 *
## DiabetesPercentage -8.55e+00 3.03e+00 -2.82 0.0072 **
## Smokers_Percentage 4.53e+00 1.62e+00 2.80 0.0077 **
## HeartDiseaseMortality -8.73e-02 2.73e-02 -3.20 0.0026 **
## StrokeMortality 4.52e-01 1.34e-01 3.38 0.0016 **
## log(days) 1.02e-01 2.99e-02 3.41 0.0014 **
## stay_at_home 1.14e+00 4.42e-01 2.59 0.0129 *
## I(POP_LATITUDE^2) 2.65e-01 9.49e-02 2.79 0.0078 **
## I(POP_LONGITUDE^2) 7.85e-02 2.91e-02 2.70 0.0100 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.13 on 43 degrees of freedom
## Multiple R-squared: 0.648, Adjusted R-squared: 0.525
## F-statistic: 5.27 on 15 and 43 DF, p-value: 8.7e-06summary(mod_k_4)##
## Call:
## lm(formula = k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4732 -0.0497 -0.0017 0.0607 0.2366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.3989580 3.5627641 4.32 8.7e-05 ***
## lamda -6.8317626 1.1179898 -6.11 2.3e-07 ***
## c -0.3221741 0.0646726 -4.98 1.0e-05 ***
## alpha 3.6975440 0.8116795 4.56 4.1e-05 ***
## miu 4.9379779 1.6006925 3.08 0.00351 **
## POP_LATITUDE -0.1920080 0.0440854 -4.36 7.8e-05 ***
## POP_LONGITUDE 0.0662907 0.0153167 4.33 8.5e-05 ***
## PopulationEstimate2018 -0.0001105 0.0000270 -4.09 0.00018 ***
## PopTotalMale2017 0.0002107 0.0000518 4.06 0.00020 ***
## PopulationEstimate_above65_2017 0.0000669 0.0000154 4.35 8.1e-05 ***
## DiabetesPercentage -0.7787347 0.1646073 -4.73 2.3e-05 ***
## Smokers_Percentage 0.3062246 0.0605033 5.06 7.9e-06 ***
## HeartDiseaseMortality -0.0257963 0.0058214 -4.43 6.1e-05 ***
## StrokeMortality 0.1439159 0.0288281 4.99 9.9e-06 ***
## log(days) 0.0993978 0.0294363 3.38 0.00154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.129 on 44 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.531
## F-statistic: 5.69 on 14 and 44 DF, p-value: 4.16e-06# intermediate model
# relatively bad models
mod_k_5 = lm(k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (lamda + c + alpha + omega + miu),
data = data_trn)
mod_k_6 = lm(k ~ lamda + c + alpha + omega + miu,
data = data_trn)
test_mod(mod_k_1, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 1.867e-01 2.328e-02 9.083e-01 4.534e-13 2.000e+00 5.830e-02 5.580e+00test_mod(mod_k_2, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.221852 0.475015 0.016221 0.000191 20.000000 0.149099 14.731106test_mod(mod_k_3, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.2054978 0.5247754 0.0017914 0.0001737 16.0000000 0.1552475 14.6026990test_mod(mod_k_4, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.2042750 0.5311550 0.0009837 0.0004907 15.0000000 0.1546657 14.4151641test_mod(mod_k_5, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.004e-01 1.101e-01 2.567e-01 2.605e-12 1.500e+01 9.543e-02 7.160e+00test_mod(mod_k_6, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.135e-01 8.328e-02 1.932e-02 7.076e-10 6.000e+00 5.237e-02 5.250e+00summary(mod_k_5)##
## Call:
## lm(formula = k ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (lamda +
## c + alpha + omega + miu), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9987 -0.0268 0.0054 0.0370 0.2963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.44e+00 4.21e+00 2.01 0.051 .
## cases 5.03e-07 1.92e-06 0.26 0.795
## deaths 1.45e-05 2.31e-05 0.63 0.533
## recovered -5.29e-06 7.18e-06 -0.74 0.465
## POP_LATITUDE -9.83e-02 5.46e-02 -1.80 0.079 .
## POP_LONGITUDE 3.81e-02 1.82e-02 2.10 0.042 *
## PopulationEstimate2018 -5.57e-05 2.94e-05 -1.90 0.064 .
## PopTotalMale2017 1.06e-04 5.64e-05 1.88 0.067 .
## PopulationEstimate_above65_2017 3.67e-05 2.00e-05 1.83 0.073 .
## PopulationDensityperSqMile2010 -2.84e-05 1.52e-04 -0.19 0.852
## DiabetesPercentage -4.58e-01 2.28e-01 -2.01 0.051 .
## Smokers_Percentage 1.93e-01 9.88e-02 1.96 0.057 .
## HeartDiseaseMortality -1.62e-02 1.09e-02 -1.48 0.147
## StrokeMortality 9.04e-02 5.25e-02 1.72 0.092 .
## days 2.01e-03 1.60e-03 1.25 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.177 on 44 degrees of freedom
## Multiple R-squared: 0.325, Adjusted R-squared: 0.11
## F-statistic: 1.51 on 14 and 44 DF, p-value: 0.147summary(mod_k_6)##
## Call:
## lm(formula = k ~ lamda + c + alpha + omega + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0289 -0.0445 0.0356 0.0886 0.2567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1002 0.0791 13.90 <2e-16 ***
## lamda -2.7175 1.2218 -2.22 0.030 *
## c -0.1608 0.0772 -2.08 0.042 *
## alpha 2.0142 1.1260 1.79 0.079 .
## omega -36.1277 24.4670 -1.48 0.146
## miu 0.8043 1.0704 0.75 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.18 on 53 degrees of freedom
## Multiple R-squared: 0.162, Adjusted R-squared: 0.0833
## F-statistic: 2.05 on 5 and 53 DF, p-value: 0.0859mod_k = mod_k_6
# diagnostics(mod_k, testit = FALSE)Modeling sigma
In this case, sigma is set to an empirical value. Modeling excluded.
Modeling lamda
# full additive model
mod_lamda_full = lm(lamda ~ ., data = data_trn)
test_mod(mod_lamda_full, k = 3)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02250 0.79955 0.17494 0.03059 29.00000 0.01670 26.37237summary(mod_lamda_full)##
## Call:
## lm(formula = lamda ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03239 -0.00625 -0.00057 0.00673 0.02290
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.64e+00 3.98e-01 4.13 0.00018 ***
## k -6.63e-02 1.23e-02 -5.41 3.5e-06 ***
## sigma NA NA NA NA
## c -4.39e-02 5.28e-03 -8.31 3.7e-10 ***
## alpha 3.96e-01 9.62e-02 4.11 0.00020 ***
## omega 4.98e-01 2.65e+00 0.19 0.85212
## miu 7.71e-01 1.75e-01 4.41 7.8e-05 ***
## cases -7.34e-08 1.66e-07 -0.44 0.66132
## deaths 4.80e-07 1.94e-06 0.25 0.80609
## recovered 1.10e-07 6.69e-07 0.16 0.86987
## POP_LATITUDE -2.08e-02 5.06e-03 -4.11 0.00020 ***
## POP_LONGITUDE 6.72e-03 1.74e-03 3.86 0.00042 ***
## PopulationEstimate2018 -1.17e-05 2.91e-06 -4.04 0.00025 ***
## PopTotalMale2017 2.23e-05 5.60e-06 3.98 0.00029 ***
## PopulationEstimate_above65_2017 7.26e-06 1.78e-06 4.08 0.00022 ***
## PopulationDensityperSqMile2010 2.75e-06 1.43e-05 0.19 0.84842
## DiabetesPercentage -7.94e-02 2.08e-02 -3.83 0.00046 ***
## Smokers_Percentage 3.01e-02 8.93e-03 3.37 0.00170 **
## HeartDiseaseMortality -2.66e-03 9.61e-04 -2.77 0.00854 **
## StrokeMortality 1.48e-02 4.65e-03 3.17 0.00292 **
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days 1.95e-04 1.53e-04 1.28 0.20967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0138 on 39 degrees of freedom
## Multiple R-squared: 0.865, Adjusted R-squared: 0.8
## F-statistic: 13.2 on 19 and 39 DF, p-value: 1.57e-11# small additive model
mod_lamda_1 = lm(lamda ~ days, data = data_trn)
# large additive model
mod_lamda_2 = lm(lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_lamda_1, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.031384 -0.003521 0.231445 0.727519 2.000000 0.024319 39.801177test_mod(mod_lamda_2, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02250 0.79955 0.17494 0.03059 20.00000 0.01670 26.37237summary(mod_lamda_1)##
## Call:
## lm(formula = lamda ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0627 -0.0235 0.0031 0.0221 0.0679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075259 0.009075 8.29 2.2e-11 ***
## days -0.000143 0.000160 -0.89 0.38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0309 on 57 degrees of freedom
## Multiple R-squared: 0.0138, Adjusted R-squared: -0.00352
## F-statistic: 0.797 on 1 and 57 DF, p-value: 0.376summary(mod_lamda_2)##
## Call:
## lm(formula = lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03239 -0.00625 -0.00057 0.00673 0.02290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.64e+00 3.98e-01 4.13 0.00018 ***
## k -6.63e-02 1.23e-02 -5.41 3.5e-06 ***
## c -4.39e-02 5.28e-03 -8.31 3.7e-10 ***
## alpha 3.96e-01 9.62e-02 4.11 0.00020 ***
## omega 4.98e-01 2.65e+00 0.19 0.85212
## miu 7.71e-01 1.75e-01 4.41 7.8e-05 ***
## cases -7.34e-08 1.66e-07 -0.44 0.66132
## deaths 4.80e-07 1.94e-06 0.25 0.80609
## recovered 1.10e-07 6.69e-07 0.16 0.86987
## POP_LATITUDE -2.08e-02 5.06e-03 -4.11 0.00020 ***
## POP_LONGITUDE 6.72e-03 1.74e-03 3.86 0.00042 ***
## PopulationEstimate2018 -1.17e-05 2.91e-06 -4.04 0.00025 ***
## PopTotalMale2017 2.23e-05 5.60e-06 3.98 0.00029 ***
## PopulationEstimate_above65_2017 7.26e-06 1.78e-06 4.08 0.00022 ***
## PopulationDensityperSqMile2010 2.75e-06 1.43e-05 0.19 0.84842
## DiabetesPercentage -7.94e-02 2.08e-02 -3.83 0.00046 ***
## Smokers_Percentage 3.01e-02 8.93e-03 3.37 0.00170 **
## HeartDiseaseMortality -2.66e-03 9.61e-04 -2.77 0.00854 **
## StrokeMortality 1.48e-02 4.65e-03 3.17 0.00292 **
## days 1.95e-04 1.53e-04 1.28 0.20967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0138 on 39 degrees of freedom
## Multiple R-squared: 0.865, Adjusted R-squared: 0.8
## F-statistic: 13.2 on 19 and 39 DF, p-value: 1.57e-11# intermediate model
mod_lamda_3 = lm(lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_lamda_4 = lm(lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_lamda_1, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.031384 -0.003521 0.231445 0.727519 2.000000 0.024319 39.801177test_mod(mod_lamda_2, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02250 0.79955 0.17494 0.03059 20.00000 0.01670 26.37237test_mod(mod_lamda_3, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01787 0.82455 0.20647 0.01453 16.00000 0.01624 24.42386test_mod(mod_lamda_4, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01745 0.82845 0.19372 0.01659 15.00000 0.01620 24.31619summary(mod_lamda_3)##
## Call:
## lm(formula = lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03394 -0.00630 -0.00201 0.00661 0.02268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.38e+02 3.90e+01 3.53 0.00099 ***
## k -6.74e-02 1.12e-02 -6.02 3.4e-07 ***
## c -4.40e-02 4.67e-03 -9.43 4.9e-12 ***
## alpha 4.21e-01 7.96e-02 5.29 3.9e-06 ***
## miu 7.92e-01 1.38e-01 5.73 9.2e-07 ***
## POP_LATITUDE -2.57e+00 7.15e-01 -3.59 0.00084 ***
## POP_LONGITUDE 1.85e+00 5.31e-01 3.49 0.00115 **
## PopulationEstimate_above65_2017 -2.07e-05 6.27e-06 -3.29 0.00199 **
## DiabetesPercentage -1.04e+00 2.88e-01 -3.59 0.00084 ***
## Smokers_Percentage 5.50e-01 1.54e-01 3.57 0.00090 ***
## HeartDiseaseMortality -1.02e-02 2.59e-03 -3.93 0.00031 ***
## StrokeMortality 5.21e-02 1.27e-02 4.09 0.00018 ***
## log(days) 7.81e-03 3.14e-03 2.49 0.01682 *
## stay_at_home 1.42e-01 4.21e-02 3.37 0.00158 **
## I(POP_LATITUDE^2) 3.22e-02 9.02e-03 3.58 0.00088 ***
## I(POP_LONGITUDE^2) 9.63e-03 2.77e-03 3.48 0.00117 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0129 on 43 degrees of freedom
## Multiple R-squared: 0.87, Adjusted R-squared: 0.825
## F-statistic: 19.2 on 15 and 43 DF, p-value: 2.6e-14summary(mod_lamda_4)##
## Call:
## lm(formula = lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03373 -0.00652 -0.00214 0.00673 0.02291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.63e+00 3.43e-01 4.74 2.3e-05 ***
## k -6.72e-02 1.10e-02 -6.11 2.3e-07 ***
## c -4.42e-02 4.46e-03 -9.92 8.6e-13 ***
## alpha 4.18e-01 7.46e-02 5.59 1.3e-06 ***
## miu 8.00e-01 1.27e-01 6.30 1.2e-07 ***
## POP_LATITUDE -2.07e-02 4.19e-03 -4.95 1.1e-05 ***
## POP_LONGITUDE 6.72e-03 1.50e-03 4.46 5.5e-05 ***
## PopulationEstimate2018 -1.16e-05 2.61e-06 -4.46 5.6e-05 ***
## PopTotalMale2017 2.21e-05 5.02e-06 4.41 6.6e-05 ***
## PopulationEstimate_above65_2017 7.13e-06 1.47e-06 4.84 1.6e-05 ***
## DiabetesPercentage -7.93e-02 1.61e-02 -4.93 1.2e-05 ***
## Smokers_Percentage 2.99e-02 6.06e-03 4.93 1.2e-05 ***
## HeartDiseaseMortality -2.60e-03 5.73e-04 -4.53 4.5e-05 ***
## StrokeMortality 1.45e-02 2.83e-03 5.13 6.3e-06 ***
## log(days) 7.73e-03 3.06e-03 2.53 0.015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0128 on 44 degrees of freedom
## Multiple R-squared: 0.87, Adjusted R-squared: 0.828
## F-statistic: 21 on 14 and 44 DF, p-value: 5.59e-15# intermediate model
# relatively bad models
mod_lamda_5 = lm(lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (k + c + alpha + omega + miu),
data = data_trn)
mod_lamda_6 = lm(lamda ~ k + c + alpha + omega + miu,
data = data_trn)
test_mod(mod_lamda_1, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.031384 -0.003521 0.231445 0.727519 2.000000 0.024319 39.801177test_mod(mod_lamda_2, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02250 0.79955 0.17494 0.03059 20.00000 0.01670 26.37237test_mod(mod_lamda_3, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01787 0.82455 0.20647 0.01453 16.00000 0.01624 24.42386test_mod(mod_lamda_4, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01745 0.82845 0.19372 0.01659 15.00000 0.01620 24.31619test_mod(mod_lamda_5, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.03121 0.22337 0.22277 0.23216 15.00000 0.02704 44.73045test_mod(mod_lamda_6, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02534 0.60579 0.10363 0.07028 6.00000 0.01508 18.16319summary(mod_lamda_5)##
## Call:
## lm(formula = lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (k + c +
## alpha + omega + miu), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03981 -0.01853 -0.00046 0.01614 0.05226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57e-01 6.44e-01 0.24 0.81
## cases -4.08e-08 2.94e-07 -0.14 0.89
## deaths -4.05e-06 3.53e-06 -1.15 0.26
## recovered 1.29e-06 1.10e-06 1.18 0.25
## POP_LATITUDE -3.72e-03 8.36e-03 -0.45 0.66
## POP_LONGITUDE 1.75e-04 2.78e-03 0.06 0.95
## PopulationEstimate2018 2.38e-06 4.50e-06 0.53 0.60
## PopTotalMale2017 -5.04e-06 8.63e-06 -0.58 0.56
## PopulationEstimate_above65_2017 1.05e-06 3.06e-06 0.34 0.73
## PopulationDensityperSqMile2010 3.05e-05 2.32e-05 1.31 0.20
## DiabetesPercentage -1.83e-02 3.49e-02 -0.52 0.60
## Smokers_Percentage 1.44e-02 1.51e-02 0.95 0.35
## HeartDiseaseMortality -1.85e-03 1.68e-03 -1.11 0.27
## StrokeMortality 8.20e-03 8.05e-03 1.02 0.31
## days -2.56e-04 2.45e-04 -1.05 0.30
##
## Residual standard error: 0.0272 on 44 degrees of freedom
## Multiple R-squared: 0.411, Adjusted R-squared: 0.223
## F-statistic: 2.19 on 14 and 44 DF, p-value: 0.0242summary(mod_lamda_6)##
## Call:
## lm(formula = lamda ~ k + c + alpha + omega + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05354 -0.01051 0.00503 0.01083 0.03496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08436 0.01422 5.93 2.3e-07 ***
## k -0.03141 0.01412 -2.22 0.03042 *
## c -0.04153 0.00648 -6.41 3.9e-08 ***
## alpha 0.42203 0.11036 3.82 0.00035 ***
## omega -2.19340 2.66718 -0.82 0.41455
## miu 0.55378 0.08718 6.35 5.0e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0194 on 53 degrees of freedom
## Multiple R-squared: 0.64, Adjusted R-squared: 0.606
## F-statistic: 18.8 on 5 and 53 DF, p-value: 1.03e-10mod_lamda = mod_lamda_4
# diagnostics(mod_lamda, testit = FALSE)Modeling c
# full additive model
mod_c_full = lm(c ~ ., data = data_trn)
test_mod(mod_c_full, k = 4)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3844 0.6300 0.1073 0.8302 29.0000 0.2652 Infsummary(mod_c_full)##
## Call:
## lm(formula = c ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4762 -0.1244 -0.0493 0.1559 0.4969
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48e+01 7.73e+00 3.22 0.00262 **
## k -1.04e+00 2.44e-01 -4.26 0.00012 ***
## sigma NA NA NA NA
## lamda -1.46e+01 1.75e+00 -8.31 3.7e-10 ***
## alpha 4.62e+00 1.96e+00 2.35 0.02379 *
## omega 1.76e+01 4.82e+01 0.37 0.71646
## miu 1.51e+01 3.05e+00 4.96 1.4e-05 ***
## cases -2.66e-06 3.00e-06 -0.89 0.38107
## deaths 5.05e-05 3.45e-05 1.46 0.15120
## recovered -4.43e-06 1.22e-05 -0.36 0.71804
## POP_LATITUDE -2.96e-01 9.95e-02 -2.97 0.00508 **
## POP_LONGITUDE 1.03e-01 3.35e-02 3.06 0.00395 **
## PopulationEstimate2018 -2.06e-04 5.37e-05 -3.83 0.00045 ***
## PopTotalMale2017 3.94e-04 1.03e-04 3.82 0.00047 ***
## PopulationEstimate_above65_2017 1.12e-04 3.43e-05 3.28 0.00220 **
## PopulationDensityperSqMile2010 -2.93e-04 2.56e-04 -1.14 0.25923
## DiabetesPercentage -1.07e+00 4.09e-01 -2.62 0.01258 *
## Smokers_Percentage 3.28e-01 1.77e-01 1.85 0.07137 .
## HeartDiseaseMortality -2.41e-02 1.87e-02 -1.29 0.20631
## StrokeMortality 1.59e-01 9.14e-02 1.74 0.09055 .
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days 4.81e-03 2.74e-03 1.76 0.08674 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.251 on 39 degrees of freedom
## Multiple R-squared: 0.751, Adjusted R-squared: 0.63
## F-statistic: 6.2 on 19 and 39 DF, p-value: 7.93e-07# small additive model
mod_c_1 = lm(c ~ days, data = data_trn)
# large additive model
mod_c_2 = lm(c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_c_1, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 4.204e-01 -6.359e-04 8.737e-01 1.839e-07 2.000e+00 4.396e-01 Inftest_mod(mod_c_2, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3844 0.6300 0.1073 0.8302 20.0000 0.2652 Infsummary(mod_c_1)##
## Call:
## lm(formula = c ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.434 -0.295 -0.198 0.450 0.734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23630 0.12145 1.95 0.057 .
## days 0.00210 0.00214 0.98 0.331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.414 on 57 degrees of freedom
## Multiple R-squared: 0.0166, Adjusted R-squared: -0.000636
## F-statistic: 0.963 on 1 and 57 DF, p-value: 0.331summary(mod_c_2)##
## Call:
## lm(formula = c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4762 -0.1244 -0.0493 0.1559 0.4969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48e+01 7.73e+00 3.22 0.00262 **
## k -1.04e+00 2.44e-01 -4.26 0.00012 ***
## lamda -1.46e+01 1.75e+00 -8.31 3.7e-10 ***
## alpha 4.62e+00 1.96e+00 2.35 0.02379 *
## omega 1.76e+01 4.82e+01 0.37 0.71646
## miu 1.51e+01 3.05e+00 4.96 1.4e-05 ***
## cases -2.66e-06 3.00e-06 -0.89 0.38107
## deaths 5.05e-05 3.45e-05 1.46 0.15120
## recovered -4.43e-06 1.22e-05 -0.36 0.71804
## POP_LATITUDE -2.96e-01 9.95e-02 -2.97 0.00508 **
## POP_LONGITUDE 1.03e-01 3.35e-02 3.06 0.00395 **
## PopulationEstimate2018 -2.06e-04 5.37e-05 -3.83 0.00045 ***
## PopTotalMale2017 3.94e-04 1.03e-04 3.82 0.00047 ***
## PopulationEstimate_above65_2017 1.12e-04 3.43e-05 3.28 0.00220 **
## PopulationDensityperSqMile2010 -2.93e-04 2.56e-04 -1.14 0.25923
## DiabetesPercentage -1.07e+00 4.09e-01 -2.62 0.01258 *
## Smokers_Percentage 3.28e-01 1.77e-01 1.85 0.07137 .
## HeartDiseaseMortality -2.41e-02 1.87e-02 -1.29 0.20631
## StrokeMortality 1.59e-01 9.14e-02 1.74 0.09055 .
## days 4.81e-03 2.74e-03 1.76 0.08674 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.251 on 39 degrees of freedom
## Multiple R-squared: 0.751, Adjusted R-squared: 0.63
## F-statistic: 6.2 on 19 and 39 DF, p-value: 7.93e-07# intermediate model
mod_c_3 = lm(c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_c_4 = lm(c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_c_1, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 4.204e-01 -6.359e-04 8.737e-01 1.839e-07 2.000e+00 4.396e-01 Inftest_mod(mod_c_2, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3844 0.6300 0.1073 0.8302 20.0000 0.2652 Inftest_mod(mod_c_3, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3285 0.6605 0.2078 0.1439 16.0000 0.2592 Inftest_mod(mod_c_4, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3132 0.6627 0.2233 0.1210 15.0000 0.2645 Infsummary(mod_c_3)##
## Call:
## lm(formula = c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5448 -0.1371 -0.0494 0.1446 0.4962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.85e+03 7.01e+02 4.07 0.00020 ***
## k -1.08e+00 2.31e-01 -4.67 2.9e-05 ***
## lamda -1.53e+01 1.62e+00 -9.43 4.9e-12 ***
## alpha 5.92e+00 1.68e+00 3.52 0.00103 **
## miu 1.52e+01 2.52e+00 6.01 3.5e-07 ***
## POP_LATITUDE -5.29e+01 1.29e+01 -4.10 0.00018 ***
## POP_LONGITUDE 3.86e+01 9.54e+00 4.04 0.00021 ***
## PopulationEstimate_above65_2017 -4.41e-04 1.12e-04 -3.93 0.00030 ***
## DiabetesPercentage -2.13e+01 5.20e+00 -4.10 0.00018 ***
## Smokers_Percentage 1.14e+01 2.78e+00 4.09 0.00019 ***
## HeartDiseaseMortality -2.00e-01 4.74e-02 -4.22 0.00013 ***
## StrokeMortality 1.01e+00 2.33e-01 4.33 8.7e-05 ***
## log(days) 1.40e-01 5.88e-02 2.39 0.02155 *
## stay_at_home 3.00e+00 7.54e-01 3.97 0.00027 ***
## I(POP_LATITUDE^2) 6.65e-01 1.62e-01 4.09 0.00018 ***
## I(POP_LONGITUDE^2) 2.01e-01 4.97e-02 4.04 0.00022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.241 on 43 degrees of freedom
## Multiple R-squared: 0.748, Adjusted R-squared: 0.661
## F-statistic: 8.52 on 15 and 43 DF, p-value: 1.51e-08summary(mod_c_4)##
## Call:
## lm(formula = c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5776 -0.1298 -0.0518 0.1315 0.5565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.83e+01 6.68e+00 4.23 0.00012 ***
## k -1.12e+00 2.25e-01 -4.98 1.0e-05 ***
## lamda -1.56e+01 1.58e+00 -9.92 8.6e-13 ***
## alpha 6.45e+00 1.56e+00 4.14 0.00015 ***
## miu 1.46e+01 2.44e+00 6.00 3.3e-07 ***
## POP_LATITUDE -3.61e-01 8.18e-02 -4.42 6.4e-05 ***
## POP_LONGITUDE 1.15e-01 2.93e-02 3.93 0.00029 ***
## PopulationEstimate2018 -2.08e-04 5.01e-05 -4.15 0.00015 ***
## PopTotalMale2017 3.96e-04 9.64e-05 4.10 0.00017 ***
## PopulationEstimate_above65_2017 1.25e-04 2.86e-05 4.39 7.1e-05 ***
## DiabetesPercentage -1.36e+00 3.16e-01 -4.31 9.2e-05 ***
## Smokers_Percentage 4.92e-01 1.21e-01 4.07 0.00019 ***
## HeartDiseaseMortality -4.29e-02 1.13e-02 -3.78 0.00047 ***
## StrokeMortality 2.43e-01 5.64e-02 4.32 8.8e-05 ***
## log(days) 1.52e-01 5.72e-02 2.65 0.01113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.24 on 44 degrees of freedom
## Multiple R-squared: 0.744, Adjusted R-squared: 0.663
## F-statistic: 9.14 on 14 and 44 DF, p-value: 6.67e-09# intermediate model
# relatively bad models
mod_c_5 = lm(c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (k + lamda + alpha + omega + miu),
data = data_trn)
mod_c_6 = lm(c ~ k + lamda + alpha + omega + miu,
data = data_trn)
test_mod(mod_c_1, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 4.204e-01 -6.359e-04 8.737e-01 1.839e-07 2.000e+00 4.396e-01 Inftest_mod(mod_c_2, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3844 0.6300 0.1073 0.8302 20.0000 0.2652 Inftest_mod(mod_c_3, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3285 0.6605 0.2078 0.1439 16.0000 0.2592 Inftest_mod(mod_c_4, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3132 0.6627 0.2233 0.1210 15.0000 0.2645 Inftest_mod(mod_c_5, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.511125 -0.046239 0.065749 0.007058 15.000000 0.448106 Inftest_mod(mod_c_6, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.359923 0.444061 0.003014 0.128642 6.000000 0.266542 Infsummary(mod_c_5)##
## Call:
## lm(formula = c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (k + lamda +
## alpha + omega + miu), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5860 -0.2928 -0.0346 0.2441 0.6879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.59e+00 1.00e+01 -0.86 0.396
## cases -9.08e-06 4.58e-06 -1.98 0.054 .
## deaths 1.07e-04 5.50e-05 1.95 0.057 .
## recovered 8.43e-06 1.71e-05 0.49 0.624
## POP_LATITUDE 1.40e-01 1.30e-01 1.08 0.287
## POP_LONGITUDE -3.45e-02 4.33e-02 -0.80 0.430
## PopulationEstimate2018 -2.53e-06 6.99e-05 -0.04 0.971
## PopTotalMale2017 7.75e-06 1.34e-04 0.06 0.954
## PopulationEstimate_above65_2017 -1.82e-05 4.76e-05 -0.38 0.704
## PopulationDensityperSqMile2010 -5.00e-04 3.61e-04 -1.38 0.173
## DiabetesPercentage 5.57e-01 5.43e-01 1.03 0.310
## Smokers_Percentage -3.40e-01 2.35e-01 -1.45 0.155
## HeartDiseaseMortality 3.89e-02 2.61e-02 1.49 0.143
## StrokeMortality -1.52e-01 1.25e-01 -1.22 0.231
## days 3.81e-03 3.81e-03 1.00 0.323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.423 on 44 degrees of freedom
## Multiple R-squared: 0.206, Adjusted R-squared: -0.0462
## F-statistic: 0.817 on 14 and 44 DF, p-value: 0.647summary(mod_c_6)##
## Call:
## lm(formula = c ~ k + lamda + alpha + omega + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8087 -0.1702 -0.0649 0.1658 0.5454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.146 0.246 4.66 2.2e-05 ***
## k -0.471 0.226 -2.08 0.042 *
## lamda -10.522 1.640 -6.41 3.9e-08 ***
## alpha 4.111 1.902 2.16 0.035 *
## omega -36.160 42.431 -0.85 0.398
## miu 7.844 1.493 5.25 2.7e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.308 on 53 degrees of freedom
## Multiple R-squared: 0.492, Adjusted R-squared: 0.444
## F-statistic: 10.3 on 5 and 53 DF, p-value: 6.44e-07mod_c = mod_c_3
# diagnostics(mod_c, testit = FALSE)Modeling alpha
# full additive model
mod_alpha_full = lm(alpha ~ ., data = data_trn)
test_mod(mod_alpha_full, k = 5)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02896 0.51252 0.02816 0.05511 29.00000 0.02461 Infsummary(mod_alpha_full)##
## Call:
## lm(formula = alpha ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03333 -0.00934 0.00033 0.00861 0.04447
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.47e+00 6.20e-01 -2.36 0.0231 *
## k 6.10e-02 2.03e-02 3.00 0.0046 **
## sigma NA NA NA NA
## lamda 7.64e-01 1.86e-01 4.11 0.0002 ***
## c 2.69e-02 1.14e-02 2.35 0.0238 *
## omega 8.14e+00 3.45e+00 2.36 0.0234 *
## miu -2.87e-01 2.94e-01 -0.98 0.3354
## cases 1.56e-07 2.30e-07 0.68 0.5017
## deaths 2.61e-06 2.67e-06 0.98 0.3351
## recovered -1.42e-06 9.02e-07 -1.57 0.1238
## POP_LATITUDE 2.06e-02 7.74e-03 2.66 0.0114 *
## POP_LONGITUDE -5.75e-03 2.69e-03 -2.14 0.0389 *
## PopulationEstimate2018 9.50e-06 4.56e-06 2.08 0.0439 *
## PopTotalMale2017 -1.79e-05 8.77e-06 -2.04 0.0483 *
## PopulationEstimate_above65_2017 -6.43e-06 2.77e-06 -2.32 0.0256 *
## PopulationDensityperSqMile2010 -4.30e-05 1.86e-05 -2.31 0.0265 *
## DiabetesPercentage 8.44e-02 3.10e-02 2.72 0.0097 **
## Smokers_Percentage -3.85e-02 1.27e-02 -3.03 0.0043 **
## HeartDiseaseMortality 3.77e-03 1.33e-03 2.83 0.0072 **
## StrokeMortality -1.93e-02 6.55e-03 -2.94 0.0054 **
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days 2.00e-05 2.17e-04 0.09 0.9272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0192 on 39 degrees of freedom
## Multiple R-squared: 0.672, Adjusted R-squared: 0.513
## F-statistic: 4.21 on 19 and 39 DF, p-value: 0.0000719# small additive model
mod_alpha_1 = lm(alpha ~ days, data = data_trn)
# large additive model
mod_alpha_2 = lm(alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_alpha_1, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.751e-02 4.329e-02 6.639e-02 1.278e-09 2.000e+00 2.533e-02 Inftest_mod(mod_alpha_2, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02896 0.51252 0.02816 0.05511 20.00000 0.02461 Infsummary(mod_alpha_1)##
## Call:
## lm(formula = alpha ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02535 -0.01478 -0.00667 0.00167 0.12315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.029069 0.007897 3.68 0.00052 ***
## days -0.000265 0.000139 -1.90 0.06200 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0269 on 57 degrees of freedom
## Multiple R-squared: 0.0598, Adjusted R-squared: 0.0433
## F-statistic: 3.62 on 1 and 57 DF, p-value: 0.062summary(mod_alpha_2)##
## Call:
## lm(formula = alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03333 -0.00934 0.00033 0.00861 0.04447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.47e+00 6.20e-01 -2.36 0.0231 *
## k 6.10e-02 2.03e-02 3.00 0.0046 **
## lamda 7.64e-01 1.86e-01 4.11 0.0002 ***
## c 2.69e-02 1.14e-02 2.35 0.0238 *
## omega 8.14e+00 3.45e+00 2.36 0.0234 *
## miu -2.87e-01 2.94e-01 -0.98 0.3354
## cases 1.56e-07 2.30e-07 0.68 0.5017
## deaths 2.61e-06 2.67e-06 0.98 0.3351
## recovered -1.42e-06 9.02e-07 -1.57 0.1238
## POP_LATITUDE 2.06e-02 7.74e-03 2.66 0.0114 *
## POP_LONGITUDE -5.75e-03 2.69e-03 -2.14 0.0389 *
## PopulationEstimate2018 9.50e-06 4.56e-06 2.08 0.0439 *
## PopTotalMale2017 -1.79e-05 8.77e-06 -2.04 0.0483 *
## PopulationEstimate_above65_2017 -6.43e-06 2.77e-06 -2.32 0.0256 *
## PopulationDensityperSqMile2010 -4.30e-05 1.86e-05 -2.31 0.0265 *
## DiabetesPercentage 8.44e-02 3.10e-02 2.72 0.0097 **
## Smokers_Percentage -3.85e-02 1.27e-02 -3.03 0.0043 **
## HeartDiseaseMortality 3.77e-03 1.33e-03 2.83 0.0072 **
## StrokeMortality -1.93e-02 6.55e-03 -2.94 0.0054 **
## days 2.00e-05 2.17e-04 0.09 0.9272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0192 on 39 degrees of freedom
## Multiple R-squared: 0.672, Adjusted R-squared: 0.513
## F-statistic: 4.21 on 19 and 39 DF, p-value: 0.0000719# intermediate model
mod_alpha_3 = lm(alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_alpha_4 = lm(alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_alpha_1, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.751e-02 4.329e-02 6.639e-02 1.278e-09 2.000e+00 2.533e-02 Inftest_mod(mod_alpha_2, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02896 0.51252 0.02816 0.05511 20.00000 0.02461 Inftest_mod(mod_alpha_3, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.025490 0.509506 0.008626 0.008274 16.000000 0.022995 Inftest_mod(mod_alpha_4, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.025671 0.485239 0.007565 0.019305 15.000000 0.023262 Infsummary(mod_alpha_3)##
## Call:
## lm(formula = alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03054 -0.01042 0.00047 0.00781 0.06194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.77e+01 6.42e+01 -1.52 0.1354
## k 8.45e-02 1.86e-02 4.54 4.5e-05 ***
## lamda 9.36e-01 1.77e-01 5.29 3.9e-06 ***
## c 3.78e-02 1.07e-02 3.52 0.0010 **
## miu -5.21e-01 2.62e-01 -1.99 0.0530 .
## POP_LATITUDE 1.87e+00 1.18e+00 1.58 0.1210
## POP_LONGITUDE -1.29e+00 8.74e-01 -1.47 0.1481
## PopulationEstimate_above65_2017 1.34e-05 1.03e-05 1.31 0.1985
## DiabetesPercentage 7.53e-01 4.76e-01 1.58 0.1210
## Smokers_Percentage -3.95e-01 2.55e-01 -1.55 0.1284
## HeartDiseaseMortality 8.19e-03 4.33e-03 1.89 0.0653 .
## StrokeMortality -4.47e-02 2.13e-02 -2.10 0.0417 *
## log(days) -1.69e-02 4.29e-03 -3.93 0.0003 ***
## stay_at_home -9.37e-02 6.90e-02 -1.36 0.1817
## I(POP_LATITUDE^2) -2.34e-02 1.49e-02 -1.57 0.1238
## I(POP_LONGITUDE^2) -6.68e-03 4.56e-03 -1.47 0.1499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0193 on 43 degrees of freedom
## Multiple R-squared: 0.636, Adjusted R-squared: 0.51
## F-statistic: 5.02 on 15 and 43 DF, p-value: 0.0000154summary(mod_alpha_4)##
## Call:
## lm(formula = alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03305 -0.01194 0.00035 0.00802 0.05910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.93e+00 5.82e-01 -3.32 0.00180 **
## k 8.67e-02 1.90e-02 4.56 4.1e-05 ***
## lamda 9.95e-01 1.78e-01 5.59 1.3e-06 ***
## c 4.35e-02 1.05e-02 4.14 0.00015 ***
## miu -6.93e-01 2.49e-01 -2.78 0.00794 **
## POP_LATITUDE 2.47e-02 7.17e-03 3.44 0.00127 **
## POP_LONGITUDE -8.54e-03 2.49e-03 -3.43 0.00131 **
## PopulationEstimate2018 1.49e-05 4.30e-06 3.46 0.00121 **
## PopTotalMale2017 -2.85e-05 8.26e-06 -3.45 0.00125 **
## PopulationEstimate_above65_2017 -8.49e-06 2.51e-06 -3.39 0.00150 **
## DiabetesPercentage 1.01e-01 2.69e-02 3.77 0.00048 ***
## Smokers_Percentage -3.85e-02 1.01e-02 -3.81 0.00043 ***
## HeartDiseaseMortality 3.11e-03 9.64e-04 3.22 0.00239 **
## StrokeMortality -1.83e-02 4.79e-03 -3.81 0.00042 ***
## log(days) -1.66e-02 4.39e-03 -3.78 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0197 on 44 degrees of freedom
## Multiple R-squared: 0.609, Adjusted R-squared: 0.485
## F-statistic: 4.91 on 14 and 44 DF, p-value: 0.0000239# intermediate model
# relatively bad models
mod_alpha_5 = lm(alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (k + lamda + c + omega + miu),
data = data_trn)
mod_alpha_6 = lm(alpha ~ k + lamda + c + omega + miu,
data = data_trn)
test_mod(mod_alpha_1, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.751e-02 4.329e-02 6.639e-02 1.278e-09 2.000e+00 2.533e-02 Inftest_mod(mod_alpha_2, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02896 0.51252 0.02816 0.05511 20.00000 0.02461 Inftest_mod(mod_alpha_3, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.025490 0.509506 0.008626 0.008274 16.000000 0.022995 Inftest_mod(mod_alpha_4, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.025671 0.485239 0.007565 0.019305 15.000000 0.023262 Inftest_mod(mod_alpha_5, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 3.376e-02 1.156e-01 2.723e-02 1.127e-05 1.500e+01 2.189e-02 Inftest_mod(mod_alpha_6, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.02497976 0.39740134 0.00033377 0.00004824 6.00000000 0.01597093 Infsummary(mod_alpha_5)##
## Call:
## lm(formula = alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (k + lamda +
## c + omega + miu), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04609 -0.01070 -0.00096 0.00612 0.10069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.15e+00 6.13e-01 -1.88 0.067 .
## cases -3.10e-08 2.80e-07 -0.11 0.912
## deaths 2.89e-06 3.36e-06 0.86 0.394
## recovered -7.14e-07 1.05e-06 -0.68 0.498
## POP_LATITUDE 1.68e-02 7.95e-03 2.11 0.041 *
## POP_LONGITUDE -4.95e-03 2.65e-03 -1.87 0.068 .
## PopulationEstimate2018 7.92e-06 4.28e-06 1.85 0.071 .
## PopTotalMale2017 -1.50e-05 8.21e-06 -1.83 0.074 .
## PopulationEstimate_above65_2017 -4.55e-06 2.91e-06 -1.56 0.126
## PopulationDensityperSqMile2010 -4.13e-05 2.21e-05 -1.87 0.068 .
## DiabetesPercentage 7.06e-02 3.32e-02 2.13 0.039 *
## Smokers_Percentage -3.17e-02 1.44e-02 -2.20 0.033 *
## HeartDiseaseMortality 3.14e-03 1.59e-03 1.97 0.056 .
## StrokeMortality -1.54e-02 7.65e-03 -2.02 0.050 *
## days -1.80e-04 2.33e-04 -0.77 0.443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0259 on 44 degrees of freedom
## Multiple R-squared: 0.329, Adjusted R-squared: 0.116
## F-statistic: 1.54 on 14 and 44 DF, p-value: 0.136summary(mod_alpha_6)##
## Call:
## lm(formula = alpha ~ k + lamda + c + omega + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03314 -0.00985 -0.00122 0.00752 0.07859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05160 0.01893 -2.73 0.00866 **
## k 0.02827 0.01580 1.79 0.07935 .
## lamda 0.51238 0.13399 3.82 0.00035 ***
## c 0.01970 0.00912 2.16 0.03520 *
## omega 11.31149 2.51653 4.49 0.000038 ***
## miu -0.31904 0.11971 -2.66 0.01018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0213 on 53 degrees of freedom
## Multiple R-squared: 0.449, Adjusted R-squared: 0.397
## F-statistic: 8.65 on 5 and 53 DF, p-value: 4.81e-06mod_alpha = mod_alpha_4
# diagnostics(mod_alpha, testit = FALSE)Modeling omega
# full additive model
mod_omega_full = lm(omega ~ ., data = data_trn)
test_mod(mod_omega_full, k = 6)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0014028 0.5044044 0.0275331 0.3528000 29.0000000 0.0007156 Infsummary(mod_omega_full)##
## Call:
## lm(formula = omega ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0017815 -0.0003972 -0.0000795 0.0003670 0.0016757
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.58e-02 2.82e-02 -1.27 0.21271
## k 8.68e-04 9.69e-04 0.89 0.37630
## sigma NA NA NA NA
## lamda 1.81e-03 9.66e-03 0.19 0.85212
## c 1.94e-04 5.30e-04 0.37 0.71646
## alpha 1.54e-02 6.51e-03 2.36 0.02342 *
## miu 1.17e-03 1.29e-02 0.09 0.92811
## cases -5.02e-09 1.00e-08 -0.50 0.61947
## deaths -1.17e-07 1.16e-07 -1.01 0.31698
## recovered 5.32e-08 3.95e-08 1.35 0.18586
## POP_LATITUDE 3.95e-04 3.60e-04 1.10 0.27871
## POP_LONGITUDE -1.93e-04 1.20e-04 -1.61 0.11503
## PopulationEstimate2018 2.40e-07 2.05e-07 1.17 0.24989
## PopTotalMale2017 -4.57e-07 3.94e-07 -1.16 0.25348
## PopulationEstimate_above65_2017 -1.61e-07 1.26e-07 -1.28 0.20858
## PopulationDensityperSqMile2010 8.04e-07 8.54e-07 0.94 0.35190
## DiabetesPercentage 1.99e-03 1.43e-03 1.39 0.17223
## Smokers_Percentage -6.98e-04 6.02e-04 -1.16 0.25337
## HeartDiseaseMortality 5.72e-05 6.28e-05 0.91 0.36748
## StrokeMortality -3.36e-04 3.10e-04 -1.09 0.28441
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days -3.03e-05 8.08e-06 -3.75 0.00058 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000834 on 39 degrees of freedom
## Multiple R-squared: 0.667, Adjusted R-squared: 0.504
## F-statistic: 4.11 on 19 and 39 DF, p-value: 0.0000931# small additive model
mod_omega_1 = lm(omega ~ days, data = data_trn)
# large additive model
mod_omega_2 = lm(omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_omega_1, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0010009 0.3186830 0.0578837 0.0002535 2.0000000 0.0009460 Inftest_mod(mod_omega_2, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0014028 0.5044044 0.0275331 0.3528000 20.0000000 0.0007156 Infsummary(mod_omega_1)##
## Call:
## lm(formula = omega ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.001438 -0.000550 -0.000128 0.000312 0.003806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.52e-03 2.87e-04 8.78 3.5e-12 ***
## days -2.69e-05 5.07e-06 -5.30 1.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000977 on 57 degrees of freedom
## Multiple R-squared: 0.33, Adjusted R-squared: 0.319
## F-statistic: 28.1 on 1 and 57 DF, p-value: 1.92e-06summary(mod_omega_2)##
## Call:
## lm(formula = omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0017815 -0.0003972 -0.0000795 0.0003670 0.0016757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.58e-02 2.82e-02 -1.27 0.21271
## k 8.68e-04 9.69e-04 0.89 0.37630
## lamda 1.81e-03 9.66e-03 0.19 0.85212
## c 1.94e-04 5.30e-04 0.37 0.71646
## alpha 1.54e-02 6.51e-03 2.36 0.02342 *
## miu 1.17e-03 1.29e-02 0.09 0.92811
## cases -5.02e-09 1.00e-08 -0.50 0.61947
## deaths -1.17e-07 1.16e-07 -1.01 0.31698
## recovered 5.32e-08 3.95e-08 1.35 0.18586
## POP_LATITUDE 3.95e-04 3.60e-04 1.10 0.27871
## POP_LONGITUDE -1.93e-04 1.20e-04 -1.61 0.11503
## PopulationEstimate2018 2.40e-07 2.05e-07 1.17 0.24989
## PopTotalMale2017 -4.57e-07 3.94e-07 -1.16 0.25348
## PopulationEstimate_above65_2017 -1.61e-07 1.26e-07 -1.28 0.20858
## PopulationDensityperSqMile2010 8.04e-07 8.54e-07 0.94 0.35190
## DiabetesPercentage 1.99e-03 1.43e-03 1.39 0.17223
## Smokers_Percentage -6.98e-04 6.02e-04 -1.16 0.25337
## HeartDiseaseMortality 5.72e-05 6.28e-05 0.91 0.36748
## StrokeMortality -3.36e-04 3.10e-04 -1.09 0.28441
## days -3.03e-05 8.08e-06 -3.75 0.00058 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000834 on 39 degrees of freedom
## Multiple R-squared: 0.667, Adjusted R-squared: 0.504
## F-statistic: 4.11 on 19 and 39 DF, p-value: 0.0000931# intermediate model
mod_omega_3 = lm(omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_omega_4 = lm(omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_omega_1, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0010009 0.3186830 0.0578837 0.0002535 2.0000000 0.0009460 Inftest_mod(mod_omega_2, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0014028 0.5044044 0.0275331 0.3528000 20.0000000 0.0007156 Inftest_mod(mod_omega_3, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.001163 0.604801 0.030730 0.350808 17.000000 0.000540 Inftest_mod(mod_omega_4, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0011062 0.6139613 0.0219400 0.3405360 16.0000000 0.0005402 Infsummary(mod_omega_3)##
## Call:
## lm(formula = omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0016894 -0.0004084 -0.0000319 0.0003170 0.0016192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.49e+00 2.55e+00 -1.37 0.18
## k 1.31e-03 8.75e-04 1.49 0.14
## lamda 8.57e-03 8.79e-03 0.98 0.33
## c 4.17e-04 4.71e-04 0.89 0.38
## alpha 7.54e-03 5.90e-03 1.28 0.21
## miu 5.88e-03 1.06e-02 0.56 0.58
## POP_LATITUDE 6.49e-02 4.70e-02 1.38 0.17
## POP_LONGITUDE -4.70e-02 3.46e-02 -1.36 0.18
## PopulationEstimate_above65_2017 5.07e-07 4.04e-07 1.25 0.22
## DiabetesPercentage 2.69e-02 1.89e-02 1.42 0.16
## Smokers_Percentage -1.43e-02 1.01e-02 -1.41 0.17
## HeartDiseaseMortality 2.89e-04 1.74e-04 1.66 0.10
## StrokeMortality -1.45e-03 8.64e-04 -1.68 0.10
## log(days) -1.26e-03 1.94e-04 -6.51 7.2e-08 ***
## stay_at_home -3.61e-03 2.73e-03 -1.32 0.19
## I(POP_LATITUDE^2) -8.15e-04 5.92e-04 -1.38 0.18
## I(POP_LONGITUDE^2) -2.45e-04 1.81e-04 -1.35 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000744 on 42 degrees of freedom
## Multiple R-squared: 0.714, Adjusted R-squared: 0.605
## F-statistic: 6.55 on 16 and 42 DF, p-value: 4.96e-07summary(mod_omega_4)##
## Call:
## lm(formula = omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0016885 -0.0004072 -0.0000307 0.0003192 0.0016125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.34e-02 2.43e-02 -1.78 0.081 .
## k 1.31e-03 8.61e-04 1.52 0.136
## lamda 8.58e-03 8.68e-03 0.99 0.328
## c 4.14e-04 4.62e-04 0.90 0.375
## alpha 7.45e-03 5.62e-03 1.32 0.192
## miu 6.04e-03 1.01e-02 0.60 0.552
## POP_LATITUDE 5.60e-04 3.01e-04 1.86 0.070 .
## POP_LONGITUDE -2.34e-04 1.04e-04 -2.24 0.030 *
## PopulationEstimate2018 3.04e-07 1.81e-07 1.68 0.101
## PopTotalMale2017 -5.80e-07 3.47e-07 -1.67 0.102
## PopulationEstimate_above65_2017 -1.94e-07 1.05e-07 -1.85 0.071 .
## DiabetesPercentage 2.61e-03 1.15e-03 2.26 0.029 *
## Smokers_Percentage -1.08e-03 4.35e-04 -2.49 0.017 *
## HeartDiseaseMortality 9.78e-05 4.00e-05 2.44 0.019 *
## StrokeMortality -5.00e-04 2.06e-04 -2.43 0.019 *
## log(days) -1.26e-03 1.89e-04 -6.69 3.6e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000736 on 43 degrees of freedom
## Multiple R-squared: 0.714, Adjusted R-squared: 0.614
## F-statistic: 7.15 on 15 and 43 DF, p-value: 1.79e-07# intermediate model
# relatively bad models
mod_omega_5 = lm(omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (k + lamda + c + alpha + miu),
data = data_trn)
mod_omega_6 = lm(omega ~ k + lamda + c + alpha + miu,
data = data_trn)
test_mod(mod_omega_1, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0010009 0.3186830 0.0578837 0.0002535 2.0000000 0.0009460 Inftest_mod(mod_omega_2, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0014028 0.5044044 0.0275331 0.3528000 20.0000000 0.0007156 Inftest_mod(mod_omega_3, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.001163 0.604801 0.030730 0.350808 17.000000 0.000540 Inftest_mod(mod_omega_4, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0011062 0.6139613 0.0219400 0.3405360 16.0000000 0.0005402 Inftest_mod(mod_omega_5, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0011632 0.4042799 0.0001611 0.0260695 15.0000000 0.0007283 Inftest_mod(mod_omega_6, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.00150272 0.29957618 0.18905170 0.00009094 6.00000000 0.00085551 Infsummary(mod_omega_5)##
## Call:
## lm(formula = omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (k + lamda +
## c + alpha + miu), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0023595 -0.0003765 0.0000225 0.0003963 0.0029578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.88e-02 2.17e-02 -2.25 0.02941 *
## cases -7.38e-09 9.90e-09 -0.74 0.46038
## deaths -4.70e-08 1.19e-07 -0.39 0.69477
## recovered 4.38e-08 3.70e-08 1.19 0.24185
## POP_LATITUDE 6.03e-04 2.81e-04 2.14 0.03756 *
## POP_LONGITUDE -2.47e-04 9.36e-05 -2.64 0.01135 *
## PopulationEstimate2018 3.28e-07 1.51e-07 2.17 0.03570 *
## PopTotalMale2017 -6.24e-07 2.90e-07 -2.15 0.03699 *
## PopulationEstimate_above65_2017 -2.05e-07 1.03e-07 -1.99 0.05320 .
## PopulationDensityperSqMile2010 1.35e-07 7.81e-07 0.17 0.86377
## DiabetesPercentage 2.79e-03 1.17e-03 2.38 0.02158 *
## Smokers_Percentage -1.06e-03 5.09e-04 -2.09 0.04219 *
## HeartDiseaseMortality 9.58e-05 5.64e-05 1.70 0.09642 .
## StrokeMortality -5.11e-04 2.71e-04 -1.89 0.06559 .
## days -3.11e-05 8.24e-06 -3.78 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000914 on 44 degrees of freedom
## Multiple R-squared: 0.548, Adjusted R-squared: 0.404
## F-statistic: 3.81 on 14 and 44 DF, p-value: 0.000331summary(mod_omega_6)##
## Call:
## lm(formula = omega ~ k + lamda + c + alpha + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.001569 -0.000517 -0.000270 0.000534 0.002377
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001837 0.000904 2.03 0.047 *
## k -0.001094 0.000741 -1.48 0.146
## lamda -0.005744 0.006985 -0.82 0.415
## c -0.000374 0.000439 -0.85 0.398
## alpha 0.024399 0.005428 4.49 0.000038 ***
## miu 0.010868 0.005730 1.90 0.063 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000991 on 53 degrees of freedom
## Multiple R-squared: 0.36, Adjusted R-squared: 0.3
## F-statistic: 5.96 on 5 and 53 DF, p-value: 0.000191mod_omega = mod_omega_4
# diagnostics(mod_omega, testit = FALSE)Modeling miu
# full additive model
mod_miu_full = lm(miu ~ ., data = data_trn)
test_mod(mod_miu_full, k = 7)## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading
## Warning in predict.lm(model, newdata = data_tst): prediction from a rank-
## deficient fit may be misleading## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.017161 0.894881 0.352920 0.928797 29.000000 0.009206 41.837402summary(mod_miu_full)##
## Call:
## lm(formula = miu ~ ., data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.018275 -0.005736 -0.000703 0.005370 0.019586
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.22e+00 2.99e-01 -4.08 0.00022 ***
## k 2.53e-02 1.14e-02 2.21 0.03272 *
## sigma NA NA NA NA
## lamda 4.32e-01 9.79e-02 4.41 7.8e-05 ***
## c 2.56e-02 5.15e-03 4.96 1.4e-05 ***
## alpha -8.31e-02 8.52e-02 -0.98 0.33538
## omega 1.80e-01 1.98e+00 0.09 0.92811
## cases -1.75e-07 1.22e-07 -1.44 0.15723
## deaths -1.08e-06 1.44e-06 -0.75 0.45731
## recovered 1.19e-06 4.63e-07 2.57 0.01415 *
## POP_LATITUDE 1.44e-02 3.89e-03 3.71 0.00065 ***
## POP_LONGITUDE -4.99e-03 1.31e-03 -3.82 0.00046 ***
## PopulationEstimate2018 1.01e-05 2.02e-06 5.03 1.1e-05 ***
## PopTotalMale2017 -1.96e-05 3.85e-06 -5.09 9.5e-06 ***
## PopulationEstimate_above65_2017 -4.73e-06 1.40e-06 -3.38 0.00165 **
## PopulationDensityperSqMile2010 2.30e-05 1.00e-05 2.29 0.02739 *
## DiabetesPercentage 4.43e-02 1.68e-02 2.64 0.01188 *
## Smokers_Percentage -1.10e-02 7.39e-03 -1.49 0.14522
## HeartDiseaseMortality 6.60e-04 7.79e-04 0.85 0.40224
## StrokeMortality -4.27e-03 3.84e-03 -1.11 0.27299
## Hospitals NA NA NA NA
## ICU_beds NA NA NA NA
## HospParticipatinginNetwork2017 NA NA NA NA
## stay_at_home NA NA NA NA
## above_50_gatherings NA NA NA NA
## above_500_gatherings NA NA NA NA
## restaurant_dine_in NA NA NA NA
## entertainment_gym NA NA NA NA
## days -1.30e-04 1.15e-04 -1.13 0.26392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0103 on 39 degrees of freedom
## Multiple R-squared: 0.929, Adjusted R-squared: 0.895
## F-statistic: 27 on 19 and 39 DF, p-value: <2e-16# small additive model
mod_miu_1 = lm(miu ~ days, data = data_trn)
# large additive model
mod_miu_2 = lm(miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
test_mod(mod_miu_1, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.032721 -0.015921 0.032293 0.000945 2.000000 0.025313 149.431195test_mod(mod_miu_2, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.017161 0.894881 0.352920 0.928797 20.000000 0.009206 41.837402summary(mod_miu_1)##
## Call:
## lm(formula = miu ~ days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04092 -0.02621 -0.00039 0.01751 0.10555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0363221 0.0094359 3.85 0.0003 ***
## days 0.0000503 0.0001666 0.30 0.7639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0321 on 57 degrees of freedom
## Multiple R-squared: 0.0016, Adjusted R-squared: -0.0159
## F-statistic: 0.0911 on 1 and 57 DF, p-value: 0.764summary(mod_miu_2)##
## Call:
## lm(formula = miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.018275 -0.005736 -0.000703 0.005370 0.019586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.22e+00 2.99e-01 -4.08 0.00022 ***
## k 2.53e-02 1.14e-02 2.21 0.03272 *
## lamda 4.32e-01 9.79e-02 4.41 7.8e-05 ***
## c 2.56e-02 5.15e-03 4.96 1.4e-05 ***
## alpha -8.31e-02 8.52e-02 -0.98 0.33538
## omega 1.80e-01 1.98e+00 0.09 0.92811
## cases -1.75e-07 1.22e-07 -1.44 0.15723
## deaths -1.08e-06 1.44e-06 -0.75 0.45731
## recovered 1.19e-06 4.63e-07 2.57 0.01415 *
## POP_LATITUDE 1.44e-02 3.89e-03 3.71 0.00065 ***
## POP_LONGITUDE -4.99e-03 1.31e-03 -3.82 0.00046 ***
## PopulationEstimate2018 1.01e-05 2.02e-06 5.03 1.1e-05 ***
## PopTotalMale2017 -1.96e-05 3.85e-06 -5.09 9.5e-06 ***
## PopulationEstimate_above65_2017 -4.73e-06 1.40e-06 -3.38 0.00165 **
## PopulationDensityperSqMile2010 2.30e-05 1.00e-05 2.29 0.02739 *
## DiabetesPercentage 4.43e-02 1.68e-02 2.64 0.01188 *
## Smokers_Percentage -1.10e-02 7.39e-03 -1.49 0.14522
## HeartDiseaseMortality 6.60e-04 7.79e-04 0.85 0.40224
## StrokeMortality -4.27e-03 3.84e-03 -1.11 0.27299
## days -1.30e-04 1.15e-04 -1.13 0.26392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0103 on 39 degrees of freedom
## Multiple R-squared: 0.929, Adjusted R-squared: 0.895
## F-statistic: 27 on 19 and 39 DF, p-value: <2e-16# intermediate model
mod_miu_3 = lm(miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010
+ log(days) - days
+ stay_at_home - PopulationEstimate2018 - PopTotalMale2017
+ I(POP_LATITUDE ^ 2) + + I(POP_LONGITUDE ^ 2),
data = data_trn)
mod_miu_4 = lm(miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- cases - deaths - omega - recovered - PopulationDensityperSqMile2010 +
log(days) - days,
data = data_trn)
test_mod(mod_miu_1, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.032721 -0.015921 0.032293 0.000945 2.000000 0.025313 149.431195test_mod(mod_miu_2, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.017161 0.894881 0.352920 0.928797 20.000000 0.009206 41.837402test_mod(mod_miu_3, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01807 0.88667 0.20086 0.93226 16.00000 0.01229 47.47454test_mod(mod_miu_4, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01703 0.88089 0.19835 0.83854 15.00000 0.01365 69.23757summary(mod_miu_3)##
## Call:
## lm(formula = miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.020163 -0.005322 -0.000809 0.005689 0.023672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.56e+02 2.80e+01 -5.58 1.5e-06 ***
## k 3.14e-02 1.17e-02 2.69 0.010 *
## lamda 5.46e-01 9.54e-02 5.73 9.2e-07 ***
## c 3.01e-02 5.01e-03 6.01 3.5e-07 ***
## alpha -1.62e-01 8.13e-02 -1.99 0.053 .
## POP_LATITUDE 2.87e+00 5.16e-01 5.56 1.6e-06 ***
## POP_LONGITUDE -2.12e+00 3.81e-01 -5.56 1.6e-06 ***
## PopulationEstimate_above65_2017 2.44e-05 4.49e-06 5.44 2.4e-06 ***
## DiabetesPercentage 1.16e+00 2.08e-01 5.60 1.4e-06 ***
## Smokers_Percentage -6.18e-01 1.11e-01 -5.56 1.6e-06 ***
## HeartDiseaseMortality 1.07e-02 1.91e-03 5.59 1.4e-06 ***
## StrokeMortality -5.26e-02 9.53e-03 -5.52 1.8e-06 ***
## log(days) -2.21e-03 2.77e-03 -0.80 0.429
## stay_at_home -1.65e-01 3.02e-02 -5.45 2.3e-06 ***
## I(POP_LATITUDE^2) -3.61e-02 6.51e-03 -5.55 1.7e-06 ***
## I(POP_LONGITUDE^2) -1.10e-02 1.99e-03 -5.56 1.6e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0107 on 43 degrees of freedom
## Multiple R-squared: 0.916, Adjusted R-squared: 0.887
## F-statistic: 31.3 on 15 and 43 DF, p-value: <2e-16summary(mod_miu_4)##
## Call:
## lm(formula = miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.02263 -0.00679 -0.00071 0.00571 0.02444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.77e+00 2.47e-01 -7.17 6.5e-09 ***
## k 3.60e-02 1.17e-02 3.08 0.0035 **
## lamda 5.93e-01 9.41e-02 6.30 1.2e-07 ***
## c 3.07e-02 5.12e-03 6.00 3.3e-07 ***
## alpha -2.16e-01 7.75e-02 -2.78 0.0079 **
## POP_LATITUDE 2.26e-02 2.95e-03 7.67 1.2e-09 ***
## POP_LONGITUDE -7.10e-03 1.14e-03 -6.24 1.5e-07 ***
## PopulationEstimate2018 1.34e-05 1.81e-06 7.41 2.9e-09 ***
## PopTotalMale2017 -2.58e-05 3.45e-06 -7.47 2.3e-09 ***
## PopulationEstimate_above65_2017 -6.78e-06 1.19e-06 -5.69 9.6e-07 ***
## DiabetesPercentage 7.36e-02 1.32e-02 5.56 1.5e-06 ***
## Smokers_Percentage -2.62e-02 5.16e-03 -5.08 7.3e-06 ***
## HeartDiseaseMortality 2.37e-03 4.79e-04 4.96 1.1e-05 ***
## StrokeMortality -1.15e-02 2.55e-03 -4.51 4.8e-05 ***
## log(days) -3.23e-03 2.78e-03 -1.16 0.2514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.011 on 44 degrees of freedom
## Multiple R-squared: 0.91, Adjusted R-squared: 0.881
## F-statistic: 31.6 on 14 and 44 DF, p-value: <2e-16# intermediate model
# relatively bad models
mod_miu_5 = lm(miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 - stay_at_home - above_50_gatherings - above_500_gatherings - restaurant_dine_in - entertainment_gym - sigma
- (k + lamda + c + alpha + omega),
data = data_trn)
mod_miu_6 = lm(miu ~ k + lamda + c + alpha + omega,
data = data_trn)
test_mod(mod_miu_1, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.032721 -0.015921 0.032293 0.000945 2.000000 0.025313 149.431195test_mod(mod_miu_2, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.017161 0.894881 0.352920 0.928797 20.000000 0.009206 41.837402test_mod(mod_miu_3, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01807 0.88667 0.20086 0.93226 16.00000 0.01229 47.47454test_mod(mod_miu_4, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01703 0.88089 0.19835 0.83854 15.00000 0.01365 69.23757test_mod(mod_miu_5, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.016233 0.832344 0.263518 0.454574 15.000000 0.008545 38.753492test_mod(mod_miu_6, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0272487 0.4796789 0.5420675 0.0002727 6.0000000 0.0187462 86.0715224summary(mod_miu_5)##
## Call:
## lm(formula = miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - (k + lamda +
## c + alpha + omega), data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03007 -0.00653 -0.00129 0.00774 0.02857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.07e+00 3.09e-01 -3.46 0.00122 **
## cases -4.11e-07 1.41e-07 -2.91 0.00571 **
## deaths 2.74e-08 1.70e-06 0.02 0.98721
## recovered 1.90e-06 5.28e-07 3.59 0.00082 ***
## POP_LATITUDE 1.26e-02 4.01e-03 3.15 0.00294 **
## POP_LONGITUDE -4.47e-03 1.34e-03 -3.34 0.00170 **
## PopulationEstimate2018 9.10e-06 2.16e-06 4.21 0.00012 ***
## PopTotalMale2017 -1.78e-05 4.14e-06 -4.28 0.000098 ***
## PopulationEstimate_above65_2017 -3.48e-06 1.47e-06 -2.36 0.02253 *
## PopulationDensityperSqMile2010 2.61e-05 1.12e-05 2.34 0.02369 *
## DiabetesPercentage 3.37e-02 1.68e-02 2.01 0.05055 .
## Smokers_Percentage -6.10e-03 7.26e-03 -0.84 0.40531
## HeartDiseaseMortality 2.01e-04 8.05e-04 0.25 0.80393
## StrokeMortality -1.14e-03 3.86e-03 -0.29 0.77024
## days -8.34e-05 1.18e-04 -0.71 0.48179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0131 on 44 degrees of freedom
## Multiple R-squared: 0.873, Adjusted R-squared: 0.832
## F-statistic: 21.6 on 14 and 44 DF, p-value: 3.44e-15summary(mod_miu_6)##
## Call:
## lm(formula = miu ~ k + lamda + c + alpha + omega, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03282 -0.01332 -0.00260 0.00867 0.07146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04172 0.02101 -1.99 0.052 .
## k 0.01310 0.01744 0.75 0.456
## lamda 0.78055 0.12288 6.35 5.0e-08 ***
## c 0.04364 0.00831 5.25 2.7e-06 ***
## alpha -0.37039 0.13898 -2.66 0.010 *
## omega 5.84918 3.08374 1.90 0.063 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.023 on 53 degrees of freedom
## Multiple R-squared: 0.525, Adjusted R-squared: 0.48
## F-statistic: 11.7 on 5 and 53 DF, p-value: 1.22e-07mod_miu = mod_miu_2
# diagnostics(mod_miu, testit = FALSE)Result
Six Models & Summaries
summary(mod_k)##
## Call:
## lm(formula = k ~ lamda + c + alpha + omega + miu, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0289 -0.0445 0.0356 0.0886 0.2567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1002 0.0791 13.90 <2e-16 ***
## lamda -2.7175 1.2218 -2.22 0.030 *
## c -0.1608 0.0772 -2.08 0.042 *
## alpha 2.0142 1.1260 1.79 0.079 .
## omega -36.1277 24.4670 -1.48 0.146
## miu 0.8043 1.0704 0.75 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.18 on 53 degrees of freedom
## Multiple R-squared: 0.162, Adjusted R-squared: 0.0833
## F-statistic: 2.05 on 5 and 53 DF, p-value: 0.0859summary(mod_lamda)##
## Call:
## lm(formula = lamda ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03373 -0.00652 -0.00214 0.00673 0.02291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.63e+00 3.43e-01 4.74 2.3e-05 ***
## k -6.72e-02 1.10e-02 -6.11 2.3e-07 ***
## c -4.42e-02 4.46e-03 -9.92 8.6e-13 ***
## alpha 4.18e-01 7.46e-02 5.59 1.3e-06 ***
## miu 8.00e-01 1.27e-01 6.30 1.2e-07 ***
## POP_LATITUDE -2.07e-02 4.19e-03 -4.95 1.1e-05 ***
## POP_LONGITUDE 6.72e-03 1.50e-03 4.46 5.5e-05 ***
## PopulationEstimate2018 -1.16e-05 2.61e-06 -4.46 5.6e-05 ***
## PopTotalMale2017 2.21e-05 5.02e-06 4.41 6.6e-05 ***
## PopulationEstimate_above65_2017 7.13e-06 1.47e-06 4.84 1.6e-05 ***
## DiabetesPercentage -7.93e-02 1.61e-02 -4.93 1.2e-05 ***
## Smokers_Percentage 2.99e-02 6.06e-03 4.93 1.2e-05 ***
## HeartDiseaseMortality -2.60e-03 5.73e-04 -4.53 4.5e-05 ***
## StrokeMortality 1.45e-02 2.83e-03 5.13 6.3e-06 ***
## log(days) 7.73e-03 3.06e-03 2.53 0.015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0128 on 44 degrees of freedom
## Multiple R-squared: 0.87, Adjusted R-squared: 0.828
## F-statistic: 21 on 14 and 44 DF, p-value: 5.59e-15summary(mod_c)##
## Call:
## lm(formula = c ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days + stay_at_home - PopulationEstimate2018 -
## PopTotalMale2017 + I(POP_LATITUDE^2) + +I(POP_LONGITUDE^2),
## data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5448 -0.1371 -0.0494 0.1446 0.4962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.85e+03 7.01e+02 4.07 0.00020 ***
## k -1.08e+00 2.31e-01 -4.67 2.9e-05 ***
## lamda -1.53e+01 1.62e+00 -9.43 4.9e-12 ***
## alpha 5.92e+00 1.68e+00 3.52 0.00103 **
## miu 1.52e+01 2.52e+00 6.01 3.5e-07 ***
## POP_LATITUDE -5.29e+01 1.29e+01 -4.10 0.00018 ***
## POP_LONGITUDE 3.86e+01 9.54e+00 4.04 0.00021 ***
## PopulationEstimate_above65_2017 -4.41e-04 1.12e-04 -3.93 0.00030 ***
## DiabetesPercentage -2.13e+01 5.20e+00 -4.10 0.00018 ***
## Smokers_Percentage 1.14e+01 2.78e+00 4.09 0.00019 ***
## HeartDiseaseMortality -2.00e-01 4.74e-02 -4.22 0.00013 ***
## StrokeMortality 1.01e+00 2.33e-01 4.33 8.7e-05 ***
## log(days) 1.40e-01 5.88e-02 2.39 0.02155 *
## stay_at_home 3.00e+00 7.54e-01 3.97 0.00027 ***
## I(POP_LATITUDE^2) 6.65e-01 1.62e-01 4.09 0.00018 ***
## I(POP_LONGITUDE^2) 2.01e-01 4.97e-02 4.04 0.00022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.241 on 43 degrees of freedom
## Multiple R-squared: 0.748, Adjusted R-squared: 0.661
## F-statistic: 8.52 on 15 and 43 DF, p-value: 1.51e-08summary(mod_alpha)##
## Call:
## lm(formula = alpha ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.03305 -0.01194 0.00035 0.00802 0.05910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.93e+00 5.82e-01 -3.32 0.00180 **
## k 8.67e-02 1.90e-02 4.56 4.1e-05 ***
## lamda 9.95e-01 1.78e-01 5.59 1.3e-06 ***
## c 4.35e-02 1.05e-02 4.14 0.00015 ***
## miu -6.93e-01 2.49e-01 -2.78 0.00794 **
## POP_LATITUDE 2.47e-02 7.17e-03 3.44 0.00127 **
## POP_LONGITUDE -8.54e-03 2.49e-03 -3.43 0.00131 **
## PopulationEstimate2018 1.49e-05 4.30e-06 3.46 0.00121 **
## PopTotalMale2017 -2.85e-05 8.26e-06 -3.45 0.00125 **
## PopulationEstimate_above65_2017 -8.49e-06 2.51e-06 -3.39 0.00150 **
## DiabetesPercentage 1.01e-01 2.69e-02 3.77 0.00048 ***
## Smokers_Percentage -3.85e-02 1.01e-02 -3.81 0.00043 ***
## HeartDiseaseMortality 3.11e-03 9.64e-04 3.22 0.00239 **
## StrokeMortality -1.83e-02 4.79e-03 -3.81 0.00042 ***
## log(days) -1.66e-02 4.39e-03 -3.78 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0197 on 44 degrees of freedom
## Multiple R-squared: 0.609, Adjusted R-squared: 0.485
## F-statistic: 4.91 on 14 and 44 DF, p-value: 0.0000239summary(mod_omega)##
## Call:
## lm(formula = omega ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma - cases -
## deaths - omega - recovered - PopulationDensityperSqMile2010 +
## log(days) - days, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0016885 -0.0004072 -0.0000307 0.0003192 0.0016125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.34e-02 2.43e-02 -1.78 0.081 .
## k 1.31e-03 8.61e-04 1.52 0.136
## lamda 8.58e-03 8.68e-03 0.99 0.328
## c 4.14e-04 4.62e-04 0.90 0.375
## alpha 7.45e-03 5.62e-03 1.32 0.192
## miu 6.04e-03 1.01e-02 0.60 0.552
## POP_LATITUDE 5.60e-04 3.01e-04 1.86 0.070 .
## POP_LONGITUDE -2.34e-04 1.04e-04 -2.24 0.030 *
## PopulationEstimate2018 3.04e-07 1.81e-07 1.68 0.101
## PopTotalMale2017 -5.80e-07 3.47e-07 -1.67 0.102
## PopulationEstimate_above65_2017 -1.94e-07 1.05e-07 -1.85 0.071 .
## DiabetesPercentage 2.61e-03 1.15e-03 2.26 0.029 *
## Smokers_Percentage -1.08e-03 4.35e-04 -2.49 0.017 *
## HeartDiseaseMortality 9.78e-05 4.00e-05 2.44 0.019 *
## StrokeMortality -5.00e-04 2.06e-04 -2.43 0.019 *
## log(days) -1.26e-03 1.89e-04 -6.69 3.6e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000736 on 43 degrees of freedom
## Multiple R-squared: 0.714, Adjusted R-squared: 0.614
## F-statistic: 7.15 on 15 and 43 DF, p-value: 1.79e-07summary(mod_miu)##
## Call:
## lm(formula = miu ~ . - Hospitals - ICU_beds - HospParticipatinginNetwork2017 -
## stay_at_home - above_50_gatherings - above_500_gatherings -
## restaurant_dine_in - entertainment_gym - sigma, data = data_trn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.018275 -0.005736 -0.000703 0.005370 0.019586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.22e+00 2.99e-01 -4.08 0.00022 ***
## k 2.53e-02 1.14e-02 2.21 0.03272 *
## lamda 4.32e-01 9.79e-02 4.41 7.8e-05 ***
## c 2.56e-02 5.15e-03 4.96 1.4e-05 ***
## alpha -8.31e-02 8.52e-02 -0.98 0.33538
## omega 1.80e-01 1.98e+00 0.09 0.92811
## cases -1.75e-07 1.22e-07 -1.44 0.15723
## deaths -1.08e-06 1.44e-06 -0.75 0.45731
## recovered 1.19e-06 4.63e-07 2.57 0.01415 *
## POP_LATITUDE 1.44e-02 3.89e-03 3.71 0.00065 ***
## POP_LONGITUDE -4.99e-03 1.31e-03 -3.82 0.00046 ***
## PopulationEstimate2018 1.01e-05 2.02e-06 5.03 1.1e-05 ***
## PopTotalMale2017 -1.96e-05 3.85e-06 -5.09 9.5e-06 ***
## PopulationEstimate_above65_2017 -4.73e-06 1.40e-06 -3.38 0.00165 **
## PopulationDensityperSqMile2010 2.30e-05 1.00e-05 2.29 0.02739 *
## DiabetesPercentage 4.43e-02 1.68e-02 2.64 0.01188 *
## Smokers_Percentage -1.10e-02 7.39e-03 -1.49 0.14522
## HeartDiseaseMortality 6.60e-04 7.79e-04 0.85 0.40224
## StrokeMortality -4.27e-03 3.84e-03 -1.11 0.27299
## days -1.30e-04 1.15e-04 -1.13 0.26392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0103 on 39 degrees of freedom
## Multiple R-squared: 0.929, Adjusted R-squared: 0.895
## F-statistic: 27 on 19 and 39 DF, p-value: <2e-16Model Diagonostics (Test Statistics)
test_mod(mod_k, k = 1)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 2.135e-01 8.328e-02 1.932e-02 7.076e-10 6.000e+00 5.237e-02 5.250e+00test_mod(mod_lamda, k = 3)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.01745 0.82845 0.19372 0.01659 15.00000 0.01620 24.31619test_mod(mod_c, k = 4)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.3285 0.6605 0.2078 0.1439 16.0000 0.2592 Inftest_mod(mod_alpha, k = 5)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.025671 0.485239 0.007565 0.019305 15.000000 0.023262 Inftest_mod(mod_omega, k = 6)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.0011062 0.6139613 0.0219400 0.3405360 16.0000000 0.0005402 Inftest_mod(mod_miu, k = 7)## loocv_rmse adj_r2 bp_pval.BP sw_pval num_params test_rmse perc_err
## 0.017161 0.894881 0.352920 0.928797 20.000000 0.009206 41.837402Model Diagonostics (Plots)
diagnostics(mod_k, testit = FALSE)![[Figure 1.1. Model `k`]](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAAA4ZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAKgAgAEAAAAAQAABUCgAwAEAAAAAQAAA8AAAAAAthHDUQAAQABJREFUeAHs3Qn8VFX9//EPsgnKjiyyy6KAgoAI4r5maqY/rczSn7b610xNrbTNn6ZZWVm/ssyycqkstTR/bpkpioiCICCCrCKy74sg2/zP++L9dufOne37nfnOzL2v83h8mZk7dznnee4wdz73LE1SLhkJAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIocBeMSwTRUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBDwBAqCcCAgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAABBBBAAAEEEEAAAQQQQAABBBBAILYCBEBjW7UUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIgHIOIIAAAggggAACCCCAAAIIIIAAAggggEBsBQiAxrZqKRgCCCCAAAIIIIAAAggggAACCCCAAAIIEADlHEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2AoQAI1t1VIwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyjmAAAIIIIAAAggggAACCCCAAAIIIIAAArEVIAAa26qlYAgggAACCCCAAAIIIIAAAggggAACCCBAAJRzAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0AANLZVS8EQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAo5wACCCCAAAIIIIAAAggggAACCCCAAAIIxFaAAGhsq5aCIYAAAggggAACCCCAAAIIIIAAAggggAABUM4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIitAAHQ2FYtBUMAAQQQQAABBBBAAAEEEEAAAQQQQAABAqCcAwgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAABBBBAAAEEEEAAAQQQQAABBBBAILYCBEBjW7UUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIgHIOIIAAAggggAACCCCAAAIIIIAAAggggEBsBQiAxrZqKRgCCCCAAAIIIIAAAggggAACCCCAAAIIEADlHEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2AoQAI1t1VIwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyjmAAAIIIIAAAggggAACCCCAAAIIIIAAArEVIAAa26qlYAgggAACCCCAAAIIIIAAAggggAACCCBAAJRzAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0AANLZVS8EQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAo5wACCCCAAAIIIIAAAggggAACCCCAAAIIxFaAAGhsq5aCIYAAAggggAACCCCAAAIIIIAAAggggAABUM4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIitAAHQ2FYtBUMAAQQQQAABBBBAAAEEEEAAAQQQQAABAqCcAwgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAABBBBAAAEEEEAAAQQQQAABBBBAILYCBEBjW7UUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIgHIOIIAAAggggAACCCCAAAIIIIAAAggggEBsBQiAxrZqKRgCCCCAAAIIIIAAAggggAACCCCAAAIIEADlHEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2AoQAI1t1VIwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyjmAAAIIIIAAAggggAACCCCAAAIIIIAAArEVIAAa26qlYAgggAACCCCAAAIIIIAAAggggAACCCBAAJRzAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0AANLZVS8EQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAo5wACCCCAAAIIIIAAAggggAACCCCAAAIIxFaAAGhsq5aCIYAAAggggAACCCCAAAIIIIAAAggggAABUM4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIitAAHQ2FYtBUMAAQQQQAABBBBAAAEEEEAAAQQQQAABAqCcAwgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAABBBBAAAEEEEAAAQQQQAABBBBAILYCBEBjW7UUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIgHIOIIAAAggggAACCCCAAAIIIIAAAggggEBsBQiAxrZqKRgCCCCAAAIIIIAAAggggAACCCCAAAIIEADlHEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2AoQAI1t1VIwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyjmAAAIIIIAAAggggAACCCCAAAIIIIAAArEVIAAa26qlYAgggAACCCCAAAIIIIAAAggggAACCCBAAJRzAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0AANLZVS8EQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAo5wACCCCAAAIIIIAAAggggAACCCCAAAIIxFaAAGhsq5aCIYAAAggggAACCCCAAAIIIIAAAggggAABUM4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIitAAHQ2FYtBUMAAQQQQAABBBBAAAEEEEAAAQQQQAABAqCcAwgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAABBBBAAAEEEEAAAQQQQAABBBBAILYCBEBjW7UUDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQIgHIOIIAAAggggAACCCCAAAIIIIAAAggggEBsBQiAxrZqKRgCCCCAAAIIIIAAAggggAACCCCAAAIIEADlHEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2AoQAI1t1VIwBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyjmAAAIIIIAAAggggAACCCCAAAIIIIAAArEVIAAa26qlYAgggAACCCCAAAIIIIAAAggggAACCCBAAJRzAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0AANLZVS8EQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAo5wACCCCAAAIIIIAAAggggAACCCCAAAIIxFaAAGhsq5aCIYAAAggggAACCCCAAAIIIIAAAggggAABUM4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIitAAHQ2FYtBUMAAQQQQAABBBBAAAEEEEAAAQQQQAABAqCcAwgggAACCCCAAAIIIIAAAggggAACCCAQWwECoLGtWgqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRAOQcQQAAB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Twt+akBczaCssSGU1ILqJz/5iR166KGmbqckBBBAoNIC/v9PheSjmHUL2R/rIIAAAgg0XIDrz4YbsgcEEEAAAQTyCezabTbRjet59+wh9vXXTrWr/753zuBnh9Zm5x1m9vhlZq98zXVvP2S5tW+xzfbai/BcPuuGvo9wQwXzbD9t2jT7xS9+4a2lru+PPPKIN67eggULbN26dfaDH/zA2rVzYX+X3nrrLTvuuOMIgnoa/IMAApUU8P9fKiQP7du3L2Q11kEAAQQQaCQBrj8bCZrDIIAAAggkVmCB6/H+/afNRn7P7GO/MXthWQ/bmWoa6dHcLT5+kNkvPmE29Tqz2/7LbFiPPav6vek0TCKpvAIEQMvra7/85S+92ZSbNWtmTz31lJ155pl1kX0FGK699lp78803bfjw4V5OFi9ebCeeeKKtWLGizDlj9wgggEB2gT59+mR/M/BOixYtrFu3boElPEUAAQQQqLQA15+VrgGOjwACCCAQR4HlG83+8LLZCbebHfNjs/99zmzNluwlHdLd7NunmU273uzei8w+6sI+zQIx0vfee8800bV61HXu3Dn7jninJAIEQEvCmH0nCm4qnX/++XVBzvDa3bt3t/Hjx9uxxx7rvaVu8Keffro3dmh4XV4jgAACjSHQs2dP69KlS95DDRs2zHSDh4QAAgggUD0CXH9WT12QEwQQQACB2hbYut3siTfMPnuf2ejvm33jUdd7d2X2MnVqucUuGLnFJlxj9vTlZl84yqxdROPOVCplM2bMMD3qt1fz5m76d1JZBQiAlpXXbM6cOd4RRo0alfNIbdu2tSeeeMKOOOIIb70pU6bYxz/+ca/1aM4NeRMBBBAog4DuQo4ePdr7Mo7avYKe+n+tR48P+m5ErcQyBBBAAIGKCHD9WRF2DooAAgggEBMBF5O0194xu/4R11X9ZrPP32/21CxzwcroArZpaXa2a935/ZOX2I2HPmNHt37e9k2tiV7ZLfWDn8uXL/cakwwePDjrurxROgGa7ZTOMnJP27e72wUutW7tRrrNkzTmw6OPPuoFQefNm2ePP/64XX755XbHHXfk2ZK3EUAAgdIL+EHOAw44wJYuXeq1Sm/atKl16NDBC4yq+zsJAQQQQKD6BLj+rL46IUcIIIAAAtUv8O56swdfM7vvFbNlrrt7rtTUNScc09fs3BF7ura3dNG1VKqHTZ68zPvtNGHCBOvbt69paDF/foWdO3faypUrvflfNmzY4A2PqEYnjP+ZS7p07xEALZ1l5J4GDhxor776qs2a5W4XFJA07sOTTz7pBUFXrVrljSE6YMAA+8pXvlLA1qyCAAIIlF5AAU/9kRBAAAEEakOA68/aqCdyiQACCCBQeYG1bgzPp93IhfdMMpv+bv789N9vT9DzfDeTe6d909dXL7rDDjvMm+dl7ty53gTXGuJQM7yrcYl/g1JbKeipdTt27Ji+E16VTYAu8GWj3bNjXYAq3X///bZ27do9C/P8279/f68lqH8X4JprrrF77rknz1a8jQACCCCAAAIIIICAGdefnAUIIIAAAghkF9i+0+xZN1rhpX82G+Fmcb/m4dzBz25tzS4a67a5wuz5q8wuPy4z+OkfTUHQIUOG2AknnOC1AN17771t9+7dXvBT76lhySGHHOJNfk3w01drnEdagJbZWZMf/fGPf/SaOev5H/7wB+vatWveo44dO9YLmp577rneh+Xiiy/27h7og0NCAAEEEEAAAQQQQCCbANef2WRYjgACCCCQZIE3lrou7lPNHphitnFbbolWzc2OH2R2juvifoobotPFLotKbdq08SbCHj58uDe3i7q/awgxBUFJlRGgBWiZ3TWb+0knneQd5amnnjINbqtg5s9//vO8Rz777LO98T/1AVHg84YbbvC6x+fdkBUQQAABBBBAAAEEEivA9Wdiq56CI4AAAgiEBBa7jri/esHsyNvMPuTCMHdNyB38HNnL7HsfNXv9G2a//pTbZkjxwc9QFkzzKLRs2ZLgZximkV/TArQRwO+8804766yzbMaMGbZu3Tr7/e9/740L+qUvfSnv0b/4xS96H5TPf/7zpjsGtADNS8YKCCCAAAIIIIBA4gW4/kz8KQAAAgggkCiBrVu32rJly7yYy7rNO+31NZ1t/Ltdbcqy0ECdESo927txPUeafWq0Wfd2ESuwKBYCtABthGrUDMqvvPKKXXbZZbbPPvt4R9x///0LPvJFF11kU6dOtaOPPrrgbVgRAQQQQAABBBBAILkCXH8mt+4pOQIIIJAkATUUU2Ozp57+pz304jL73vP72eeePsx+Mrl/zuBnRxea+aSbyOiJy8xe/qobB9R13CX4Ge8zhxagjVS/GvhW3d5vv/12mzx5sm3cuLGoIx988ME2fvx4bzKkX/7ylwXPKl/UQVgZgYQK7Nixw9avX2961ORj7du3p3tCQs8Fio0AAgjESYDrzzjVJmVBAAEEEAgLbNu2zR565nV7dlEHm7DyQ7Z5Z8vwKmmvm++VsmMGNrFz3bieHx5q1qxp2tu8iLkAAdBGruBmzZqZJjiqb7rwwgtNfyQEEGi4gO4Wzpo1y95+++204SU0PovG6+3Tp0/DD8IeEEAAAQQQqLAA158VrgAOjwACCCAQKaAGKCtXrrRNmzZ5jVH0O6xdu3bWuXNnb9zMyI3cwuWuPdn/zdhtv35+l727eUy21eqWH9D+PRvRdr4d2XWJnXTsGGP29TqaRD0hAJqo6qawCCDgC+jLdsKECbZhwwZ/Ud3j+++/b9OmTfO+iNX6moQAAggggAACCCCAAAIIIJApkEqlvLE33333Xa9XnX5L6cabhv/r1q2b16hEs58Hk36LzZkzxxYuXJjWEMVfp3nz5jZw4EDr37+/7bXXnpEbt243e/Yts4fcLO7/nG2WSmn5niEG/e2Cj707mJ1+iNmnDzfr07G16yZvtmDBdu933vHHH0+PvyBWQp4TAE1IRVNMBBBIF5g5c2Zk8DO41vz5861Tp07WvXv3usX6gtcA27t27fK6y+vLnYQAAggggAACCCCAAAIIJE1ALTenTJmS8btKv5UUCF27dq299dZbpkYlfu+6zZs326RJk0yPSvq9pT8FSdWlfdWqVd7+1FNv2bLl1qLHWPv7jOZe4HPrjtzCbfc2O+kgN6GR6+J+zMD0dYcOHWrLly/3GrnoMfgbL31NXsVVgF/uNVSzukuyYsWKuhz37Nmz7nk5n+hOzrPPPuvusKQafBgNTqy0fbu7fUNCoEIC+mJdvHhxQUefO3eu9+WoL3E9X7Rokfdl7m+sO5ua1Kxv377WunVrfzGPCCCAAAIIxEKA689YVCOFQAABBEousG7dOnvppZdMw4ppzOkBAwZY165dvedapvf120ld3NW7bsuWLd46EydOtPfee8/r6j5ixAjvMZy51+etsbueXWvPTe1p67c3D7+d9rqpawg6tt+eoOeZw8xaZolyqSVpv3797I033vBarBIATWNMxIssp0Yiyl5zhZw+fboddpibpuyDVIqApL+vXI+XXnqpPfroo7lWKfq9hx56yL76VTfVGgmBCgisXr264KPqi1stPl955RWvS0d4Q32RKzCqP3XRGDJkSF03jfC6vEYAAQQQQKDWBLj+rLUaI78IIIBA+QXUulOtOBXoVGMQBTKDPeP0XAFG/b3zzjs2depU7/fSmjVrvOBnhw4dbNy4cWnbrN1i9uQss3snmc1Y2skVQn/ZU9e9N9mpgzbb1R/pbprRvZCklqZKarlKSp4AAdDk1XnRJf7yl7/sDRK8e/fuorcNb/DYY495zeAZdDgsw+vGFCi2BbLuWGqW+HxJXeYVLB09enS+VXkfAQQQQAABBHIIcP2ZA4e3EEAAgQoLzJ492+sVp8mK1EirSZMmWXPUq1cvb5xP/aZSl3i1xNQ2CpK+v9PshXl7xvV8/A2zXXlCDt3b7pm9/aguC23r8uk2aNAgF/z8z3BlWTPxwRv+WKTF/h7Mt1/erw0BAqC1UU8VzeWJJ55o+itFGjlypPefnprIkxColIBmFywmqdtGoWnp0qWmYSN69OhR6CashwACCCCAAAIhAa4/QyC8RAABBKpEQK0+/eHEhg8fnjP46WdZ439q0iM1Fmnfvr0tWN/aHnzW7C+vmW3c5q8V/diy6S4b0m65fXpcK/v4ER3d8cx1rW9iry+3tKHJordOX6qWq0rF/h5M3wuvalWAAGgN1Zz+c9FgvSQEEGiYwH777ed9URcyjITG9dQYNcWkBQsWEAAtBox1EUAAAQSqVoDrz6qtGjKGAAIIVERAkxSpd6i6k++7774F52HDrnb2/NIe9tL0A2zlE7k3U5BzVO8943oe3HqevT1/tg3qMMj9huvobdi2rWsK6pLyUkzy1/e3L2Zb1q19AQKgNVSHaiKuQYVJCCDQMAF1fTjggANMXdbzJX05FhsALaS7fL7j8j4CCCCAAALVIMD1ZzXUAnlAAAEEGl9ArSWXLVvm9eDU86ZNm5omgFULUCW15MyX1Lrzn2+aPThVXd3H5FvdenXYE/T81OFm3fbEOF1rz5b2ttvSb72pnWgMUfUq1e809cDTOKT5kvK9cOFCb7VC1s+3P96vPQECoLVXZ+QYAQRKIKDJijZu3JjzruHQoUNNs98W2/Jad0T1p/FtSAgggAACCCCAAAIIIIBArQjod4zG+FRjET3PlrKNo7lzl9nLLs6ooOej0822u9e5Uic3gdGpQ8wucPHRgyPimPo9ptS8+X9mg9eYowceeKC9/vrr3p+Cseq5ly2p55/GIFUQVcHTLl26ZFuV5TEWIABa4crV3QoNBKw7F/rTXYx27dqZWp2pSTljZVa4gjh8UQL6YtGs6du2bfO+oPTlopYj1ZgUnBw7dqzNmzfP1GU9eEdRn8HBgwd7La6LGf/TL2erVq0IfvoYPCKAAAIIVJ0A159VVyVkCAEEEKgKAQUbX375ZS9GoQypB2q3bt1Mv2927drlTQy7aNEir5GIZnfXJEi9e7u+6i7NddMmKOj5p8lmmtE9V2rR1OzoAWYfG7lnUqOmOdqNaOZ4pTZt2qTtUuOKqoWqfq+NHz/eNN9IVGBTv00VKFWjFv021Yz1pGQKVGdkIsZ1sWnTJrvnnnvs/vvvt5kzZ5peZ0v6cB5yyCE2ZswYO+OMM+y0004raIDhbPtjOQLlFFAQUQNbB+8EKsjYr18/L5ioLhPVlpQ/zRw4cOBA27x5s/dFri93/flJ44UqILphwwZ/Ud5HulTkJWIFBBBAAIFGFOD6sxGxORQCCCBQowJqzPLqq696wU/9Hho9erTXWjJYHP3O0WSvzz33nLf435PetPVvdrC/v9nGC4AG14163muf9XbEfkvsG5880Dq1+U+Lzqh1tUy/0fwGKeHhANUKVLPJK88a23PixIl1rTuVf3V5V+McBT4VvFULUsVWwoHUbMdmefwECIA2Up2uWLHCbrzxRrv33ntzBj2D2dEHdurUqd7fr371Kzv44IPt1ltvtdNPPz24Gs8RqLiAuhO8/bZGZklP6jKhrhNq5XzkkUd648akr1Edr/Tlme2L0P9ifeGFF9KCu9lyrhkFFVAlIYAAAgggUGkBrj8rXQMcHwEEEKgdAc3srkCifs8cffTRaY1CgqVo3qqdzd46wP69uJO9sb6rpczNWJQj9XHzFp1y4A7rve1569hiT9PQt+dut06uxWaupN+SiocoMKvWnlEztyuoqV59+s351ltveQFPBT3DSYFbDW+Wq5t8eBtex0+AAGgj1Kk+gCeffLLNmDGj7mgKqnTv3t1rLq4WZrpDoQ+0gp5qoq2xCdWkXEElv2uuWoyeeeaZ9qMf/ciuvPLKun3xBIFKCvjnaa486DMwa9Ysr0VzrvWq9T3NbnjMMcd4XSf8mQOj8qrPsL6Ao76co9ZnGQIIIIAAAuUS4PqzXLLsFwEEEIingAKISuqFGuwRp2UuBmmTXXsXdXF/eJrZ1h1DtThraru3C3oO3jOh0eG9t9uECRNs4+4tXpf61atXe7EObTxs2LDIIdMUA5kyZYrXkEbDAmp4smxJvfrUAEU9D/VbTd9/2l49ajVpk7rwE/jMppes5QRAy1zfW7Zs8Vps+sFPNSP/yle+YieeeKIp8JkvaQyOV155xes2/7vf/c7ronvVVVd53XbVJZ6EQKUF5s6dW1AWNOPeQQcdlDZ4dUEbVslK+vIcN26c6TO9yI17o1Y1GrdX3Sn0nu4qDhgwwDTDPAkBBBBAAIFKCnD9WUl9jo0AAgjUnoAaYPlzkgSH81q8dk/Q84+vmi3fmLtcTfdK2RH9mti5rmHnmYeYtXDRJnU/f/75Gd6+1ahk1KhRXoBSMQ41pFH3do0hqvlP9DtKjcEUxNR7ahxWTAMTBTzVyEx/JASiBAiARqmUcNlf/vIXbywK7fK8887zxv7UHYpCk5p0q+uw/j760Y/aWWed5QVBv/71r9upp57KRCuFQrJeWQT0BZVrHNvgQdV1QQNY6w5cLScFO9V9Qn8kBBBAAAEEqlGA689qrBXyhAACCFSvgAKgSh07dnQTGDWxJ2aZ3feK2cyl+fPcvdVGG9P5HTuy6xLr062NuRkV7PWpO73Wm/q9qKRZ2jX+puIbmqjI712nodLUoCaqUY1+N6qFaLg1av4csQYC0QIEQKNdSrb0pZde8valD64mPyom+BnOhFp83nbbbXbFFVd43enVoq5///7h1XiNQKMJBCc8KuSgxa5fyD5ZBwEEEEAAAQTSBbj+TPfgFQIIIIBAboEt23bYjHVd7U9LB9rER8x27c69fve2Zqcd7Bp5jdhub03+tzdZsxq8rFq1J+Dpb63gpXrJ9e3bNy0W0rZtW2+cUXWHVytRBWDV+1UtPjUBrVqh6pGEQCkFCICWUjNiXxrrQukjH/lISbr+nnPOOV4AVPvUGB0EQCVBqpRAsWNdavwWEgIIIIAAAgiUV4Drz/L6sncEEEAgLgLT33Vd3F8ze2BKH9uyvV/OYrV2I32dcOCecT1PdI9uWhPXG/B908ihGmNTEyetX7/e68au7ujq8p4viNm5c2fTHwmBxhAgAFpm5SVLlnhH6NWrV0mOpLEx1Gxcd0e2bt1akn2yEwTqK+DfoduwYUPeXTRt2tTrUpF3RVZAAAEEEEAAgQYJcP3ZID42RgABBGIt8LYb1/Mf03fbvZNS9u6Gph+UNXqYPgU5D+u9J+j5X4eatQpNd+BPEKtAp34bdu3aNdZ2FK62BaLP8touU1Xl3m+hOXHixJLkS12aFPxUGjFiREn2yU4QaIiAJjYqJKnrg+4EkhBAAAEEEECgvAJcf5bXl70jgAACtSawwbWd+qtr6fmJ36TsyNvMbn16r0DwM7M0vTuk7OoTzV79mtnfvmj2qcMzg5+7d++2BQsWeBsHJ07K3BtLEKgOAQKgZa4HzXKm9MADD7jZz55v0NHUnPzqq6/29qHBifv1y91EvUEHY2MEChTQ4NQHHuj6QORIhayTY/Oyv6XxatSi+v333y/7sTgAAggggAAC5Rbg+rPcwuwfAQQQqH6BnbvMXphndsVfXOOpW8yuetBswgLXpDNLatN8ux3VZZF9/eDn7KaRz9ol47ZaNzfWZ7Y0Y8YM27Jli7Vp08YbszPbeixHoFoECICWuSauu+46r8u6Zj/TLO533nmn1WcimGnTptkpp5xielS65JJLypxzdo9A4QJqBTp69Gjvyy+4lcb81Gzphx9+uDcwdvC9aniuoKc+U48//rg9/fTT9uSTT9pTTz1ls2fPtp07d1ZDFskDAggggAACRQtw/Vk0GRsggAACsRGYs8Ls5ifNRt5q9sm7zR5yIYTtLhgalVq6Dnonuw59d57vZnz/Tgu7fPS71mufDbZ582YbP358Xe/T4LaKZ0yZMsUWLVrkTWyknqlN1FeehECVC9AftcwVpC5It9xyi1177bWmcRIVuNTzY4891g499FCvFafGydDsaAoWKeiiYKlmQXvnnXds3rx53n88M2fOrMupAqE33XRT3WueIFANAur2oL/33nvPO4c1Vq0Gvq7WL0O1qNbQFOEbEvr8zZkzx5YtW2bjxo3zxrKpBl/ygAACCCCAQKECXH8WKsV6CCCAQDwElropGR53IYP7XjGbtyp/mYb3MDvHjaj3sZFmbfb+z/pjx461V1991VasWOH9pnvmmWe8mIXiFbt27fImOdLvJMUtNLzZYYcdZh06dPjPDniGQBULEABthMq55pprTJMXXXbZZV43202bNtljjz3m/RV7+FNPPdXuv/9+705LsduyPgKNIaAZAPVXzUnj6E6aNCkj+BnMs25C6M6mgqAkBBBAAAEEak2A689aqzHyiwACCBQnsGHLDvvbqxvtH2+0tEnv7uM2zt0Kc7+Wm21Ep6V23siddtxhA6xFi9CMRm4Pmrh2zJgxXo+4t956y/u9pMYh4aRGXOrpp+7vJARqRYAAaCPV1MUXX2xnnHGG3X777Xb33Xfb8uXLCz6yZlNT4PNzn/uct4+CN2RFBCIEdLdOXRo0aLVaaEZ98UVsFqtF6q6hlp75kmY1XL16tXXu3DnfqryPAAIIIIBA1Qlw/Vl1VUKGEEAAgQYJuJ9wNnH+drv7+U327KJ2tmN3p5z727fFLhuz/wYbuvebdnDnDV7Lza2rUq6X6VJTa0/9Hgwn9eAbPHiwqeGWWnvut99+Xo9VBUf32Wcfb6b3qO3C++E1AtUmQAC0EWtE/3HcfPPN3p8CMC+//LLNnTvX6+6u7vH6D8bvNty2bVtT96UhQ4bY8OHDI/9jasSsc6gYCGiCn1mzZtmSJUu84KdfpC5dunh373TOJSUVcwNC61ZLAFQtV1WPClonMXCdlPOTciKAAAKlFOD6s5Sa7AsBBBCojMCiNWZ/mbLb/jzZbOVmtdzMHvjcq8luG9R2tX1owCb70hl9bcG8ZW5ovdUuvnCQN2TZa6+95nVl13BgGpov2+8KtfJUAFQNsjTOJwmBWhcgAFqhGuzbt6/pj4RAYwhoXM4XX3zRG4IhfLyVK1famjVrvK4O+pGUhKTJjwpNxaxb6D6LXU91pImZ1q1bV7epAtaDBg2yHj3cAD4kBBBAAAEEChDg+rMAJFZBAAEEKiCgXmfvvvuurV271mvwoPE1dzZrZ1NWdbEn5neyRRvVUjP3HNb7t9pox/RcZf99ZEt7Z950bwKjmdPXet3aVSTNOaIu60cddZT321BzIrzxxhtZg5sa91NJDTBICMRBgABoHGqRMiCQQyCVSnkDWecK5GlAaw12feKJJyZi0h9dUBSa1Cq7kklj7ij4GU4ao3Ty5Mle8HrYsGHht3mNAAIIIIAAAggggAACVS6g32hTp041Db2ltGP3Xvbmhi42aVUvm76um+3OE/Tcr/UOO+OQJtZr23jr1mqzHX/88V6Qs2/3dt5kykuXLvW6sGvfGgpNSV3ZNXnRv/71L1u8eLEddNBBXhd3783AP+p9plTp30OBLPEUgQYJFB4FaNBh2BgBBColoBn8dHcvX9IX3MKFC70vwHzrFvu+9q0vX93RVLBVY8d0797d2rdvX+yuSrK+urRryIlCkiYwq1RSl5Oo4GcwP6oztQatb4ty3dHVOaJWwgoMaxbHjh07msb+ISGAAAIIIIAAAggggEBpBXT9rRaf6t2lofH0+2jJ1k725vYh9syCDrZ5e+7r8FbNUza6+3o7uNUcG9JuhbVzvwU22iY3Nme3ukmJ1NJTjST87u4qgX6LaZg9Jf/3mH6j6TfHAQcc4C0P/qP1lRjvM6jC81oWIABay7VH3hEoQEDdpwtNCoTpDmApk/apL97t27en7VazCqr79qGHHuoF3tLeLPMLfcHrYkOtY3MldfvYf//9c61S1vfefPPNgvavIGmfPn2KClqq7GpdqnGINSFWMLVr185GjhzpBVaDy3mOAAIIIIAAAggggAAChQuoIYgCnQp66rnmF/Bbe67a1tpeW3OATVjVx9a8r1ncs6cmlrL+bdbYyf032lfOOsBatejgApqDbNKkdd6cItoyPJxZz549Tb8n/J6AOrae+13btb4CoJqPJJyUV80dodStW7fw27xGoCYFCIDWZLWRaQQKFyhmzJZi1i0kBwq+arKvbEnj3CgwesQRRxQVvMu2v0KX6y7mIYccYtOnT8+6yV577eV1DVEXkUoktcgstJWq6k2tfNV6s9CkrjbvvPNO5Oq6CHrhhRfs6KOPrsogqO6SqwuPBmynpWpkFbIQAQQQQAABBBBAoJEFdH2qYap0ba4//dbRXAvBRhfv7Wzmurb3slfW9LY5GzrnzWE/1xntHDf/UI/3xluLXeu97ujN9+rttmvm9doaPXq0TZgwIXI/uk5WkFPd3NXzTr8Xpk2b5s3+rvc0uZFSuKGKls2cOdNbrt5wxfzG0LYkBKpVgABotdYM+UKgRALZZvWL2n0x60ZtH1ymIJW+YPMl3QHVl7JaMDZm6tevnxdA05f7tm3b0g6tFpBqmVqpLvrKTDhPaRmMeKG7uYVenOhOb7bgp79rXcBNmTLFjjvuuKoJMirf8+bNq5sMSkFqzU554IEHmuqMhAACCCCAAAIIIIBAMQJq6ahGG37gUo0f/JvtCgzqelN/Suo1pff0WkFNva8/Pdd72lcw2Onno3nLVjZ9xb42aXUvm7p2f9uZyt3Aok2zbXZ8/2122cntbegHndGeeWaHbdnixgj9oGWmP/yVhvZSd/Yt7k2VI9yVXRMfKWmIKzWw0Dqa+0GzuvuBz+BvQJVvxowZ3u8zWTDXgF+LPMZBgABoHGqRMiCQQ0B3/dTdu5AU7jZRyDbZ1lFg0+9ukW0df/nbb7/d6AFQHVtd8DUWqe7Mbt682buYUSAtV+BTFzUK2OpPF0pKGmOnV69eXhn8CyTvjQb8U+xg48Wsv2DBgoJypvLJRhdWlUwyf/31103nSTDpQlNjFqk7jy7iVAckBBBAAAEEEEAAAQSCAgrq6XeJHhXUU28n/en3ih6jgpbB7ev7fNm2dvbyyh42cVVv27JzT2vLbPtq6SIz4/q8bwP2et2GdVhmzZruZQd0OMmtvmcm9uC1vq59/QCo9qffcAqA+mN2Bo+hgKlS69atbcyYMV7vPF0/6xrfbwGqfWtMUi3T70Y1xJCTJkrSXAMkBOIiQAA0LjVJORDIIqAAnwJ0+bpT60sufMcwyy4LWlzIxEv+jrSuLjxK2Z1ZdzQ13o6+9PWFr9aRUftXwFIXDYUEf7WvSZMmeRcHft71qOPoT60qdWHhX0wE1yn2ubrpaz+FDEugMhTa+lP5iLo4ypY/rVvpAOj8+fMzgp/B/Orc0Tizah2qsY7UulcTOpEQQAABBBBAAAEEkiWgG+QK5ikgqOCmrtH1O6iYIKcCgn7gUHr+daU/i7qW6feFWlT6SV3FdewFy7faa2t72MtubM+lW1r7b2d97LPPOjv5gA129dl9bd3KFW5G+GV1+9Z4/eqVpqTfBv7vK5UtmNSTToFL5Vl5Ut785F/36/egWoEec8wxXsMCGfktQHWtrT8/qTHI8OHDczYK8dflEYFaEuAXYi3VFnlFoB4CCvrp7p3GhvG/5MK70Tqa9MYfEDv8fn1eF3ORUcy6+fKiMqpbe7iLt4KJQ4YMsd69NWZO/ZK6hOvOaLakCyx1KTnyyCMjg63ZtotarjrRLI2zZs2KejttWbaAny7CdLdb+1Ld6rnyV4y37pRXMulCUxd/hSS1WJWXLgDHjh1bNwtmIduyDgIIIIAAAggggEDtCij4p8k9Fy5c6I0VX2hJFLhUoNQPbqpRwaBBg+yVV16pu2ZWF3F1JfeDidq3flNoIlKlbbua2ktLO9kL73a1qas0Jn/uWdx7tN1uB++70D4xcpftWLcnvzvea1t3PPVIUyBTLTUViNS1vCYi8icl8vPqHdz9Ewx4qlHAuHHjvJ5tyq/KpgCu36BBgVTNvzB+/HjvPZVNv5MU9FVrTzWeUcMQHZOEQNwECIDGrUYpDwIRAvoyO/bYY73xXNRlIph0h08TAumOYCmTvlwLTRq3phRfsgp+avIedWcPJ7Wk1MQ/ek+B0GKTxsvRjPb5kgKkulgpxezxAwYM8O5g69jZki7SBg8enPa2Ap26INPA634AUxc+Cojqr5gUvKAqZrtSrSvP8EVevn3rgnHixIl2/PHHexdz+dbnfQQQQAABBBBAAIHaFVALT/XS8ltGqmebroH1+0K/M4K/DfzxMv11/MYNeq1eVWrQMHnyZC8YqWtxv5u8ri8VKFTDA11nvzl7js3b2MnefH+I/XtRO9u+O/e4nm33TtnQNu/Y4Z0X20fHdHGzs8+2Ds33t//P3ntAyVGd6fufEgoI5RwHBZSzhCJCIphgGREccML2j/XBBoxtjDHGZo9Z/+1dZ+/aLAbDYsAYbIIJJichlHNEGQnlHFAOI/3rucNtalodqmd6Zrp73u+cVndX3aq69dSo69Z7v3BmsD+EW8RNcttjPNN45wXOiecqhElE2ER1Avz5cb6cz7Rp01zuTl+PAacKn6aL7RFJEUbZHx6h2XSCyd+/IvW8OhCQAFodrrLOUQQCAghZhGdz08NTDiGMm2kmQmUmILmB+4FFuu0IW86GcZP3A4Bk+2OAwQxoq1atkjVJuJwCPFGNttkQQBnEcM0YZBGWEhYvWUe4S58+fRxn3zcGMwx64r19MxUR/f6ihOD7thXxjphbFmM7rnVZxO6yHE/biIAIiIAIiIAIiIAIVD4Bnm0Y+/KO5yTPNjgBIOrh6UhkGM8HCJ+ImF4kPe+885zTgo806t69u6sPMGnSJCeeIg4yjqT9m2++6U4MQbRmk272+o569vaGVrbveEl+zmRnXbPGSevZaIeNbrvNfviF/rZs6Z4gUmlX0NeSvJqIqwiqjFkRLnFqYIyP5ybnwniWsTjnhICJtyrnxrkS9dSzZ0+3HOcLjOcP9sPr7bffdss4D5xhiI4j7N07SCDm8pwh8dNh0j/VhIAE0GpyoXWaIuAJcBPkVdFGOAU3cQYdqQxhllnJ8hoDG3/zT7cvBhmZCqB+sJRu36zPpG26/THYYfDFoIzBDAMe2CLi8h42RE5mv+PFz3CbTD9TMIljMxirCvM5l8pybAZ4EkDLQk7biIAIiIAIiIAIiEB+ECDCi/ExY2Oi2iYFAqZ3IiAsnkgqxpN4OpIvnucADHGRMHYvgOJE0KNHDycW4gXqnQfYdv/xM1z19t+t6mird7N1Z/5Jar3amH2y52Fr+eHb1rDOcXf8M2r3d16c64JUTRyLKDxyeiJKYpwDYeiEuvNM458n/FgYITQcjcZ5MNZF9PQFThFNOXcfKu/3SwqssLFN3759JX6GoehztSAgAbRaXGadpAhUDQGETcS4lStXJuwAM7HkauRmX17zg4co+0FIxJvSh4JE2QZv1qiWyX6j7tMPiFK1p0p6WT0mk+2XwRaDs0yKLCXbV1mWlyc1A7P8mV7nsvRR24iACIiACIiACIiACFQ+ATwlvcBJzQPyf5LrHmETMdA7YhQVFTnHAS9q0tNly5aVmignTRiCqW9z+NhJe3r2AXthyRn21upLLSjXmvIEm9c7YgObbLTLu++1z102NOhHPXvhhRPBu7l9EoHHsw+G2EluT1I2+eck/6yBAwkCKH3BcBahT4iYvBNlh3PCwoULXXEnRF1v4doBtENkRUjleQzHCTxBEVgpiCQTgepIQAJodbzqOmcRqEQC3MS50TIg8bOp3PzJY8NgxN/sy9ulTLweGRgxgIj3oEzVBwTA8Kxrurap1lfUuqj9y/T4DNKqyhj08bcS1bs33E9mwKvKczXcD30WAREQAREQAREQARHIPgHv6UhoOOmi1q9f7w5CWDc5QH3VdIr6YP55gecPBEleGOHgTPrjULF8Z32bsqXIZu3saEdmp3bSqFvzuF3at7Z9enCQa3Tv4mC8utnq1qjr9skYlON4QRXPT8LdMZwliEbDWcRXX6c9fUacbN++vfPupC3CJw4ORLsRxUdVeJ5hKNS0ePFid04chzB51iN6sr1ETujJRKA0AQmgpXnomwiIQAUQQDysaA/CTML6GXRk6nXKTDIztOE8nIlQMXghN2dVWEUJlZkIxRVx3syQM4DN1LvVhwFVRJ+0TxEQAREQAREQAREQgYojwLiWsR+OC4y/iezhRfg4YqCP9KEHOAGEHQEIa0dY9FFZ/jnBj/8RC9kPoie2/ciZNn1zJ/v/HmphWw60dcuS/RMM9W1g2yPWp94yG9pik336ygmu6Zw5JR6iPnoKhw/ET54NOAcEUD+mZoIfI5c/k/z+3F555RX3zBSObCNPKMZ+2I4CTfTdC6tES5177rlOxHUN9Y8IiEBSAhJAk6LRChEQgXwi4Gd2o/SZGVcGEZkYM8nkFSLcJJXh8VpVM65+UJeqf2VZx0xyVRqz8uRtoshVeHCbrk9FRUXpmmRtPR4FDNLJ08TgNNO/r6x1RDsSAREQAREQAREQgTwlgNBJSifEQu+dWZZTYRIckdAbAiPh336MjqfloRN17PW1zez599rYqv0tfNOk763qHbDLehy0Wy5rbYd3bw2eCUq8TQkxZ+wXFi0XLVoU8/bEO5WxNKHqPtzdV3sn7J6+IdTSN/rsBU/fEdYhoPIi5N8b59OlSxcX7q9xp6eidxFITUACaGo+WisCIpAnBBAoEbwYMKUyBggkOC+LsX8GIeQT8nl5/H4IPWEW14e2+OWV+c4AKzwwysaxEfQ4t8o2BqY+TQHXjJl78sUy48819uFCyfqFII7XbkUbvMm3RF5Zbwi2DEhdpdDg70UmAiIgAiIgAiIgAiKQmgBCIMU8vfDJ+A/RL4ox5mI78oFiiIMU+ZkxY4YLKydUHAeI5i3b2NK922zW6o6uqFHxqdTjtGb1i+3yPkFO0aNTrH2DfS6tV6uzWtk789a54/APYidFhfD8xOsTEZe0X/48CEcnOoyxIssxhE8mzn3xog4dOrjtvGiLoEp4POfh8+Hv37/f5Q5lXM5xVL3dodQ/IpARAQmgGeFSYxEQgVwmwECHwUFYjIrvL+HU5fFoRFTzOSn9wIYZW5b5sJb4Y1bWd8RXhMF0YfqZ9IfBYmUZg9wNGza4waAfADLIgy2DRwa1CI6Io1xDxFAf/hPuY8eOHV1i+YqeDcdDAa/UeGMATGJ9ZvARbatCQI7vk76LgAiIgAiIgAiIQK4SIMKHXJeM8XA2YDIZgZCxXNFHDg6ME5kQ9ymfGH/zGacEhMRx48a5SWnERcaTiJJEbzFWW7Wztr30+F57I/D43HNoZEoMdWufsp5nbbHhLTbYJX1r2ckTVJIv8SZl3xQuYpxKX+gv41PvtdmzZ0+Xi3Py5MluHf2nf+Qm9c8NLKO934bO+NylrGMSvXfv3rHwfd9ZvFp5yURABMpOQAJo2dlpSxEQgRwjgNA0atQoF2LCQMjn9aGbzJ4Snt6iRfoQl3SnRah5ZXgXputH/HpmggcNGmRz586NX1Xm78w+V4YxqCWn0ebNm0sdjoElCe59kvtSK4MvDD59+gNmyxFLK2NwSE7SdOkQCIXCWxjRXSYCIiACIiACIiACIlCaAJPZeFCGxUAm8hE/McaHjA15LwqEUF6TJk1y6xjnE9XFWIttGP9SCR4xlfZvz1xh+5qeZw8sbmFbD5VUX3cbJvwn2H/DPTay1SYb3Xab1Thx0ImvWzeXeKAy9keYJLeo7yvjO4TQ5cuXu+MRNUW/mSDn+Bjv4crsfplbGfyD0Mu+GcPzjIInKONZmQiIQMUQkABaMVy1VxEQgSoiwKwxVRG7d+9u5OTBQ5BBBQOMyjSO7Wd6CV8hVKUyjIETnqiIc4QSlccYVPqwm/LsJ8q2eEzGi59RtmPmHzGSHKEMPCvLyOHkB7epjrkuCNfn71FhSqkoaZ0IiIAIiIAIiEB1I4Dn5KxZs5ynJOfOGB5REScGhEU8PBFIvUMDYeQUDMIY17Ocl//MNlt37rcPag6yF5aeYe/tI4qpRvBKPgYnr+fgZoHo2Xq9FbWoXTJ2P8ERSsTLkk/mvDh9/k6/jHB9b3hu8swRL3b69f4dsZNnAnKAIuYykS8TARGoPAKV97RYeeekI4mACIiAm7WlwmNlG4MvZrLjc3EiJPbv379SvBMJWycdAIPKshhCIqE3lZXPFBGTxPBlNdIeMFhGaKwMQ/j0OaaiHI+2DNplIiACIiACIiACIlDdCTBBT05OwsnDhhcnIiNOBIih5513XqnCQUQKeYcCvDGZPCdCqG+//vb4W+/brJ0dbe7sVnbsZOrc9Q1qHbNBzbfa2PbbrcMZm2JdCFKIprRUE9/hdYihfOed/rZp08aNqeXZmRKvVopApRCQAFopmHUQERCB6kCAkGfyAiXKwYkgSj4gckL6kO2KZIIQG9UYoFHAiX4zUENArUxvSgTC8MAxar/D7RgAV5YASi4nQvOjms9VFbW92omACIiACIiACIhAvhBgDHfs2DHnJYmHIwInoeC8mKT2npp4SPqcnf7cvFjId8aeiJ8YAihjLZ/rHU9JH9nFegp/7iluas+vbG3z5rSxHQfasTip1axx0no22mHDW26wgU03W+2aiYsrEUVFnnnefQ53+sJ50Vf6w4v+eDGWc6eND2PnPOg7y9hGJgIikDsEJIDmzrVQT0RABPKYAKIYydsTiZ/+tFjH7PVFF13kBlJ+eUW8p+pH/PEYuDKTXlWDNJ/nKb5fmXwnRMrPtmeyXVna+kFwVNG2qotjleUctY0IiIAIiIAIiIAIJCOACEjRUYr34ACACFgW82MpxqCMj8mnSfog9j9lyhQndLJfcu/jHbrjgNn8XWfbfz/cyFbuHJv2kB2Cyu3nBsWMhrdYbw3rHI+1ZyznX4iZiJ4UTSJtVTbMi6fZ2Jf2IQIikD0CEkCzx1J7EgERqMYEGABGGfzRhuTo3bp1q1BaflY6ykFIFVBV4if9y4a3KQPoPXv2xHKWIqqSG5QZeFgwsM3WObIfPA8Y8Eex+MJbeOf6hwUG3XjcZnK9ohxTbURABERABERABEQgWwQYZzG2YgxLHk48O8tqjKPYH+/kwiQUnu8Inn587L1B8aI8frKmTV7XwF5ZN8bmbGtmwZYpD9264Qkb0nyzDW280to1POJEzcOHawXep8ddiijEVMRPmQiIQPUjIAG0+l1znbEIiEAFEIgqhnFoEqQjmJITiIFeRXgIsm8GjVE8Qdu3b18BRKLvEjExGzZjxgyXYoB8oj5Jvt8vYUnkRWV2PxtGka0o15zrQBJ/jLCv+fPnJ8wfymC8X79+WRGDs3F+2ocIiIAIiIAIiIAIkMKJPOuIlInS/yQbazK29Y4BCJ14ROLVyefGjRu7SWpET7xI/T6I5uFYWM2atWzdwUY2Y20Hm72rgx0pTi1YNqhz0vo02mQXnr3XbrqqX5CSamNQrf1gMLYaaE2bNrW3337bHUfip/6mRaB6E5AAWr2vv85eBEQgSwT8IC/K7hjwMXPOi5n04cOHxzwXo2wfpQ0VMXv27Jm2GiUJ2bt27ep2Sb8QDnkxy8/sOMWbKOBTESKtPw+8MzlOfOEovz7qO2kIpk6dmlD05XxIUYAI6s836n4TtcNrE/E6VfEm+FHNFCP/FTlg6UciQxBn4D969Gg3QE/URstEQAREQAREQAREoDIIIHZS1JPxSSrzE+2M44jEYSyJ2Dlq1Ch755133HeWIX5ifP7wo2pDTIAjgPoq7nh9rt5ebAv29rJ3t3Sw3ccapDp04Ad6yno23Wtfv6CptT4y2/bu3Gq9evUK9rkzED93uEllxmu+WjvFPeX5mRKpVopAwROQAFrwl1gnKAIiUBkEGLyVxRBO8VwcP368S55eln0k2wYvRYS3NWvWJGzCIBDPSPKS8o4AGR/SxIw/OZcGDRqUNe/JRJ0ZPHiwEwgzEZIT7ccPxBOtY9mSJUtc+Dqia3ktXV8RZGFPgSmqnSYTP30/4L9q1Srr0aOHX6R3ERABERABERABEahUAoiH8+bNc5Er4QMjcjJOZHzDGHPDhg2xNkVFRbGJbMTTuXPnOrGTKBg/tkTwRAj1hTrxxuTznoMnbcHuTjZrVydb9WH6qKAOZx2xwY3ftxEt11vnVvWtqEWRLViw1QmeHIMJb4wJfD5zDFINaXwVvpr6LALVk4AE0Op53XXWIiACWSZADqP4sOuoh2AgSdJ3RMZsGx6Pbdu2tbVr17qZeQaehBr5KpzMvKcztmEAOXLkSJevMl37sqxnYDp27FgXIh7fJ58rqiz7TbQNXptDhw5NtCryMvqYziuCnXEswuA3bdoUad9cJ6rZc84yERABERABERABEagMAoiUGzdudKJmsglbH6mDqNm7d2/bvHlzrGsLFy50n8l7jieoFz0Zh86aNcuFzxNNRBSO+36yhr3+3gl7c8Nom7qhkRWfqhnbV6IPTeodtwGNN9joVh/Y2U2OxMLr9+49GoifC9wm9Gv69OnuWORYZ0zFxDiT/CNGjJD3ZyKwWiYC1YyABNBqdsF1uiIgAhVDoEOHDs5T8tChQ2U6AIPIgQMHVojwxWw4L2zZsmWun2XpJINbKnRWlDiHCDpmzBg3U88gG+GVEH08LQnDypZFyd2Z7lhRxE+/j2QeuH59+B2P3YMHDxqFqcJGeDyiKywY1Lds2dK9h9voswiIgAiIgAiIgAhkQoDxFtExiJapjLQ/vjo74iYRK4xXiCYix+b27dvd5jgEEGXjUwQx/iTnJ8dBWN1R3MqeXt/XZu7oaAdPnJHqkFanZrH1abzNhrfcYBf0OGU7t29z7YOhkDOfO9TvJHwOTPRjjM8RYcsaqeX3rXcREIHCICABtDCuo85CBESgigmQ74hcnuSgTBcanairiH1sV9YBGgM9tmd2HYEskTFgJZy9rIa4iwgXX9W8rPtLth0DZV7efK4o/72872W5PvHHzKRPPCBkYuEiAwiiCM/x3sWI0OSyIryeBwCZCIiACIiACIiACEQlQJg7Y8L4SWHycDImZSKWMQhRShjjS8ZmPkqHgp4Y3pX9+/e3N954w31nPEzBSS+A8v1Ireb2xuamNm1RkW17tVbQriT3vNvgtH9O2dkN99jwFhtsWIuNVq9WSe7QnSX6aqnW8WmPEGMZA9N3RFgioHwhylIb6osIiEC1JSABtNpeep24CIhAtgk0atTIxo0b5woP4dEZPzBLd7yyCFmEVq9YsSIWasQx6Adh1PHV3QltKq8h/FW0ABrfR84H74LwzH58m0y+JxOIM9lHJm0Z/GdiPExgCLXvvvuu87CI354iAlRKRdQmrKssfzvx+9R3ERABERABERCBwiawd+9eN7HKeyJD/MQopMlYg1yeGIInkTpYx44dXag8nxnrstwXMmKsRm7PI8W1bdGeNvb4A8U25f1eQcvUqX3annnYPjc8yOd5fIbVObbdRS7t3l0c9IGjfGx+op+xHONDXoybED0re3z3ca/0SQREIF8ISADNlyulfoqACOQFAQZhQ4YMcdW/8dojiXwUY/CYaWVKCusggsUbIiWFjRiEEvbjzSed99/L8s5guCqM9ABUUQ97R5a1H4RnldfwKEj28BC/b4oGEPblw7Hi14e/Iy4zuMe4vum8R/Hg4G+A0DSZCIiACIiACIiACCQjwLiU8WF4gh6xkmJGjO+YOPc5yxmDUOgIw5uSMY8fkzApTUg733nhJYoAefjIUZsa1N18ZE0Ne27BpXb8VOoJ4Mb1TlrfRhvs3ObrrVuj3dayQUtXvZ0oF8bT4TEnfSDSSiJnsqur5SIgAlEIKG4uCiW1EQEREIEMCRBCxAw5A7YoRjhzJkYOykTiZ3gf5J5kUJtNY6a9KozjMvBN5k2JgBxF2MRTMhtiIdc2qtGWYgHpjAF/r154SZgLO4vqsZtJjtF0fdB6ERABERABERCBwiOwbds2V9AS8dOPpciz7oVGcnVSINJPxhP+7sPYmXwOj3uIcioKqr5jiJRTl2yzp9Z0sx/Nv8R+MX+wPTW/ZlLxs1aNk9anyVb77pCVNu27R+yLXRY68ZN9MamLsU8/BmJsRPV2csRL/HR49I8IiEA5CMgDtBzwtKkIiIAIpCOA5yJhzKk8Fxl0ZiKAMjCkanwUo+iRH7TitcgAuKzGINkXUyrrPsq6HeHg5MJMxhEPBDwtyU+VzNMV8ZPBvQ/hKmtf2I4iRGFPiWT76tKli+sT/aKPpCtIZAzwBw8e7M6B9XhWRDU8S8nPyoOMTAREQAREQAREQAQ8AULaGQuGJ839WIqxgx+XMMbDGEswjsKjkwKMjJ1It8MQ0WQIAABAAElEQVQErZ9UJ29orTPb2KStZ9u0HZ1t08yP87b748a/dz5rv1094KS1PTzVzqx93IXMz5rxgRM7GacyTqIYU9hwIhg1alRMlA2v02cREAERKAsBCaBloaZtREAERCAiAQZ0zFqTQ4mBZLwhoiGSZpLDkYEoIdVRjHaExONBSSVMP5sfZdv4NiS5z6Sf8duX5zviJwJiKmNgPmjQINeOSqXhYketWrVyXphcD4wHAlIE4OGARwHhXN4jItUxwus4FsKl91IIr+MzonY4BQH5tAhxp3IqDw94YeApjOcqOVvD3rU+B1f8PpN990UKkq3XchEQAREQAREQgepFgDHOtGnT3DiQM2cMR0QKQiOfu3btamvXrnVjIsYyjJWY4EUAZbzEuNWHy9OuRu36Nm97E1fBfcnMNnYqTV7PlvWP2oAm621Uyw+sa8sabhL7cCB+Mt6ib94Yv3lxlWWMregbfeWzTAREQASyRUACaLZIaj8iIAIikIQAM9jjx4933pd49iFWMdNOdcqw6JVk89MWR8klGd6I9hyHwSyiHIPYTIxBMmJfmzZtMtksa23xUCDcKopxbueff75L3o9gipcD3gw+pIuBPN6zeEJ4Dwj2ixBJaDxCZNTBNgN48r127tzZDdwRptkW1uTUQlSNNwRQX0SK4ycTXTP15vSeG/HH03cREAEREAEREIHqR4Bx04IFC2JV3CFABJGPBGLcgMDI+MhXdJ8/f77LYU9b8oWW5PrcY+sPN7fHZ7W1Obs6BMWN6rA6qdWrddz6BSHuw1tusJ6NdgTjopKmYR+A8PgrvCP6gjcoY1U8UGUiIAIikG0CEkCzTVT7EwEREIEEBBARETx5lde8mBd1P76oDu3xSGQAjIdkIkOsxSMS0RRRkJD3oiDPU1XmXcJbMqqRpB/vSfoeP3hG/Jw+fbrzvozfH9sgjOL1MGzYsMgiKPsJi5rx+031PZn4yTY8dHDdwl6syfbFNQpf42TttFwEREAEREAERKAwCSAqIloifJJLM1EkCeM/n2eTyWW8MH1qI8ZNbEOUCpO4s1fsspc2tbGpW4fa7mOpU+zUCHxBz2m004a3CCJxmm+yM2qezAgy4yjE2EQTxxntSI1FQAREIA0BCaBpAGm1CIiACOQaATw5/UA1Xd9ot337dhf6zsCXPKDkpWSGnUJKhMf75YTIE/6Ua5ZpeDftOe94I89VOjGVhwe8Qwm9qkpDMCffFqH/6cwXTkrXTutFoNAJ8FvGA3z8JBH571566SWbNGmSe8C++uqrbeLEiRlNdBQ6O52fCIhA/hJg7ELV9nTpkYhaYYJ76dKlbuzHbyJRM3iDsu2JWmfZa2ub2tL3gvHH1nppgbSp/6Gd22KjjWix3hqf8XFIu9+QqBgmaJlEJ7LF52CvW7euC8FnOQIs32UiIAIiUBkETn9CrIyj6hgiIAIiIAJlJuDzNvnE9al2hEcAD/9hYztE0FQ5PRES8KYMC6QUGYoaHh4+Xnk/Z+J9Sv8SDaThELVaOt4P8KmKcw2zwvOW/FvJ+k3/BgwYEPPeCG+rzyJQnQjg8XTffffZI4884t4/8YlPxE7/jTfesEsvvbRUyotHH33UrrvuOnv44Ydj7fRBBERABPKRAGM6XxiT8SHRLl7QZDxEnnEmvDGETwRPxnebNm1yYuiMWXNtV/2B9vii47ZwT1srPlUzJYZGdY7Y4GabbFSrD6x9g/2xtnhvtmvXzhWI9OO2qh5HxTqnDyIgAiLwEQEJoPpTqHQCt4+oZd/u8o4V/6pXpR9bBxSBQiHQ/ZRZxyB0KVkepbTnOd/s6HO17Yy6Z5zW9GTxSTsaVF2vFQyiw1ksD340k1+rdq3TtqnIBa2Dc70gCNUK3tIaYeWnVvzAiuNaFgfnNDbweohqJxbWD7wTPkpcFXWjCmjHr+Q5J4pdDq/i4Hpg9IrzxMut5oqap52ra6R/KozAL9vssF1dUj8gVtjBtePTCBDCicCJ9xMWnjCgqMa1116b8HcSsZQJhFtvvfW0fWqBCIiACOQiASal8fbknTznvMKh7r5gEV6XeHQifpLDnTEDOdLx/pw1a5Yrvjlz1QGbsaOjzZrb0Q6eOH0sGD7/OjWLrS95PYMQ9z5NtpkfHiF4kj+d6KJEkTfhfeizCIiACOQCAQmguXAVcrwP//M//2N/+MMfYlUAy9NdKgx+cUwNa31GUMF697ry7ErbikC1J5A+OCkNIqKVEhRWR9qpn2zT6Bpisj1kvBzBL3X2qbhdHor7HnxFsj3z9MXJl5weyZW8bQWvoe+VKzlX8Anl+e5bBSOnJuX+z5fnEHKo+3feeWdM/MTbCA8ob3/6059s165d7uvAgQPtj3/8oxMAbr/9dps3b57xfskll1ifPn38JnrPIQLZHn9yauR5lolAvhFAzFy0aJFt3bo1adf5/SN6B/N/5wikWFFRkRNAdx2tb68vbW63T6tnGz8c59Yl/+eUdWm420a03GhDmm+0erVOxJoSyj5y5MhYSHtshT6IgAiIQI4TkACa4xcoF7pHBcHVq1dnsSv6s8siTO1KBERABERABKolATzg77nnHnfugwcPtgceeMB5O3kYTzzxhP9o//d//xdbR1g8ggBeVFOmTJEAGqOUWx+yP/40o/CLTATygQCeneuCgpV4svNb5c0Lnf4db09+C31EEL+FH3zwgZv8YQJo3uKVtvbEOfZ/74+zhTsaBbthWjm5taq334Y232wjW35gzeoGDishw8uTFEHdu3eXx2eIiz6KgAjkDwEpUflzraqsp3/+85/trrvuis0qlqcjn/rUp4LNV5ZnF9pWBERABERABERABFy4OyHw2B133BETOPlOXjwKmmE9evQotY5cdZ/+9KedKIonqCw3CWR7/Pnee+9Z27Ztc/Nk1SsRCBHYtm2bzZ8/31VpDy12H72Xp3/v16+fKzD07rvvuvXk+RwwcLBNfX+VzdzZ0ebPam/HnWNo4/hdxb6fWfuYDWy22YW4dz1rd2w5HxBa27RpY507d7aWLVuW8rIv1VBfREAERCAPCEgAzYOLVNVdJG/M2WefnZVukIz7p1NP2MrmFxihaTIREIHsE6By+I6g8nsUa9DgTBs1epRrSnjV9mDQHcUaN2liw4YNi9K0QtogepwIqr3zm1I7yIUZxTYERQCiFI7q27evtUnykFwceGRsC9jijXEy8LioH1Q1bRU8EJzZsGGULqhNBAKHgpC9mUGOMlinsy5duzpvlHTtsrn+e0HOyGdWPGuXZ3On2leZCCBoYYxTLr744lL7ePnll2PfL7vssthn/wEPUAyRQZabBLI9/szNs1SvqiOB48H4BcNrE7GTgod8JoUH4es7d+50631RoybBmGv48OH25ptvupyfjRo1inmFsm3jxiXi5vajjW36+nZ228ymtvfoaLePZP/UqnHSBrbca0OarbO+Z220li2a2u7dH4ufjK/OOecc69ixo8s5nmw/Wi4CIiAC+URAAmg+Xa0C6eu+wFlj+/EGVqNZdkTVAsGi0xCBrBGo2+aIHfowdYiTP1ijVm1i/xcPnrHFDkXMbVirzlmx7fy+KvM9YjdLdalT8Juz61SjWDXUUis/+tI1ENTa9umbaJXLvUVIpvc4c412BxVQN+53k0QIp+H8gwl3ooVpCSxZNdP2125uFmGEsmRT8OBXdJa1aNEi7X6z1WBHcUM7Gl9lK1s7134yIuArG7dv394QCMIWFkDJ8xlvXoAIFxCJb6PvIiACIpAtAuTv5Ddrx44dpQoXpdq/L2qEKDp37ly3HV6Yo0aNshdeeMHVZ5i1eJ3tWts2qOI+zjYfTu7l6Y/TtclBG9T4fRse5PZsUOuYXxwTP/H47NWrlzEe0pgmhkcfREAECoRAhMeLAjlTnYYIiIAIVBMCiAErV0ZLNdGhQ4cYlfr1k5Y+irXxH/DMoZpovXplkSL9Xir/nWqozZo1s1WrVrnqqb4HVDAlTBZ2iQwPjZkzZyZa5ZZRXRVhtCq9YpN2Lo9WHDt2LGWRh0SnwrVMJYDyd8q1wZsl3/5eE52vln1MwP9+bY/zeKdgyOTJk11Drvn555//8UYffVq+fLn71KlTp9PWaYEIiIAIZIsA9585c+bEvDoz3S/3LvbhvUK53x0OHEiX7O9s72xsZUv3trZTafJ6NjvjkI0/e799+5Otbfvqxc7rdMiQIbZ3716XSsTnE0XwHDt2bMyjNNO+qr0IiIAI5DoBCaC5foXUPxEQgWpBgJDqTZs2uTAoBqCEM/FwXxbBhtAo0lYgyqUyBtHt2rWLNSHHk/eoii1M8oFB86uvvuqEJyoox3tfJdksJxaTx4oXxTAQ3Hi4SCX+EpYWJUx28+bNxivMNCdOOI864SvWZtJl/1AYvw3/n0h5sH9/4KX7kSF0E9LnhTO/XO/5SaBbt26u44jcFDa66KKL3Pe///3vboKGL+PHjz/t/ze/jc8++6xrm60UP25n+kcEREAEQgT4bWIyhkkZihUx9iA3MeOK1q1bu7Ea4mjYE71OkNaHvJ5EnOAByjgQj8933plsq/Y1tn+82sDm7DplB4/1Dx3p9I/1axfbZX1qWPdaC61j7fVBLk+zDxY3it0TGXdSLAkjnyjHPffccyV+no5SS0RABAqIgATQArqYOhUREIH8I8DgdvHixbYuqPQZto0bNxoeSoRV+1x14fXpPrMdA+xkgibiJwNdZv29IYBSHGTPnj1+Udp3xCcS77MvBvP5ZA2C/J280hlha3hfRDGuowTQKKQStwn/PSZucfpS/g/x8Eh1Wm/8n/IFcPwy3hFDCSPkb5wHTFl+E+AaEqq5bNkyu/766+0Pf/iDm0S6NcjT6u3LX/6y/+jep0+fbl/60pfMh8B/5StfKbVeX0RABEQgGwQQFWcF+awRPxlbkcNzxowZbmxGtAkemBRh4/7FeiYAmZQlwoa8m1R/J1x+5dYTNunNU/bs4ots55HUUTc1Al/QHo122LktNthlvYvtvFHnBhEvDY10yUyuh6vJEz3hjeJgjBujjIn8NnoXAREQgXwk8PHTQj72Xn0WAREQgTwngGchYmciQ8CkoBGWqQjKQJdwbzzdEEHJH4VQhAccyxDp4sUmvhPCPW3aNCciJOpTomXsd/bs2c77qiweq4n2mUvL8HaNapm0jbrP6tSuYVBMir9d/qaiGsJnWPzk7z2R+BneH+vxslb4c5hK/n3mN+vf//3f7fOf/7z7nZs4cWKpkyCU85prroktGzduXOBF9U7s+xVXXGEDBw6MfdcHERABEcgWAcZ2TLYxLhoxYoT7zBiB7/zuIHjShnseYy+KuvEdr9Fl72+1yVs72kvLe9j7B4Kc2Gmsbf0P7eIu++zfLmhiS+fMdPfQA/vOcAKqj4LA+9RHBnHPRHjl2AizrVq1SnMErRYBERCBwiAgAbQwrqPOQgREIA8JkLcumfgZPp0lS5YYs/OEamdqJMvnFdUIBUc0IIco3gdRPR8Ra9esWWOEwxeaZSLGZdK20Dhl43x4KMMzhr+9qBZ+cMPjxud2TLc97fCyiZ8ISLed1ucWgWuvvdb9Tn3961+PeXXSQzxDCXMn7NSbr5TM9wkTJthf//pXv0rvIiACIpBVAj6yh1QdeIH6iBzSbnCv27Jlizse9zzGXidO1bTFe1rbzJ0dbdGsVlYcfE9lzRsU2yf7FFuno9Osbf197reudaOxtiXIc050Dt6kTGh78+In3xE/OSbiZ/h30bfVuwiIgAgUKgEJoIV6ZXVeIiACOU/A515K11HERYRSKnJWhpEHCiGzd+/ezgM1aj8RdAtRAD3zzDMjY8+kbeSdVrOG/N3xtxRFfEe8JKenNzxdeNCMYrSjPTlzZflNgDD2T3ziE867E9GBvJ94VOHdFDa8rhDJP/e5zzmv0fj14bb6LAIiIAJlJUDUze7du93mTGKHjeXce3w4+s5T7ezfXzB7cm5/23+sVrjpaZ/PqFlsfZtsteFBiPunhjS0/v362qJFzQLPzn1O8HzzzTfdbxwbUvARD1R+88JGmDveoF26dCkVPRFuo88iIAIiUKgEJIAW6pXVeYmACOQ8AQbIUS2TtlH3ma4d4hLia1SLIlhF3VcutcP7dunSpZHCspNVkc+l88n1vhAeOGbMGJc7zYfuJeszglbYe4XQwUyM9hJAMyGWu235f4o3aCq7++67U63WOhEQAREoN4HVq1e7cHa/I6J3eHnBc9u2bbZsw35b9GGRvbPpQts+s+FHTZOJn6es61m7bWLf43Z57+O2Ysk8137/hyXjMz8+RPD0oisNwp85Pp6nRD0oz+dHuPUmAiJQLQlIAK2Wl10nLQIikAsEMgmXjp/Bz7T/DLh9lXkS7FO1nfyH5ARNZeHw0VTtWJdJ23T7yqX1hIl1797dVRRP1S+8P/GokJWfALlA8eLD85nUCjw4hv8P8KCHp2jz5qVzo2X6N5hp+/KfmfYgAiIgAiJQqATI4xkuLsS44KKLLnKn++Jr79jU9WfZgv3dbNEOxl4fF6FMxKNL85N2Rf9T1nr/JGtU64DVrVPX2rYYaTs+EjoJcyfEHaGTkHpf2I0oHl6HDh1yuyVKgpQgMhEQAREQATMJoPorEAEREIEqIoDIEzVcl7ZlMQbEVL1GAA0bA2e8FHr27Gk9evQIryr1mfyh6QrK+A3CuRj9skJ5hxEeruuC8NpExvWhyAEPIbLsEMADGW8VXoifBw4ccHnL8F5Jlg8Xb04e/PyDYKqe0E7en6kI5e86KicjQvDCi/jmm292J4OYjpd2IRZry9+rpZ6LQH4T4P5E3mrGVPFRCwcOHrLnZu22t9Y1sxcWnWfHTpZOyxF/5o3rFlu/xutdiPuXL+1tLVq0sNdfPxmImebGIJMmTSo12cxvHUZOT39s7n+8uMdRDBPveJkIiIAIiEAJAT2p6S9BBERABKqIANXY/eA1XRfKElrNoHzWrFkuGX6y/VMIBo9QkvQnstatW7sQYx9ilagNy8ilV8jej4hxAwYMsHbt2jkRlLxapAfAu4NlhJbBUVYxBOCfzluZI/N3SK7cKIWQaKcckBVzvapqr0888YTdcccdFs5bjJewF0B//etf29NPP2033nij/ehHP3ICQVX1VccVARHIbwJUccf7kvuN97bkjBgLbD3S2KZsaWOzgoJGHx6v99GJJhY/a9U4aed3O2mfG1bb+jbaagvmL3Ltp0+f7iZs2DfRCowZKWREcaNUxr2ScQn3OERQmQiIgAiIwMcEJIB+zEKfREAERKBSCeDZxoN6OE9Tog4gTpbFA5TwYTw909myZcvcwDqRVxTC09ChQ23KlClJi9LQZvDgwdUirxQesbxyyXx1WURq0irwt4Jg3rRp01zqZqX1hXQFu3btSjm5wDWknawwCCAKXHfdde53KtUZ4cHNpBO5QOfMmWNPPvmkq4ScahutEwEREAFPgIlPfm/4LUEADdu+Y3Vtwd6OtvBgL1uxPbHYGW4/oP0p61V3qQ1put7aNm9gY3qOCQq5LXdNmLjhPoZnKUZ0CUIok3bh3OyInQidPgKH6AhN7Dlk+kcEREAEEhKQAJoQixaKgAiIQMUTQDg899xznZdmMhG0qKjI5TosS2/8wDndtohm5AdlEJ3IENTOP/98o5Lp5s2bSzUhl2jfvn1Py8VYqpG+VBgB0hPEF2gi3QGhvgjseK1WN89UHv5IR4BXDnzCD4uwwFOZ1A96SKywP8tK3TGhn1R1nz17tjsugsDo0aNdSOgbb7xRqi/8n/D24osvOk/Qhx56yC/SuwiIgAgkJUAalpkzZ7p0LDRiDHf0RE1bdqC9Td/WzpbsaWWn0uT1bFn/sA1uusFGtFxvo/q2DSYtTwX3qePGBObrr7/uvDsZc/Xr188dy6dJQvwMe5ky9uI+RpSOTAREQAREIDoBCaDRWamlCIiACGSdALP1VLxGgMRjkwE2wgyVrRE/44u8ZNIBX3E0yjY+d1SythQCGjZsmPMCZb/e05AQcFnVEMALZfHixUkPjgCOOITInszwEMaTZe/evbFrSugcBbLyWSCk7xRJovgDkwvkb+X/GsWTlKc12V9Dfi7Hm9OLn//v//0/+9WvfuWu84MPPmjxAuj9999v//Zv/2ZXXnmlbdmyxR599FG788475Q2cn5devRaBSiFw5MgRN0YjWoYJtbp169m2U53speX1be7uDnakOPXjdL1ax21g0y02vOVG637WjmCyuUvgRXrI5QwNh6j70Hbu2+T6xIjMYTy4fft2lwub+xhjsfKMDd2O9Y8IiIAIVFMCqX+xqykUnbYIiIAIVCYBvAjI7cQrm8Z+s20MvnMtBDzb55gP+0PQw/MznSHy8IovgkB+WMRTRNSw4W1CiPC6QBQdPnx43ocHI3b60MDweepzYRBAKCCvJ3bJJZfYn//857TCPRMCCKP9+/d3YsYDDzxgv/jFLwoDiM5CBEQgawSYGKaq+9atW90+tx1uaLN3tbcZOzrZnmMNUh6npp20nk122b9d0NTOLzpoC+YuM4RUjPsrnuqE0Ccq2Ofb0ZbP/jse7H369ElaBJD2MhEQAREQgdQEJICm5qO1IiACIpC3BBhg+4FzupNQNex0hCpvPd61CJx4hiTzVsRjOBzanap369cH+cXiqsASHh4vfob3QTjejBkzXOqDfPYEDZ+TPhceAf6O/W/cb37zm7TipyeAd/DEiRPtmWeesZUrV/rFehcBERABR4B77Pz5823fkZq2YE9RIHp2tLUHmqWlU9TkiH1xRF3rdGKOndi/xbrV72atW/SxCy+80KVlIT0N9/j4CB3u9YzZuO8zec19l3YUP8IDlHt4gwapRde0nVMDERABERABkwCqPwIREAERKFAChDFHqTLPQJuwZ1nVEiAMfcWKFS7UjQcfjDxf5GaN9w5GoIxq8W3JI7Zq1aq0m/OAhqcKOTNlIpCLBBYsWOC6hXDQq1evjLqIBygCKHliZSIgAiLgCWzast3+7/WNNnPHYFu8t60Vn0odTdO4zmG7sOsh61VnoXVrUWwXjrkwyOnZ3SZP3uJSGzHhgsBJvnQKX+K5ToQFy/gNwxN0yJAh1qZNG98FvYuACIiACFQQAQmgFQRWuxUBERCBqiZAJXC8/9KJoAzOE1WAr+r+V6fjk68TbxNC08OGKDp37lx3DQcOHOg8Q1gf3y68Tfzn+LYUsopfFr+N/44XjARQT0PvuUYAT2kML6lMPZV93mPlMc61q6r+iEDVEFiw0ezJucXBq4kdOjE8ZSfq1DxhQ1vvsasHnLR6u2dYrZo1XMoYJhiZVOnWrZsbV+GhTm53JmnCRg5P7q+In+RYV6qWMB19FgEREIGKIyABtOLYas8iIAIiUKUECKMiWf68efNiOazCHWI9XlPJqr+H2+pzxRHYs2dPQvEzfESEbCrDdu/e3S2Of5gKt43/HN+Wh7GolknbqPtUOxHIFoEBAwa4Xe3atcuYRAhXeU93DCYWMLyyZCIgAtWTwIY9Zs8uNPtH8HOwdhcMan30SsxjeJHZp/octfrbXrfGDWrbJeddEkxStndiJvdoBFDyhiJq8h0BlBf3YSZs8P6sVauW8ZvlixiS1zPTCZzEvdNSERABERCBdAQkgKYjpPUiIAIikMcEyCdFqBVeoFSZx9uAgTah1YTIM0CXVS0B8hhG8cgkPP7ss892YXN491KRNsp28aJQJg9ambStWoo6enUkgHiJmEA+XKrBU9Aoir3yyiuxKssSQKMQUxsRKBwC+4NaRC8HNQSfnm82NUIGjNb19gcV3DfZjz/f09o1gUNde+21M4yigbt377ZzzjnHCaBEbPCZvMJz5sxxnum09vdRPD4xPNZnzpzpPjMBzf1cJgIiIAIiUDkEJIBWDmcdRQREQASqlACV21W9vUovQcKDI9ykS1HgN6Ttzp07XZ4wiiHwoIUomsoIs4vPH0pBhaiWSduo+1Q7EcgWAVJ3XH311fbkk0/agw8+aD169LDvfe97McEh0XHefvtt+9rXvuZW8f9owoQJiZppmQiIQAERKA7Sak9ZY/bUPLOXAvHz6InUJ3dm7aM2uNlmG95ig3VptNdNNjaq3SHYqGTSmAlk7r9Lly61MWPGxMLdWV63bl23/NixY+4giJ0s8xEVCKdE4PTs2dPdx1P3RGtFQAREQASySUACaDZpal8iIAIiIAIikAEBQuOieHH6XfLg5A2xB1F09erVflGp9xYtWrgUCDxohY2CVzy0HT9+PLw44efOnTsnXK6FIpArBO69916bMmWKbdmyxW6//XYnhlLhfevWra6L/P+i0Ag5dl9++WW33vf95z//uXLcehh6F4ECJLAs+BnA0/Op4LUzTfaX2jWKrXeT7Tai5QY7v+sJq3HqhKvWXlxckpt7+vTpdv755zsPTjw3KWhECpuFCxe6kHfu54S5kzcbr0+Wc//lXuvvt3wnKoN0NorAKcA/OJ2SCIhAzhOQAJrzl0gdFAERqGwCzNKTxJ7QJgat5HKiOmdRUZELP67s/uh4hUuAFAWZWLg9D1LkDiN8jmrtVHunejwPVSxr27ZtrGhS+BjsgwrYPgdieF34M0Ip+5CJQC4TwMv54Ycfdp6g/HbPnj3bvXyf+R0fNGiQ/xp7v/zyy+2WW26JfdcHERCBwiCw7UOz5xaV5PVcvi39OXU+c4/z9BzWYqM1qH3c3TdHj7zcjfeYZMTTc9WqVS6/57vvvutEUO6j5557rpt8IUe3D3MnJQfjRj8xyb2YfJ9MXuIFOnbsWMPzXCYCIiACIlA1BCSAVg13HVUERCBHCaxdu9Ylpg975ZHUngEsouiIESOsUaNGOdr7/O0WvAnvJocWDxxUZm7dunUsh1b+nlnqnpMLDMHSh8albm3WrFmz05qQz5UK8ZmYD4vHQ4WiDPGG2N+vX7/4xfouAjlJ4OKLL3YixR133GF//etfU3pVM5n1i1/8wr785S8nnCDIyRNUp0RABFISOBxEm7+2rMTb8+1VFvwGpGxurRoctUvOOWRdbZ61rHvA5dc+dapkMpHxyDvvvOPESoTO3r172/79+51XOfdqCksifjZt2tSFvxPijucnhkDK5KS/r5J7HSOdDNtI/HQ49I8IiIAIVBkBCaBVhl4HFgERyDUCJKhftChwG0hizOBPmzbNxo8f72bykzTT4gwJ4KFFeGq8CIgnBTmyunXrluEe86s550eIbjrDIzObD0+IoK1atYoVb0B4RozlOBL5010Nrc81AvzdPvLII3brrbe632k8tniRY5fiYeTM5XXFFVfo7zvXLp76IwJlIIDIOXNdSXj788HQ7VBJys2ke6pX67gNbr7VvnZ+I7tsUGObMWO+bdt2wJjwIyoCQ8gknYb3Jh81apRbPmTIEJs8ebITQlnPPZt7NyIovy8UM0T45D4aNoRP9k86GdbLREAEREAEqpaABNCq5a+ji4AI5AgBQocXL16ctjcMjgmH8oPltBuoQUoCiJ9Tp051odvxDXmQIFclebUKuVIzRRO2b99umzdvjkcQ+47wWRF/c3ig8vAmE4FCIYA3dKYe0YVy7joPEagOBFZvN3tmodmT88y27Et9xrVqnLL+LffZwLNW24BmW6xD25bWoWWNIJy9TiB+bnOh67169YrthElXBE6MyRMmxgljr127tvMInTRpkh08eNDl/yQHaNh85BATiHiNEp1B2LtMBERABEQgdwhIAM2da6GeiIAIVCEBwq99CFO6bhDSRHiwZvPTkUq9HtGZPJS8p7I1a9a4HKwU9SlE4+9o6NChzoOEvGHxPAjZRdDRg1QhXn2dkwiIgAiIQDoCuw+a/SuYo6aY0bwN6VqbdW12zIY0WWuDm7xvZ9X52DUU0ZMXgiZGDmEmAr0hXuLZ6XN44tmJAIqxDfdiJm3ZBsEzXNyItD1MaCp3tqepdxEQARHIPQISQHPvmqhHIiACVUCA/E5RjQHvsWPHJEhFBZakHVWaya8axci/WqgCKOePCIoXCpVl8Toh3QK5xzhn8qGGjYcucqXCjgcyeZmE6eizCIiACIhAIRA4FqSnfmtFkNczyBDzepDf80TquVJrE6RnvyKIZB901go7uXu5Q0AI+sGDJ11OzgEDBrgQdbw6qd6OcR/lnhqe0MZ7k+VEZRAKT+ojhM2zzjorll+YHJ9+shIRdeTIkUbaHpkIiIAIiEBuE5AAmtvXR70TARGoJAK+gmfUw4UHy1G3UbvSBAh/j2r+YSVq+3xth1eJ9zZJdA54H/u0AOH15PMkTYC8RMNU9LlQCDz++ON21113VcjpUAle1eArBK12KgJlIjA/8PDE0/PZQPjcdyT1LurXMftEEMH+6cFm53ezwHNzpS1bttyFtiN4cj/917/+5b6TixNjopEcnoSwE87OPTWcZsdHZbz22msuBQ+TkrzChvjJuJF9ETaf6RgyvC99FgEREAERqDwCEkArj7WOJAIikMME8BKIavXr1y8VMhV1O7UrTcBXSS29NPG3TNom3kP+LyX3LOF4iQxhFEH5vPPOs3r16iVqomUikLcEPvzwQyMVRkVYdZlcqQh22qcIZIvA+mA+9Nkgr+c/5pqtSzM3Si2hEUUloueEvmZnfpRmE29Nf4+k4joh6T61kQ959/0lTN3n8OS3hUlEoim8IYJSFJAc5OTJxiOUyAwigHgnL/eYMWOM8aBMBERABEQgfwhIAM2fa6WeioAIVCABKnkS3hQlFJ5qnrLyE4gP7U61x2xWP091nFxdt2vXrtiDXbI+8oA2f/58F4qXrI2Wi0A+EkC8QIyoCAvn/6uI/WufIiACiQl8GHh3vrw08PacZzZ9beI24aXdW5ldNaBE+GyXYM6avJ2EszNGQ/zE+P+NmEnaIiZSvRBK2Dpemz6MfdWqVTZs2LDw4ZzoyQIEUMaH2OzZs50AyjEkfjok+kcEREAE8oqABNC8ulzqrAiIQEURYIDsk9v7AXGiY+EpSoJ8WfkJUNznvffei7Sj6l5UgIezKEY1ebzlKOQgE4FCIXD99dcbL5kIiEB+EzhRbPZu4MyN6PlKcPs/GuT5TGXNghTYn+oXiJ6DgtyeHRO3RNjk3kckBIawSSX3Vq1aubycTHATIUHxI59iBiG0Y8eOMS9QcpL7sHb2wWQ4k4rk4vaTL4TBkxeU/bOtTAREQAREIP8ISADNv2umHouACFQQgWbNmjnvuTlz5sTCpsKHatmypQ0ZMkSJ7sNQyvEZjwq8KHwYWrJdEdJNnq3qbDt37ox8+jykSQCNjEsNRUAEREAEKpjAe1tK8no+E+T13Hkg9cHOCGoJXdCjRPS8qGdQfT34nsgQPpkcJIS9uDhQVj+ytWvXGi9ETsYOTKAigK5cudJ99vk6yd2JoElYO+In+UC9p6cPpW/Xrp3zIGV7vD+x7t27y/vzI9Z6EwEREIF8IyABtIquGDlpylKsgjBIcs9g5KuRiYAIZJcAVbcvuugio0ooA14GxoQ54a2IACrLLoF+/fq5HFt4ZiQyfidHjBjhvDASra8Oy3iwCz/cpTtn8qBxn1B4XjpSWi8C1Y+Axp/V75pX1Rlv/dDsOfJ6Bt6eKxLf4kt1bXDgVImn58QgzL1xmtSa3ONmzJjhIh7YCdE5+/btcyHvTKwyaUh+X3Jns46JVKIjFi1aZBRHIuqHZeQKnTp1quvHzJkzrVevXk4I9Z6eCKBss27dOhdej5jao0egzspEQAREQATykoAE0Eq8bE899ZQ9+OCDNm/ePFdNkBso+WZuuOEGGz16dKSefPWrX3XVDGlMnhuZCIhA9gngNcAAmpesYgnUqlXLhg8fbuvXr3cPGHv37nUH5MGEUDU8LcoyWVSxva7cvcOIv8mohaB4UOOFANqlSxfnAcPDnkwERKB6EtD4s3pe96o468PHzF5dVhLi/s5qnlVS96JTU7MrB5p9NqjiXtQ8cVtEe+5pTJQywcfENPc0noMYK/AshefmSy+95Dw5ETFZzyQ2z1wIo+Qcx/OTiBMKGzH5yjImvckTSo5QPECJAPLG/qdPn+6/uvRHvXv3dvuOLdQHERABERCBvCIgAbQSLhc31BtvvNEeeeSRUkcjvILXY489Zt/+9rftZz/7mTx2ShHSFxEQgepAgAcVLzjzwEEoGqKf7GMCFHTAKzkTw0Nm6dKlLjcaXrQ+7C+TfaitCOQCgYceesi++93vuq4gQEybNs19Ju/fOeecU64u3nHHHcarEE3jz0K8qrl3TsEt22asKxE9/7XE7FAggqayRvXMLg+qt+PtOeLsVC3N3n//fZcrPD4KwjuBIGbioTl06FBXmZ28nXh+ktKI19ixY+3dd991winenKSIQUjlxXomChE/MS+q+h55gZUIICYTfXi8X693ERABERCB/CMgAbQSrtmdd95ZSvxkxpHE3Mxm+of93/3ud/biiy/aK6+84qoNVkK3dAgREIFqRgBBjMIAPBQjhjVp0sSF9ueS2MgDSC71J1f+RBB5CMnzD32Z9IsHPoRQPF5kIpCPBBAo8OLCCGP1xv8Hv9wvy/Qd77JCNY0/C/XK5sZ5rdpuRk7Pp+abbSn575m0Y7Vqmo3tViJ6XtrHrG7oCZRJTy9KMj4h2gHPTv7f48WJIUIWFRU5wXLSpEnuXkhBSgof8RvwzjvvuDYIoFSDJ7Qdw7uT3O2sp9DR+eef73KGsp3ft2sY/MPvCcchNyjCKGMRjUc8Hb2LgAiIQGEQCN1+CuOEcu0sFixYYPfcc4/rFh48999/v02YMMGJD9yw+Y7nJ59Jzj1u3Djjxn722WmmRHPtRNUfERCBnCXAoB5vc4oFxAtoPGQMGjTITcrk7AmoY66oEddp/vz5p13DKHgoCEE6Aa63TATyjQCeV4gfmK/izGfECb+c72UxJoIK0TT+LMSrWvXntPug2fOLSkTPBRvT96d32xLR85ogzL15w9Pb48WNByfCZzLzBShJBcO9jHEMuTj79Onj8nES5s7kLpN9/CbwGScT/9vA/3EcTzgWaXa4lzIhyHMX4yKsf//+7rcFwVQmAiIgAiJQuAQkgFbwtb333ntd8Qpu2q+++qpLvO0PSVLu73//+/alL33JLrvsMlu4cKHLg3fhhRe6nDMIpjIREIGSWXlCmsj9hIcg/3dU5Tr6Xwa/LckqrRM+Ro4rQqT1mxOdaVW07Nixo8tZ9t577xkF8TIxHhh5+OvUqVMmm6mtCOQEgS984QvGK97I34cgIjudgMafpzPRkrIROHrC7K0VJaLnm8vNTgQh76msTaOgkFF/s88NMTsnxaMM4e2LFy92u2rYsKG7P/kJCQoSEfbOmA9hkzD2kSNHOgGTDRA0MZ6vCH8nLQb3RcRS2jPuIeqF6AlEUZZzD8ThBI9Tfje8+Nm3b185njia+kcEREAECp+ABNAKvsbLlgWZwANj4E7VwUTGLObkyZPtiiuucCEa3JQ/+clPus+Ey8tEoDoTSOYd0LRpU/d/CjFUlpwA/JKJn+Gt8Cy86KKL3MNEeLk+5xYBcpaNGTPGCNslFNjnQozSS8RumQiIQPUgoPFn9bjOFXmWc9eXiJ7PLzTbl+b2Ub+O2aW9A2/PoJjReV0tiHRL3TO8NL34SdEiIhQQOzGEUcRPREs8NWfPnu3ud7NmzXIh7bQJF0ckpQ8enG+//bYLaydPMBOFeHgy/uE5y+cQJdSedDL+fkhB2q5dgw7LREAEREAEqgUBCaAVfJlXrAimTAMj/0wqw5vt5ZdfNu/9OXfuXPvsZz9rzz//vPLPpAKndQVNYMOGDa6CZ6KTxCMUj4BRo0a5RPaJ2miZuTCwKBwQ1MiP1aFDhyjN1aaKCfDw17x5kpK5SfpWp07whCoTgQIiQDXoqVOnujPiXpBJ+OqTTz7pRBImp6+88soColJyKhp/FtwlrZQTWh+k3PxnkNfzyXnB+CH4nMrQK0cFGbuuCYoZTQhSTDdIEj2OJyaTsbxj/D/1f5+EsZPLM2yIlBgRC6S/GD16tHMKYdznPUT5vx82nqOYGKcNn5koXLJkifMYJRzemw+1Z794fnpPUr9e7yIgAiIgAoVNQAJoBV9fX1mwQYMGaY9Ewm0ET0I8SOD90ksv2be+9S373//937TbqoEIFBoBEtmTwyyVMaOPZwCei0pUn5gUDwNRjbYSQKPSqvp2eL0ggkYNhydcWCYChUSAIibjx493p4RHGQVMotr1119v+/fvt69//esFKYBq/Bn1L0HtPgy8O18MItEpZjRzXXoe3YPo86uDnJ5UcW+bIgiHKAUK8CF+JjLC14lqiDfGf5iP8GHCDw9PwuJJhYRx34tP6UJ7xjFsTy0FCh4R8u4jYRA/SfWD1ydCqvc4dTvUPyIgAiIgAtWCQJoAhWrBoEJPkpAOjFCMKMYDKpXgCfvAyOH029/+NsqmaiMCBUWAECjyNKUzwpg2bdqUrlm1Xe/DvqIAyKRtlP2pTfkJUA2XhzneE1m850yiNizjoQ+PF5kIiIA5TzTvjRZ1AiHfuGn8mW9XrHL7e6K4JK/njU+YDfq52ff/mVr8bBZk5PrqCLN/3Wj29nfMvjUutfhJBA+V1xEfmaBu166dEx4RH334Ovc1InkY74XNj0XCE9tMbuAo4u+FhLETuRI2395vzzpEUSZ2/f93QurxFJX4GSanzyIgAiJQfQjIA7SCrzUDUDzUHnvsMfvxj3+ccKYzvgvkosET9IILLnA37Ntuu80QRq+77rr4pvouAgVLIJOH0kSeAAULJsMTI48wVU+jmHIOR6FUOW1IR0D+srAHL54yPDyGQ/Z4KOSesWbNmqQdIwJh4MDAXUcmAnlM4JlnnnG5+8KngAent4ceeiiSyI9oQsohL6QQgluIpvFnIV7V8p/T0s0lnp7PLAi8KJMXXncHOqOW2UU9S/J6XnBOUHAo+B7FuH9RmR0rKioycnyG01MgjmKdO3d2OTrJBYo3qPforFevnnv+YfIvHEFHxMPGjRtdiDvepUQJnXvuuTEx03uOsr03CgDSjgl1RFiNczwZvYuACIhA9SQgAbSCrzvFj/72t7+5GVA+P/zww5EqLVORGdH005/+tLtpf+1rX3MVC6N4xFXwKWn3IlApBHz4XpSDZdI2yv4KqQ0D/qgCKIUCZFVPgJBB0qDEG+G+06dPt549ezoh1K8njxkPdcuXL7f4/wtcf0IHvceN30bvIpBvBPDqIi1QMrvzzjuTrUq5fPjw4SnX5+tKjT/z9cplv99b9pk9t8jsH3PNViaORi910CGdSsLbr+gfeFDWL7Uq7Rdyc3rxE+GTKuzx5vN3UqwIb0wESqq2E/2GlyeTfUz+IaSGU7f4PNaMVfDoZP2cOXPcBJ+vFs+xfFg990P6ghcq90DulTIREAEREIHqTUACaAVff6q5k5/wjTfesFdffdXNgk6cONEVRbr55ptTHv2qq65y+T+/+c1vOhH0Jz/5SVBVUVkLUkLTyoIhwCDYz+anOynayhITIA8WVVB94v/Ercy6dOliDRs2TLZayyuJAJ4xicTP8OEROinyEBasuc540+ANzf8bHhR5sNT/jTA5fc5nAp/5zGfs4osvttdffz1rp3HHHXcY47RCNI0/C/GqRj+nQ8fMXgmybz0VOGK+GwQIBI6QKa1zkIrzqiBQ4DNBFXc+l9WIRkDgRMxMJH6yX8RI2pDCiPvWjh07XCojoh4oSkbIOvtZFxQvIsLB38d85XbSuTBxMWPGDOcVzvaMX/Dq5t6Idyhh9Yx9WIb3Ke39fsp6btpOBERABEQg/wlIAK2Ea3jfffe5BPuEeDCj+Ze//MWFxacTQOnaDTfc4AYKJOnnJi4P0Eq4YDpEThAgZ2HUMHjayhITIKwMj3IeFJKJoO3bt7dCDQNNTCU3lxKqt2zZskido11YAGUjJsh8/uhIO1EjEcgzAg888IC99dZbsV4jdHz7299233//+9/HiqbEGsR9wEuM8FjEEn7zCM8tZNP4s5Cv7unnRtr06WtLQtxfXGKGCJrKGgWR4hOC6u3XBMLn8KCaezbM52RPJn5yDAoQUcwIz0wES9qyHXk9iVhgPdELfKfw0ahRo1yI/M6dO10XWU/UA0WO8BxluU8Xw28C23gjZQz7VOi7J6J3ERABEajeBCSAVsL1x7Nq1qxZRi5PxE9ECG7sUe2rX/2qDR061G688UaXLDzqdmonAvlMgAdTZvD9jH+ycyHUSQJoMjoly3nYHzdunOPJQwa/QYhlPETAOZPfo9RH0tryEODBzRdqSLcfch9yHfVQl46U1hcSAXIEMibytm3btpgA+rnPfS6jKvB+H4X8rvFnIV/dj89t5TYzcno+Pd9sy4cfL0/0qVYQSDYuqM9KBfdP9A68McvwJMj9B+9KBEzuWThnMLHAeAxhk2JE5OtMZow5yOWJlyfRC4igbM94j/2R9xNPUCq486KYEpN7eI1S1Mjf9/AkZRzjhVGW4+3Jy0dJEAkhEwEREAEREAFPoAy3Pb+p3jMhwI39j3/8o+GhQL4aHnQzMfLWTJ482R555BFXGT5qVflMjqG2IpBLBAjhJWSJnIfxeQ19PxH2mByQpSeAJyheFqm8MtLvRS0qkkBU8dP3gfb+QdAv07sIVCcChMIyrsIQPGSnE9D483QmhbBk1wGz5xeXhLgv3JT+jPoGfhfXBKLn1QPMmpcx2w1CJ88fTE4TsRA2Uq/4tEWsY9yWLPc0xfsQMhE358+f79KCeQGUImUIoIiYo0ePdg4k5DFHcMXw4p47d64TSsmL7fuRLN9ouI/6LAIiIAIiIAISQCv5b8CHo5b1sFSCVzX4stLTdvlGgJl9PBcJ9yUUiiIYGAPjjh07ukIwPil+vp2b+isC8QQy/VvOtH388arbdx7e8VjioZmUMuSD40EcEU2WnwQQSnwIfH6eQeX1WuPPymNdUUc6esLsjeUloudbK82Kg5D3VNY2mBO4kryegfB5TjkzBfH7SSod8m0iQpK7E49sfj+JKCEiweewpi1OG2PGjEmYd5PtBw8e7KLaiErh99hPdPN36g1RtCiIUlm0aFEsBRhiqC/syH4IcUf8ZLwoEwEREAEREIF0BD6+y6RrqfUiIAIiUAUEECkYKA8cONDN+DPgZRnvMhEoJAI8wBE66IX+VOeG+CnhLhWh0uvID4fXEA/pYcObiYd4csTBXiYCIiACuUZg7nqzJ4NiRs8vMvvwSOreNTjD7LIgtB1vzzFdyQ2dun3UtUuWLHHiJ16dROfEh5bjgU1eXTw1CVXHG5T0X2PHjk04XqM9+clpQyoLb4TGc39je5Z7r1ImvUnpQOQDgqkPf2dCXCYCIiACIiACUQlIAI1KSu1EQASqlAAeBgr3rdJLoINXMAEEuKLA24W8aOmMB0H+T8jSE8BbaMqUKTEPovgt1q9f73LP8TCuiZV4OvnzndyDTz75pK1YscIJ3YgkPjw21VlMmDDBeMlEIJcIfLA7yOsZ5PR8KnjxOZUxHzyqS0lez0/2NUMEzaYRqr527Vp3z+F3MpW3JcX5+E3Fk9OHrnNfS2TkCR03bpzzLPWpwagEHzYmvPHwRADFUh07vJ0+i4AIiIAIiEAiAhJAE1HJ0WXMqIZnSTt06JCjPVW3REAEREAEykKABz1CtH1F20T7aNGihXK5JgKTYBkCGDnmCMlMZYTG47mU7EE91bZaV/UEfvnLX9rPfvazjPOr03PSIEgATX0NNf5MzSdba/cdNqN6O6LnrHXp93pOq4/yegZh7m0bp29f1haInxgFi9IJkF27dnUCKBMQGPlCU/2uEvruPfO7d+/uRFa2Jfydokp4mmpiyqHUPyIgAiIgAlkgIAE0CxAraxfkwAkXfIni2VBZfdNxREAEREAEyk8AL9BRo0YZ4Ya+6IPfKw+BPID27t1b3p8eSpp3xGTvWZSmqfNwSvWgnm57ra8aAs8++6z94Ac/qJqDV5OjavxZcRf6RJDafNKqEtHztffMjpWkOk96wOZnmk3sH3h7Djbr3z5ps6yu8M4XpAtJZ4S2I2SuWhWcVGBUjCdsHU/OeKN6++zZs13aF/bNvU0mAiIgAiIgAhVJQAJoRdLVvkVABERABEQgQwKEDpLzFm9QHhCpiku+s5YtW7oCYBnurlo3T+VJGw8GoRRPUaUWiCeTu9+5Xtdff32sg+SL/sY3vuEmCkiZEsVzTNE0MXz6UIkEFgeV258OPD2fWWi2u3Rq4tN6UTd4Wru4Z4m35/hzzGpXYrpi/o8dOXLE/V+KmneaexeiJ/k8sZkzZ7r8nWzP/0nSVVD8aOvWrW49hYwGDBjgPusfERABERABEahIAhJAK5Ku9i0CIiACIiACZSSA6Nm+fSW5+JSxj7m+WbrQ9/j+U4BKAmg8ldz9Tr5PvHyxSy65xJ555hmjMrxMBHKRwJZ9Zv9cEBQ0CoTPVdvT93Bo55K8nlcEHp+N6qVvXxEt/G8ov4tRJhToA+2GDBniUrkQ3k4OUVKRxBsRD926dbMePXpE3nf8PvRdBERABERABDIhIAE0E1pV3JbZUT9bWsVd0eFFQAREQAREIOcJZFI4jWrCVB+W5Q+BefPmxTr7ve99T+JnjEZ2P2j8WXaeh46Zvby0JMR9SlDfLkhLnNKKmpldHVRw/3Tw6hR8rmojIgGhkskhH42QaZ/OOeecWCg86bsIhyeXNRN85PqUiYAIiIAIiEBlEZAAWlmks3AcBiGtW7fOwp60CxEQAREQAREofAKEVnLv9AU5Up2xvG1T0cnNdf664nE2evTo3OxkAfRK48/MLiI116a9XyJ6UtTo8PHU2zcONMAJ/UpEz2FFqdtWxVqqtVMoDieMzp0Dt9QIRu5PvD+ZVMLDU571EaCpiQiIgAiIQIUTyEgAffzxx+2uu+6qkE7dcsstxkuWewS2bNlib7/9dtoqulF67kPVfEhNlG3URgREQAREQATKQoCH7549e7qiUqm2x/sTLyVZfhEYOXKk6zBeZYwvFP6eX9cvXW/zbfy5cluQ1zMIcSe359YPU59d7Zpm44KfHDw9L+5lRp7PXDXy5CKArly50nltIoins2XLlrkmTCxJ/ExHS+tFQAREQAQqi0D6O1ioJxQIWLMmiN+oAMukUEEFHF67TEHgm9/8pj333HMpWmS+yidGz3xLbSECIiACIiAC0Ql07drVFfFYvXp1wo3ItTp8+HCFYiakk9sLEa3xTtu1a5e9+eab9pWvfCW3O6zeZUQgH8afuw6YPbso8PYMsjEs3pz+9Pq1C0TPwWZXBTV/mgUV3avaDh065Dw78djEo5rfw8aNG1ubNm1iKUEQQPn95DmQtBNDhw5NKWoilCJeEzqviaWqvsI6vgiIgAiIQJhARgIoM34NGzYMb5+1z3hfVEfbvHmz81pgAMKLXDgMPBo1auQG9bmQG+emm25y/cHDorz24osvuqToVDOWiYAIiIAIiEBlEOjTp49LIfP++++7ey4P+uSha9u2rSvCUV3HIJXBvqKPQWTSd77zHbvzzjvtk5/8pMstWNHHLIT9a/xZ9qt49ITZ64GD41OBp+fbK82Kg5D3VNa2cZDXc2CJt2f3VqlaVt46qrQvXbrUVWNPdFTEyy5durjwdT4PGzbM3nnnHSdsTpkyxfr27WvNmpVOUspzzHvvvRfb5+DBg93vbKL9a5kIiIAIiIAIVAWBjATQ66+/3njJyk6AGdZHHnnEHnvsMReSx/dkhuDcr18/55kyYcIEu/zyy6ukSuLFF19svLJhDIbw9uXBUyYCIiACIpAfBHhY5uGWB+GzzjrLvedHzz/uJUU3eMkKi8C3v/1tF57785//3KU7+I//+A+74IILXK5CjTU+vtYaf5Z//Dl7XYno+cJisw+PfMw20acGgV/H5X1KRM9RXSzwmEzUGe1THQAAQABJREFUqmqWkS5i5syZduzYMefJyUQQYiYpQ44cOWI7duxwr1WrVrn3ESNGOAeYMWPGuO0Yx7/77rtGkTkcNghxJ9/n3r173Qlxnxg0aJC1axe4u8pEQAREQAREIIcIZCSA5lC/864r27ZtMwbljz76qKuEGOUE8FCZP3++e/3pT39ys63/9V//5TwcomyvNiIgAiIgAiJQHgI7d+50Hj3hNDU83BYVFTmxKUouuPIcX9uKQCoC+/btM6JUMMROQuH9d5Y1adIkrVh/++23G69CNY0/y3dl1+0yeybI60mI+/o9qfcV1OKyMV1LRE/Ez/o5GNyGUDljxgw7fvy4USRu4MCBpzkldO/e3TkrzJkzx4maiKUUGSNCbfz48S4cft26dU70ZH/euDcgepJ3Wfl4PRW9i4AIiIAI5BIBCaCVcDV4cMSDcvHiYMr4I6NiKTOunTp1MsLBGbiTdwfRk9lX8uxs2LDBPvjgAzt69KjbasmSJXbFFVfYb37zGxfu5feldxEQAREQARHINoH169e7Cbj4/RYXF7t84BTFwCNI4ePxhPS9sggwXiKiJpl5j7Rk61mOd3OhmsafZbuy+4I/Cbw8CXGf80H6ffRobXZNEOJ+dVDQqE2j9O2z2YL0VDxTRLWFCxc68ZMcn+eee27SbZs2bWpjx461yZMnOzGUGhDk88RLtFevXk7k5P8X/38obErKLrZBBJWJgAiIgAiIQK4SyAkBdOvWrYaXCflkCs2YGSUnlRc/yaFz66232oUXXuiEz3TnywztrFmzXNj8Qw895AYt3/3ud90ghJB4mQiIgAiIgAhkmwCTcAsWBG5PKYyQWtrwEC0TgaogQOgtBVrKY4TwFqJp/JnZVT1RXJLPkwrurwX5PY8F31NZi6AkwpX9Swoa9a3ESG9+m/G+ZAIqLD6S3qNjx47OqzNZvwl9J7wdEZMQ9XTCKY4ZtJs6daoRDk9BOS9wsi2CJy+ZCIiACIiACOQLgawLoJs2bbKnn37a3ZjJLcOsYNjwHOGFpyMzh1QDnzZtmv34xz8uSAH0H//4h02fPt0huPbaa52nAgP2qMYghbATXhMnTrQrr7zSiaB33HGHXXrppSmrMEY9htqJgAiIgAiIQJgAD7tRCt9R6ZcH8kIVkcJM9Dn3CBBBQ7SM7HQCGn+eziTRkkWbSsLbqeS+++No7kRNrW7w1HRxr5IQ93HdzWpXorMjz1M4UyB+xhue0DxP8eL/BPn2ExVR5fcaI/osquc+wiqpJHhmw1mldevA3VUmAiIgAiIgAnlKIKsC6M0332z333+/E+jylEfWu424i/Xv3995cWYifsZ3Bo/PX//610bCfwZBa9eudbOx8e30XQREQAREoGIIMIFH1AKeNHwmzxnpTCgMVEiGd1FUo60E0Ki01E4EKoeAxp/JOW/eF+T1DDw9nw6c3FdF+Kk7t6hE9JzQz6xRveT7rag1iJ9cT3Lc4nl59tlnO8/ncAEixM3Vq1c7D0/C1s8777zTcnuSMxdDJM3EEEERQNleAmgm5NRWBERABEQg1whkTQB9+OGH7Z577sn4/AilIHyOCoOFaISNYJ/61KdcyEl5z/Gaa65xAij7WblypQTQ8gLV9iIgAiIQkQChg/PmzXN5msObLFu2zFWc7tevXyw8MLw+3z7j+UkER1Tzeaqjtlc7ERCBiieg8efpjOu0GWrLO91tw39pgYf76evDS4qaBXk9g5yevDoFn6vSFi1a5MRPvDp5XqIYUdgaNmxoFC7Cs5O0Wb7KOzk8w44X/ned0PZMzLf322eyrdqKgAiIgAiIQC4RyIoAyswkeS29ffazn3Xh2cwSfuYzn7FDhw7Z3XffbTwcclPm5kw1dHLXUE3w9ddf95sW3DvhKBh5ebJhzZs3d0IquUELOXF/NlhpHyIgAiKQLQKE/pHOJFlYuC9Yl6qoRLb6UtH7wcOI9CvcZ6JY1FDKKPtSGxGoTAKFnINe48/T/5Ia9PmC7T8zSNyZRPxsHHh3fipY/ZnBZkM6nb59VSzB85L7C0JmIvEz3CeEStpMmjTJeWuyHd6i3vxvdaZCpm/vt/f707sIiIAIiIAI5BuBrAig5F9C2MS+8Y1v2L333hvjQIXY1157zUjGftVVV7nl119/vX3hC1+wCRMm2BtvvGGPP/64ff7zn49tU0gfSBhOkQgenG+44YZynxohMP6hlMTkMhEQAREQgYolwCTf/Pnzk4qf/uiIKYgO2Zrw8vutindCJDdv3hzp0JmGU0baqRqJQIYElIO+NDCNP0vzSPatdpCWf/w5JcWMLu5pdkZWnoySHS3z5aS7wrp06XKa52eivTF51adPH5s9e7ZLlRUWQAmZJ5KBCb1WrVol2jzhMtpjhZbqJeHJaqEIiIAIiEBBE8jKbZ5QbG8U5wkbxXsQQN98883wYjv//POd5+eoUaOMquaXXXaZS7JdqlEBfBkyZIgTQP/+97/b1772NXfeZT0tZoG/973vuc2bNWtWala3rPvUdiIgAiIgAqkJkOOSSIYoRoGKQhBACaeMIoDyEE2BDJkIVCUB5aA/nb7Gn6czCS8Z0L5E9JwYeHw2OzO8Jrc++3zMmdxXyEuNELp//3537yJXNcbyNWvWOI9SfuNpk85wcNmzZ49L75KJaJpuv1ovAiIgAiIgAlVBIHo58hS9I+k2xg22c+fOpVr26hWUSwxs6dKlrmBEeCVhGoTFb9u2zRAIC9F++MMfugEGFRqp4n7fffdllFvNM8GL9BOf+IQTU1mGp61MBERABESg4gkw+RTVMmkbdZ9V0Q5Rk/tzKjvzzDNNkQipCGldZRDwOeh9dEzUY5KDfuTIkQWbg17jz9P/Ek7sXmltdz5lk75j9uJNZl8bmdviJ9EHPD+QliQT70va+8J04ck70mjxIqR94cKFpwOKW8L/KaIfMDyK+T8jEwEREAEREIF8JpAVD1A/s9i0adPTWJxzThBXEhg3cDxFvSDqG+IJSnJvqpoXojFg+PnPf27f//73XT4ehEs+c94DBw50XpzkSq1fv76R3PzEiROO1YcffmikFkBcpprjkiVLYngQQn/605/GvuuDCIiACIhAxRHgITSq0ZY8oTyA5rsRconIyf3nwIEDsdPh3PBGIsxSOeFiWPShCgjw/0056BOD1/jzdC4H5//JOnTeat1affr0lTm4pLi42PUK4THTe4ovfhR//xowYIB7riBlBPcqvif6Hcd7lDB6fvsRU/EYlYmACIiACIhAvhPIigDas2eQNCcwwjTiH/y4YXLTZjmzjfECqPcwKVQBFC633Xabm3G96aabXOEiBhX/+te/3Iv1mdill15qjz32WKmqjplsr7YiIAIiIAKZEUAEjGq0zfRBNeq+q6IdE3S8uG+Ry5sHcbxDo4ROVkV/dczqRUA56FNfb40/U/PJ9bX8zvKbi3PE0aNHzVdjj9Jvfq8xnCvChicpxfooSEuaE57dOnToYKTW4ng4rLBsy5YtbjPaE7FXu3ZWHhnDXdFnERABERABEah0AlkJgfcCKKESeCuGDe/Qdu3auUVz5swJr3Kfp0yZ4t59EaXTGhTIAvJ/Uo3xzjvvtDZt2mR0Vgx4CJ9/4YUX7OWXX3aDlIx2oMYiIAIiIAJlJsBvtvemSbeT9u2DxHIFaDwEw4GCRxI/C/AC5+kppctBz2kly0HPRAU56AslbUWyS6jxZzIy+bGckHXMC5JRek0UGaHveHYmCp3nd5xINHJ6Iq6Su3revHk2c+ZM56zCsfj/QQGl8847z0WpRTmu2oiACIiACIhArhPIynRe48aNraioyN1Av/Od79hLL73kEm37kydHGKEW5Pn80Y9+ZD5UnrAM2mKE2hW6MeD42c9+5l4MNmbMmGGrVq0yBir79u1zHjY8WDZs2NCFmxC+1Lt3bxeewjKZCIiACIhA5RNgEqpHjx62bNmylAcnlUm3bt1SttFKERCB7BHIJAd9OH+hz0FPCibGpjfccEP2OpWDe9L4MwcvSsQukW4Ej0zEfjw1o3hi+nsVE3LJIhJ4riAHLs8gCJ6EuuPIwv0OL38mvLinyURABERABESgkAhkRQAFyH//9387L0WK9RDm/sUvftHuuecex+qrX/2qC/feuHGjTZgwweXA5ObKNjt37nRtfCi8+1IN/kEw5iUTAREQARHIfQKkcyEE8f3330/YWR4UeZiUd2RCPFooAhVCQDnoM8eq8WfmzKpyC0RMhH4cJYikGzZsWMpiRMuXL7etW7c6odTXYUjVf/J7+oJJqdppnQiIgAiIgAgUAoGshMAD4oorrrDrrrvOMeEm/dxzz8X4XHXVVbHk2dOmTTO+jx8/3p599lnXhhvvLbfcEmuvDyIgAiIgAiKQSwTwomGibvTo0S6tC3nVEDuJgGDS74ILLkgYaphL56C+iEChEfApmHwO+vD5+Rz0LEtU8dpPvBdyDvowD33OTwLcexA9ud9s27bN3n333ZjzSPiM8OAkr+eKFSvc4iFDhpyW/zPcXp9FQAREQAREoDoSyJoHKPDuvfde44b7hz/8oVQIPLnTXn31VaOATzhfE9vgNXPfffe5PDR8l5n98pe/dLl4YAFLQpdkIiACIiACVU+gRYsWxksmAiJQ9QS8AOpz0JPX0JvPQU8KJjznrr32Wr/KvVeXHPSlTjrNF40/0wCqotUU1xszZozL0YmTydSpU93zEw4kPGNR8IhQdoxUDzyLZVpvoIpOTYcVAREQAREQgUolkDUPUHrNYBNPTkTOBx54oNSJkEgb78/f/e53LlQeL5qbb7454aC01IbV8Auzu+Sk4uWrOFZDDDplERABERABERABEUhKwOegpwE56OMLxZCDHmM8tWfPHveZf6pbDvrYiaf5oPFnGkBVuBqxk+g5RH8iEA4fPuw8QvmbR/wkN2jnzp3twgsvLOWEUoVd1qFFQAREQAREIOcIZNUD1J8d4RqJ8s5QyZABKi+ZCIiACIiACIiACIiACJSHgHLQl4eets0nAoicFOTjGWv//v2u0jtiPoIotRXwBpWJgAiIgAiIgAgkJ6A7ZXI2WiMCIiACIiACIiACIpDDBJSDPocvjrpWIQRwNMEjlDD3du3aWbNmzSR+Vghp7VQEREAERKDQCEgALbQrqvMRAREQAREQAREQgWpEgBz0eIJ269bNunTpEjtzn4M+UVSSctDHMOmDCIiACIiACIiACFQLAlkJgSdP5a9+9atyARs3bpzxkomACIiACIiACIiACIhAVAI+B/23vvUtW7VqVanNfA76Rx991CZNmuQqaJMb9Jvf/Kb17t27VFt9EQEREAEREAEREAERKFwCWRFADxw4YHfffXe5KBHOIQG0XAi1sQiIgAiIgAiIgAhUWwLKQV9tL71OXAREQAREQAREQATSElAIfFpEaiACIiACIiACIiACIiACIiACIiACIiACIiACIpCvBLLiAUrlwWeffTYlg1OnTrlqhbt27bJZs2bZ008/bYcPH7Yf/vCH9tOf/lTJu0P0yFlFpUcMbwaZCIiACIiACIiACIhAaQJKwVSaR3m/afxZXoLaXgREQAREQAREIJcJZEUArVu3rk2cODGj87zppptswoQJ9p//+Z+uiuEtt9yS0faF3Pi5554r5NPTuYmACIiACIiACIhAuQkoBVO5EZbagcafpXDoiwiIgAiIgAiIQIERqLIQ+BEjRtirr77qcH7nO9+x+fPnFxhanY4IiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEBVE8iKB2hZT2LIkCHWqVMnW79+vb355ptGVU6ZCIiACIiACIiACIiACKQjoBRM6QhpvQiIgAiIgAiIgAiIgCdQpQIonaDy+yOPPGJTpkyx2267zfdL7yIgAiIgAiIgAiIgAiKQlIBSMCVFoxUiIAIiIAIiIAIiIAJxBKosBN73Y+XKle7jBx984BfpXQREQAREQAREQAREQASyTkApmLKOVDsUAREQAREQAREQgbwgUKUC6JYtW1xFeEgNHDgwL4CpkyIgAiIgAiIgAiIgAvlLwKdgOnXqlEvBlL9nop6LgAiIgAiIgAiIgAhEJVBlAujzzz9vw4YNs5MnT7q+Dh06NGqf1U4EREAEREAEREAEREAEykyAFEwYKZhkIiACIiACIiACIiAChU8gKzlAd+7caYQUpTPEzmPHjtnu3bvt8OHDseYdO3a0z3/+87Hv+iACIiACIiACIiACIiACFUVAKZgqiqz2KwIiIAIiIAIiIAK5SSArAmhxcbGtWbOmTGd4xhln2BNPPGHNmjUr0/baSAREQAREQAREQAREQASiElAKpqik1E4EREAEREAEREAECodAVgTQGjVqWMOGDSNRqVmzptWrV8/atm1rV199tX396193nyNtrEYiIAIiIAIiIAIiIAIiUEYCpGC68cYblYKpjPy0mQiIgAiIgAiIgAjkK4GsCKCtWrWy/fv35ysD9VsEREAEREAEREAERCDPCCgFU55dMHVXBERABERABERABKqQQFYE0Crsvw4tAiIgAiIgAiIgAiJQDQkoBVM1vOg6ZREQAREQAREQAREoIwEJoGUEp81EQAREQAREQAREQASqjoBSMFUdex1ZBERABERABERABPKNgATQfLti6q8IiIAIiIAIiIAIiIApBZP+CERABERABERABERABKISyEgAnTdvnr300ktR951Ru7FjxxovmQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhki0BGAujs2bPtrrvuytaxS+3nJz/5iQTQUkT0RQREQAREQAREQAREQAREQAREQAREQAREQAREoLwEapZ3B9peBERABERABERABERABKqSwLFjx+yDDz6w6dOn2+TJk23ZsmW2a9eulF06ePCg3Xbbba5tyoZaKQIiIAIiIAIiIAIikPcEMvIAveSSS+zZZ59NetL/+Mc/7G9/+5tbf/bZZ9tNN91kPXv2tA4dOliLFi1s69atbnD61ltv2QMPPGBHjx61AQMG2N///ndr06ZN0v1qhQiIgAiIgAiIgAiIgAiECaxcudKeeOIJ91q+fLmdOnUqvNp97tatm1188cX25S9/2UaOHFlq/W9+8xvj9fvf/97Wr19v7dq1K7VeX0RABERABERABERABAqHQEYCaFFRkfFKZO+++6499dRTbtVvf/tb+9a3vmW1a5feffv27W3IkCF29dVX2w9+8AObOHGizZ8/326//XZ7+umnE+1Wy0RABERABERABERABEQgRgDPzhtvvNGYeE9nq1evNl733nuvXXrppcYYtVevXrZ9+3b71a9+5Ta/5pprJH6mA6n1IiACIiACIiACIpDnBLIWAv/FL37RCD+69dZb7bvf/e5p4mc8p44dO9o///lPa9KkiT3//PORBrHx+9B3ERABERABERABERCB6kPgtddesz59+iQcN9apU8eYbG/ZsmVCIK+88ooNHz7cXnzxRbv77rvtwIEDVrNmTbvjjjsSttdCERABERABERABERCBwiGQFQF0zZo1tmHDBkcF8TOqde7c2Qirx6ZOnRp1M7UTAREQAREQAREQARGoZgQWLlxoV111lW3bts2dOeIl0UR/+ctfbMuWLS610saNG513J16ijC0ffPBB++xnPxsjtX//frviiivsvvvuc8sYtw4aNCi2Xh9EQAREQAREQAREQAQKk0DpGPUynuOUKVPcluT5JN9nJta7d2/XfMaMGZlsprYiIAIiIAIiIAIiIALVhACC5pVXXmmHDh1yZ9yvXz+7//77bcSIEQkJ/P/s3QucTeX6wPFnxriMy8gtdyGEQwipFBVJEnVUdCqFLoocVCq6k5zorotKTqRSp3M4SEUlCalQbrnfy/1+N2b/9/Oe/1rtPbP3zN4ze9bee+3f+/lse13etdb7ft9tZs2z1/u+pUuXlosuusi8evbsKd27d5e7775btm3bJhkZGeYY7Qo/bNiwgMezEQEEEEAAAQQQQMBdAhF5AvT06dNGRW9ODxw4EJbQ0qVLTf7U1NSwjiMzAggggAACCCCAQGII/OMf/5CNGzeayuoTm/PmzQsa/AwkcvXVV8vy5cvl7LPPtnc3a9ZMihQpYq+zgAACCCCAAAIIIOBegYgEQGvXrm2EdPZNnY0z1KRd53UsJ036LTwJAQQQQAABBBBAAAFfgePHj8u7775rNumTndOmTZPixYv7ZglpWbvE672nlXTyzv3791urvCOAAAIIIIAAAgi4WCAiAdALL7xQdDxPTf3795dvv/02RzIdo0m7Mh08eNDkvfPOO3M8hgwIIIAAAggggAACiSXw6aefivYy0qTd2CtVqhQ2wKlTp6R3797muLS0NPN+7NgxmThxYtjn4gAEEEAAAQQQQACB+BOISAA0JSVFhgwZYmqv39Jfeuml0qFDBzO7+7Jly8y36zpDvH7r/vXXX0u/fv2kVq1aovs0XX/99XL++eebZf5BAAEEEEAAAQQQQMAS0MmPrGQFMa31UN915ndrws4RI0bYh/3000/2MgsIIIAAAggggAAC7hWIyCRIyqNPcGpA85VXXjFaM2bMEH3llPTp0fHjx+eUjf0IIIAAAggggAACCShgjf1ZokQJqVatWq4EJk2aZI6rW7eueRL00Ucflb1798qmTZtydT4OQgABBBBAAAEEEIgvgYg8AWpV+aWXXpJx48ZJhQoVrE1B3wsVKiSPP/64fPPNN8IESEGZ2IEAAggggAACCCS0gPXkZpUqVXLtoF+2f/bZZ/Lqq69KUlKSNGjQwJxLh2QiIYAAAggggAACCLhfIGJPgCqV3lDefvvtcuONN8qsWbPMjebKlStl586dol3jy5cvL3rz2r59ezP+Z9myZd0vTA0RQAABBBBAAAEEci2gEx9p0kCoTrip95vhpoIFC8pVV11lH2YFVc844wx7GwsIIIAAAggggAAC7hWIaADUYipatKh06tTJvKxtvCOAAAIIIIAAAgggEK6ANdHm4cOHZe3atVK7du1wT+GX/8CBA7JhwwazrXr16n77WEEAAQQQQAABBBBwp0BEu8C7k4haIYAAAggggAACCERLQCfOtFIkJi3yPYfvua1r8I4AAggggAACCCDgPgECoO5rU2qEAAIIIIAAAgi4RuCGG26QAgUKmPro+PFHjhzJdd1OnTolDz30kH383/72N3uZBQQQQAABBBBAAAH3CoQVANUJjnSsJH1ddNFFtoqO8Wltz+37iBEj7POxgAACCCCAAAIIIICAClStWlU6dOhgMLQL/IMPPphrmMcee0x+/vlnc3zr1q3tyZByfUIORAABBBBAAAEEEIgLgbDGAD158qTouEmaDh48aFdQB6S3ttsbw1w4ceJEmEeQHQEEEEAAAQQQQCARBB555BGZMWOGpKenyxtvvCE6MZJuK1asWEjV18k4n3/+eRk5cqTJn5ycLBoMJSGAAAIIIIAAAggkhkBYAdASJUqINVh85cqVbSHtlmRttzeGuaBPjpJiU2D06NHy6quvmplX81rCzZs3m1PkNWCe13JwPAIIIIAAAgjEj8CFF14oo0aNkv79+5tCP/PMM/Lee+/JP/7xD+natavdRT5zjTIyMuRf//qXDBo0SDZt2mTv1uPbtGljr7MQewLcf8Zem1AiBBBAAAEE4lkgrACojpMUaKyksmXL2rNpxjMGZQ8soF3FVq9eHXhnLrcePXo0l0dyGAIIIIAAAggkosDf//53WbNmjbz22mum+lu3bpWbb75Zbr/9dvNFvE5odPbZZ5tgqHaVX7dunaxfv160B5Nv6tWrlzz88MO+m1iOQQHuP2OwUSgSAggggAACcSwQVgA0jutJ0fMg8Pbbb8ujjz4q+hRFXlPnzp1l5cqVUrFixbyeiuMRQAABBBBAIMEE9KnAtm3byt133y06Br0mndhIA6P6yi5pt3kNnnbr1i27bOyLEQHuP2OkISgGAggggAACLhEgAOqShszPaqSkpJgnKiJxjSJFikTiNJwDAQQQQAABBBJU4Nprr5WLL75Y3nnnHfnggw9k6dKl2UrUqVPH9GC66667+AI2W6nY2sn9Z2y1B6VBAAEEEEAg3gXyJQCqkyLpIPUFCxb089En/z777DOZPXu2lCpVSv7617+KPhGYlJTkl48VBBBAAAEEEEAAAQSCCejwS9qNXV/Lly+XX375RbZt22Zep0+fFh2rXl9/+ctf5Lzzzgt2GrYjgAACCCCAAAIIJIhARAOgv//+u4wZM0bGjx9v3tu1a2czzpo1S9q3by96U2qlCRMmSPfu3c0g9tY23hFAAAEEEEAAAQQQCFVAg5z6IiGAAAIIIIAAAgggEEwgYgHQEydOmACn1Q1JB5630pYtW8x4S77BT2ufBksbNWokAwcOtDbxjgACCCCAAAIIIIAAAggggAACCCCAAAIIREQgOSJn8Z5k8ODB9hhM2qU9OfnPU7/55puyZ88ec6nGjRvL3LlzRZ8ItbokDRo0yHRfilRZOA8CCCCAAAIIIIAAAggggAACCCCAAAIIIKACEXkCVJ/s1Fk1NWlQUwelb9KkiVnXfz766CN7+d1337X3aRC0evXqcvDgQRMUpfuSzcQCAggggAACCCCAAAIIIIAAAggggAACCERA4M/HNPNwMu3url3gNelg9L7Bz1WrVsn69evNvnPOOcdvn06EdP3115t9ixYtMu/8gwACCCCAAAIIIIAAAggggAACCCCAAAIIREogIgHQFStWmPIUKFBArrjiCr+yzZgxw16/6qqr7GVrQZ8A1bR48WLzzj8IIIAAAggggAACCCCAAAIIIIAAAggggECkBCISAN28ebMpT+XKleWMM87wK5tvAPTKK6/026crp06dMtvS09Oz7GMDAggggAACCCCAAAIIIIAAAggggAACCCCQF4GIBECrVKliyrBz506/shw7dkzmzJljthUpUkRat27tt19XfvvtN7OtWrVqWfaxAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyItARAKgtWrVMmU4fvy4md3dKtCkSZNEt2m67LLLJDU11dpl3jds2CCTJ082yzVq1PDbxwoCCCCAAAIIIIAAAggggAACCCCAAAIIIJBXgYgEQBs2bCj16tUzZenVq5f897//lQ8++EAGDhxol+/WW2+1l3Vh/vz50rZtW7sL/G233ea3nxUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCvAil5PYEen5SUJI8//rjcdNNNouOBdu7c2e+0rVq1ki5dutjbLr30Uvn222/t9U6dOknjxo3tdRYQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFICEQkAKoF6datm5w4cULuvPNO+6lO3a5Phmo390KFCumqSSVLlrQWpWPHjvL+++/b6ywggAACCCCAAAIIIJBbgV27dsmaNWvM69ChQ9K3b19zqnXr1olO2Knj0pMQQAABBBBAAAEEEksgYgFQZdNu7O3atTNPd27cuNGM+9m8eXNJTvbvaa9Pe3o8Hunatat5ajTz/sRqAmqLAAIIIIAAAgggkFeBjz76SB5++GHZtGmTfaoyZcrYAdBRo0bJp59+Kvfee68MGTJEChYsaOdjAQEEEEAAAQQQQMDdAhENgCpVxYoVzdOg2bE99dRT2e1mHwIIIIAAAggggAACIQnopJrdu3eXuXPnZptfv5zXp0P1PvSnn36STz75JMsEndmegJ0IIIAAAggggAACcSvg/2hmhKuhN5nz5s2T9957T0aPHm2fXbsgWbPD2xtZQAABBBBAAAEEEEAgDIH09HTTo8gKfpYoUULat29vJtrMfJqqVavam6ZPn26eBLU3sIAAAggggAACCCDgaoF8CYBqF6Tq1avLmWeeKS1btpTbb79dnnzySRtSuyBVq1bNbDt16pS9nQUEEEAAAQQQQAABBEIV0Kc5f/zxR5O9Z8+eok95zpgxI2BvpLfeekt++OEH01tJD5gwYYIZJzTUa5EPAQQQQAABBBBAIH4FIhoA1S5Il1xyiRnX03f8pcw8vl2QrrvuOjl27FjmLKwjgAACCCCAAAIIIBBUQJ/+1C/VNV155ZXy9ttvS+nSpYPm1x3nn3++zJo1SwoUKCCnT5+Wd955J9v87EQAAQQQQAABBBBwh0DEAqB0QXLHB4JaIIAAAggggAAC8SDw22+/2UMqPf/881km3QxWh/r160vnzp3N7tWrVwfLxnYEEEAAAQQQQAABFwlELABKFyQXfSqoCgIIIIAAAgggEOMCS5YsMSXUcT/r1asXVmnPPfdck3/9+vVhHUdmBBBAAAEEEEAAgfgUiEgAlC5I8dn4lBoBBBBAAAEEEIhXgRMnTpiiFypUKOSnP626Hjp0yCwWK1bM2sQ7AggggAACCCCAgIsFIhIApQuSiz8hVA0BBBBAAAEEEIhBgUaNGplS7dmzR7Zs2RJWCX/++WeTv0GDBmEdR2YEEEAAAQQQQACB+BSISACULkjx2fiUGgEEEEAAAQQQiFcBDV7qZEaadCimUNPnn38us2fPNtkJgIaqRj4EEEAAAQQQQCC+BSISAKULUnx/CCg9AggggAACCCAQbwJFihSRv/71r6bYY8eOlZEjR0pGRka21fjmm2+kR48eJk/RokWlY8eO2eZnJwIIIIAAAggggIA7BFIiUY3MXZCqVq0a8mnpghQyFRkRQAABBBBAAAEEfATeeOMNmTt3rvzxxx8yaNAg+eSTT8wM79u3bze5PB6PaE+lxYsXy4wZM8x+6/Dhw4dLzZo1rVXeEUAAAQQQQAABBFwsEJEAqNUF6fTp06YL0jvvvBMSGV2QQmIiEwIIIIAAAggggEAAgTJlysh7771nngQ9fPiw/Pjjj+ZlZd27d680adLEWrXfO3ToIP369bPXWUAAAQQQQAABBBBwt0BEusDTBcndHxJqhwACCCCAAAIIxKrAFVdcIatWrZJbb71VkpKSsi1mhQoVTMB02rRpOebN9kTsRAABBBBAAAEEEIgrgYg8Aao1pgtSXLU7hUUAAQQQQAABBFwjUKlSJRk/frwMHDhQ5s2bJ2vWrDGvXbt2SY0aNaROnTrm1alTJ0lLS3NNvakIAggggAACCCCAQGgCEQuA0gUpNHByIYAAAggggAACCOSPQOPGjUVfJAQQQAABBBBAAAEEfAUi0gXeOiFdkCwJ3hFAAAEEEEAAAQQQQAABBBBAAAEEEEAgFgQi9gSoVRm6IFkSvCOAAAIIIIAAAggggAACCCCAAAIIIIBAtAUiHgC1KkQXJEuCdwQQQAABBBBAAIHcCnz44Yfy2GOP5fbwbI/TmeCZDT5bInYigAACCCCAAAKuEMi3AKgrdKgEAggggAACCCCAQFQFDh48KOvWrcuXMuzbty9fzstJEUAAAQQQQAABBGJLICYCoFu2bJHffvtNdAxRksjp06fNjf7atWulVKlSZvbSChUqQIMAAggggAACCCScQEpKihQvXjxf6l2oUKF8OW88npT7z3hsNcqMAAIIIIAAAqEKRDUAmpGRIa+//roMHjxYBg4c6PoA6Ndffy2TJ0+Wo0ePyjvvvJOljZYuXSr9+/eX77//Xk6cOOG3//zzz5c77rhDevXqJcnJEZ27yu86rCCAAAIIIIAAArEkoPc++iLlToD7z9y5cRQCCCCAAAIIuEsg1wHQU6dOyccffyzTp0+XDRs2mCcVGzZsKDfddJPo+J85peXLl8udd94p8+fPzylr3O8/fvy4GV/q7bffNnVp1KhRljrp2FYjRoyQ9PT0LPt0w8KFC81r/PjxomNhValSJWA+NiKAAAIIIIAAAgggwP0nnwEEEEAAAQQQQOBPgVwFQPVJxc6dO5vA55+nEpkxY4a88MIL5mnOZ599NuCTivpk4/Dhw02w7+TJk76Hu3b55ptvln//+992/XQsK9+kT4MOGzbM3lS2bFkTRK5Vq5bo2FRr1qyRJUuWiD4xO3fuXLn66qtl3rx5UqxYMfsYFhBAAAEEEEAAAQQQsAS4/7QkeEcAAQQQQAABBETCDoBu3LhRWrZsKYcOHQrop08wPvfcc1KmTBkZNGiQX57Vq1fLddddJytWrPDbfvnll0v37t39trll5auvvrKDn5UqVZJ//OMfxsCqn47zqd3eNRUoUEAeeeQR8ypatKiVxbxrAPS+++4zAdBff/1VnnjiCRk1apRfHlYQQAABBBBAAIFEFtAvi3/88UcztvyOHTvMuOo6jvpZZ50lF198sSTKmJ/cfyby/wLqjgACCCCAAAKBBMIOgGpQ0wp+asBOx2S66KKLZPPmzTJ16lRz06kXevzxx+Waa66RevXqmet++eWX0rVrV9m/f79djtKlS5sgXo8ePextblvQMU41aUBTb0br1q3rV8VPPvlEjhw5YrZpN3gNbAZKOqzAzJkz5cILLzRPg44dO1aefvppc95A+dmGAAIIIIAAAggkioDeXw4dOlTef/992blzZ8Bqp6WlmR5Mmk8Dom5O3H+6uXWpGwIIIIAAAgjkRiCs2XR03M9p06aZ6+hEPF988YWMGTNGbrvtNtHgnY5Tqd1tNGlX94kTJ5rl0aNHS4cOHfyCn926dZOVK1eKm4OfWnmd3V6TTmCUOfip23/++Wd9E+32/uijj5rlYP8UKVJEXnnlFbNbb/T1SVASAggggAACCCCQyALfffed1K5d2wzDFCz4qT46BNGECROkfv36ol8kuzlx/+nm1qVuCCCAAAIIIJAbgbCeANVu2MeOHTPX0cmO2rRpk+Wab731lgmE6riV+sSjBj779esnHo/H5NVu4Bo07dixY5Zj3bhh06ZNplpNmjQJWD2dQEpT06ZNTRf4gJl8Nups8AULFhQNRuu5L7jgAp+9LCKAAAIIIIAAAokjoOPSa4+jAwcOmEoXLlzYjJVes2ZNqVatmqSkpMiWLVtMTyX94n737t1y9OhRueuuu0R7IunQTG5M3H+6sVWpEwIIIIAAAgjkRSCsAOj27dvtawW7YdSu3tdee62MHDnSdIfXLvJW8FOf+nzjjTfkjDPOsM/j9gWdyOiXX34RvUEPlJo1ayaLFi0KtCvgNr1x1+CnpjPPPDNgHjYigAACCCCAAAKJIPDggw/awc8777zTDCVUuXLlgFXXIYe0a7j2WtKeStoLqW3btlKiRImA+eN5I/ef8dx6lB0BBBBAAAEE8kMgrC7wvrOX67fqwZI17ufp06dNF/CkpCR5+eWX5cMPP0yo4Kf6aIBT03/+8x/znvmfVq1amU0aBFWvnJI1BIGannfeeTllZz8CCCCAAAIIIOBKAZ1cU5/q1KTBTO2FFCz4qXmKFSsmGjDVnkia9KnRd955xyy77R/uP93WotQHAQQQQAABBPIqEFYA9PDhw/b1ypUrZy9nXihZsqTfJh0DVLvBJ2LSLuuatKu7PhWbOekwAuq1a9cuGTFiRObdfus60dSLL75otuk3+5md/TKzggACCCCAAAIIuFjAGgtdJ+XUL9pDTTp2vc4Ir+mbb74J9bC4ysf9Z1w1F4VFAAEEEEAAAQcEwgqAhloeHX/JSi1btpR7773XWk24d73Jbt68uan3oEGDpG/fvrJnzx7boUKFCmZiqdTUVHnqqafMAP6BngT94YcfRG9mV61aZY596KGH7HOwgAACCCCAAAIIJJqANTRTw4YNw+7Grvenmqyx2N1mx/2n21qU+iCAAAIIIIBAXgXyJQDqW6gbb7zRdzXhljUY/Omnn5pZ3rXyr732mpx99tly6623yrhx42Tx4sVmkH5d1rFS77//fvnLX/4iOnbqsGHD5JZbbjH5dbKjHTt2GD+9qdX9JAQQQAABBBBAIFEFzj33XFP1P/74I2wCnQhJk96TuTFx/+nGVqVOCCCAAAIIIJAXgbAmQcrNhcqUKZObw1x1TNWqVc1Tnhr0XLNmjRlz6v333xd9BUr6lKf1pGfm/TpmqA7gT0IAAQQQQAABBBJZQHvYFCxY0HxBPGfOHLHGVc/JJCMjw+76bnWFz+mYeNzP/Wc8thplRgABBBBAAIH8Esj3J0B9u8PnVyXi4bwtWrQwM8E///zz0qhRo7CLrBNLvfHGGzJ79mwpWrRo2MdzAAIIIIAAAggg4CYBHT7o9ttvN1Xq2rWr6FjpoaQhQ4bIsmXLpFSpUnLDDTeEckjc5uH+M26bjoIjgAACCCCAQIQF8v0J0AiXN65Pp8HggQMHmpfOXPrzzz+bG/D169fLwYMHRSeZOnnypJmltESJElKlShXRca305jU3QdO4xqLwCCCAAAIIIIBADgL65fDOnTtlypQp5l5pwIAB0r9/f0lLS8typA47pOOta16dOOnjjz+Ws846K0s+t23g/tNtLUp9EEAAAQQQQCA3AgRAc6MWgWPq1Kkj+iIhgAACCCCAAAIIhC+wf/9+ueOOOyQ9Pd0crOtPPPGEDB06VCpXrmyCm/qU57Zt28zToRoo9U3dunXzXfVbrlmzpixcuNBvmxtWuP90QytSBwQQQAABBBDIjUCuA6DDhw8P+O26FkKfaLTS+PHjZcGCBdZq0Pd27dqJvkgIIIAAAggggAACCOQkcOLECTPRZOZ8GhDdtGmTeWXeZ62fPn1a9uzZY61medfAKQkBBBBAAAEEEEDAPQK5DoC+/fbbISlMnTo1pHza5ZsAaEhUZEIAAQQQQAABBBJeIDk52QwXlB8QFSpUyI/Tck4EEEAAAQQQQACBKAnkOgAapfIm9GVPnTplZjq1EHSMUBICCCCAAAIIIJCIAuXKlZMtW7YkYtUdrTP3n45yczEEEEAAAQQQyCeBsAKgzZs3l2HDhuVLUVq1apUv53XTSX/99Vdp1qyZXSWPx2Mvs4AAAggggAACCCCAQKQFuP+MtCjnQwABBBBAAIFoCIQVAD3vvPNEXyQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAeBMIKgMZDhSgjAggggAACCCCAQGIJrFy5Un744QczEeeuXbsk1F4yHTt2FH2REEAAAQQQQAABBNwtQAA0jtq3UaNGsn379jgqMUVFAAEEEEAAAQTyT+DQoUMyYMAAGTdunGRkZIR9IZ3siABo9mzcf2bvw14EEEAAAQQQiA8BAqDx0U6mlCkpKVK+fPk4KjFFRQABBBBAAAEE8k/goYcekrFjx+bfBTizcP/JhwABBBBAAAEE3CBAANQNrZjPddCnTmfPnh1yd7LsirN3716zOzdPaWR3XvYhgAACCCCAQGIJbNy4Ud5880270j169JBrr71WypYtK4UKFbK3Z7dQqVKl7HazL4oC3H9GEZ9LI4AAAggg4EIBAqAubNRIV6l3794yZcqUiJ5269atET0fJ0MAAQQQQACBxBLQ2cmtsT6HDBkiw4YNSywAl9eW+0+XNzDVQwABBBBAwGEBAqAOg2e+3O+//y76VOTRo0fNq0iRIlKyZElJS0uTMmXKiK5HO91zzz1SvHjxXI2tlbnsn3/+uezbt0/KlSuXeRfrCCCAAAIIIIBAyAJbtmyx89522232Mgs5C3D/mbMRORBAAAEEEEDAXQIEQB1uTx2sf/z48TJx4kRZtmyZ6HqwpGMuNWzYUFq0aGEG6O/QoYMkJSUFy55v26+88krRVyTSeeedZwKgqampkTgd50AAAQQQQACBBBVo2rSpqbneG1WuXDlBFUKrNvef3H+G9kkhFwIIIIAAAu4VSHZv1WKrZjt27JA+ffqYG/S+ffvK/Pnzsw1+aunT09Nl8eLFZnwrnaH03HPPlenTp8dWxSgNAggggAACCCAQBYFmzZqZHiraDX7JkiVRKEHsX5L7z9hvI0qIAAIIIIAAAs4I8ASoA87a5fuKK66QpUuX2lfTpxUqVqwo1apVM93B9YnIwoULm6Dn8ePH5eDBg6JduzZt2iQnTpwwx+kTo506dZLnn39e+vfvb5+LBQQQQAABBBBAINEEtKdM9+7d5fXXX5fBgwfLN998E5WeMrHqzv1nrLYM5UIAAQQQQACBaAgQAM1n9SNHjsjVV19tBz+bN28uAwcOlDZt2oQ0DuapU6dk4cKFptv8uHHjRNcHDBggderUEe0ST0IAAQQQQAABBBJV4JVXXpHdu3fLxx9/bIbrGTVqlOkxk6geVr25/7QkeEcAAQQQQAABBP4nQAA0nz8JekOu3d01devWzYz9mZwc+sgDBQsWlJYtW5pX586d5dprrzVB0Icffljat28v4Zwrn6vK6RFAAAEEEEAAAUcFChQoIO+//76sWLFCZs6cKY0aNZJixYpJ9erVTff4nApzxx13iL7clrj/dFuLUh8EEEAAAQQQyKsAAdC8CuZw/Lx580wOHb9TJz/KS8BSn/jUJxv+/ve/mydKN2zYIGeffXYOJWA3AggggAACCCDgTgEdNkjHWNdhgqykTz8uX77cWs32Xb9MdmPi/tONrUqdEEAAAQQQQCAvAqE/ipiXqyTwsd9//72p/TXXXCP6NGdeU5cuXexTrF692l5mAQEEEEAAAQQQSDSBrl27yrvvvpto1c6xvtx/5khEBgQQQAABBBBIMAGeAM3nBt+6dau5QtWqVSNypTJlyphAqo4FeuzYsYick5MggAACCCCAAALxJqBPf37xxRem2CVLlpQhQ4aIfuFcqVIlKVSoUEjV0YmU3Ji4/3Rjq1InBBBAAAEEEMiLAE+A5kUvhGOtLurWOKAhHJJtFu3SpMFPTU2aNMk2LzsRQAABBBBAAAG3Cui91YkTJ0z1xowZIw8++KDUrVtX0tLSpEiRIiG93BoA5f7TrZ966oUAAggggAACuRUgAJpbuRCPa9q0qck5adIk+fbbb0M8KnC2/fv3y/333292li5dWmrUqBE4I1sRQAABBBBAAAGXC6xZs8au4VVXXWUvsyDC/SefAgQQQAABBBBAwF+AAKi/R8TXHnnkEdNlXbtp6Szu+oTCyZMnw77OkiVLpF27dqLvmnr37h32OTgAAQQQQAABBBBwi8CFF15oV2Xfvn32Mgsi3H/yKUAAAQQQQAABBPwF3DnwkX8do7qmXZCGDx9uumUdOHDABC61i1br1q2lcePG5inO8uXLS2pqqumqlZ6eLhosPXjwoGzZskXWrl0rc+bM8ZvdVAOhQ4cOjWq9uDgCCCCAAAIIIBBNgQYNGkjZsmVl9+7d8vnnn8vdd98dzeLE1LW5/4yp5qAwCCCAAAIIIBADAgRAHWiEBx54QHTyoj59+piJiw4dOiTTpk0zr3Av3759e5k4caIkJ/Pwbrh25EcAAQQQQAAB9wgkJSXJvffeK08//bQ899xz0qpVK6lXr557KpjHmnD/mUdADkcAAQQQQAABVwkQRXOoOXv06CGbNm2SwYMHS4UKFcK6auHChU33+alTp8qMGTNEx/8kIYAAAggggAACiS7w1FNPyT333CPr16+XFi1ayJtvvikbN24Uj8eT6DSm/tx/8jFAAAEEEEAAAQT+J8AToA5+EsqVKyfPPPOMeenN+YIFC0QH8Nfu7to9Xp8MLViwoBQvXtzMYKrdl+rXry+NGjUy2xwsKpdCAAEEEEAAAQRiWkDvn/r162eCnfplsd5HaTBUkz4dWqJECXNflV0lBg0aJPpyc+L+082tS90QQAABBBBAIFQBAqChSkU4X/Xq1UVfJAQQQAABBBBAAIHwBY4dOybvvfdewAP1CVANkOaU9ByJlLj/TKTWpq4IIIAAAggg4CtAANRXg2UEEEAAAQQQQACBuBDQ8dCrVKmSp7KmpaXl6XgORgABBBBAAAEEEIgPAQKgMdhOOpD/okWLTMleffVV0a5LJAQQQAABBBBAAIE/BfT+aMuWLX9uYClPAtx/5omPgxFAAAEEEEAgxgWYBCkGG+i7776TSZMmmdeRI0disIQUCQEEEEAAAQQQQMBNAtx/uqk1qQsCCCCAAAIIZBYgAJpZhHUEEEAAAQQQQAABBBBAAAEEEEAAAQQQcI0AAVDXNCUVQQABBBBAAAEEEAhVYPv27bJs2bJQs5MPAQQQQAABBBBAII4FGAM0jhuPoiOAAAIIIIAAAgiIbNu2TT799FPZuXOnnDx5UjIyMvxYTp8+LfpKT0+X/fv3y9atW2XevHny6KOPSoMGDfzysoIAAggggAACCCDgPgECoO5rU2qEAAIIIIAAAggkjEDfvn3lrbfeklOnTiVMnakoAggggAACCCCAQHgCBEDD8yI3AggggAACCCCAQIwIvPfee/Laa6+FXZoCBQrI+eefLxdccEHYx3IAAggggAACCCCAQPwJEACNvzajxAgggAACCCCAQMILaDf3gQMH2g433nijtG/fXsqXLy833HCDHD16VJ566ilp2LCh7N27VxYuXCgTJkyQY8eOyWWXXSYzZ860j2UBAQQQQAABBBBAwN0CBEBjsH2Tk5MlJeV/TZOUlBSDJaRICCCAAAIIIIBAdAW2bNliAptait69e8sbb7xhF+jiiy+WL7/8Uo4cOSLXXXed2d6rVy/529/+Jh07dpRZs2bJhx9+KDfddJN9TKIvcP+Z6J8A6o8AAggggIC7BZgFPgbbd8qUKWYcKx3L6qyzzorBElIkBBBAAAEEEEAgugKrV6+2C/Dwww/by7rQsmVLs/7VV1/5bW/durV58lO/YB4wYICZEMkvQwKvcP+ZwI1P1RFAAAEEEEgAAQKgCdDIVBEBBBBAAAEEEHCbwNq1a02VihYtmuUL43r16pl9y5cvN7O/+9Zdx/3UbvE7duyQSZMm+e5iGQEEEEAAAQQQQMClAgRAXdqwVAsBBBBAAAEEEHCzgAY+NZUqVSpLNevUqWO2HT9+XHyfFLUy6pOgmpYuXWpt4h0BBBBAAAEEEEDAxQIEQF3cuFQNAQQQQAABBBBwq0DdunVN1Xbu3Ckej8evmrVr1xZrHPVffvnFb5+u6BOgmgiAGgb+QQABBBBAAAEEXC9AANT1TUwFEUAAAQQQQAAB9wlYAVAdM33OnDl+FdSnQytVqmS2/fTTT377dGXu3Llmm84OT0IAAQQQQAABBBBwvwABUPe3MTVEAAEEEEAAAQRcJ1CyZEmpXr26qVf//v3ljz/+8KtjkyZNzLqO87lv3z57X0ZGhnz22WdmvWbNmvZ2FhBAAAEEEEAAAQTcK0AA1L1tS80QQAABBBBAAAFXC7z88sumfkuWLBGd+KhPnz52fW+//XazvHXrVunYsaNMnjxZZs+eLV26dJHdu3ebfVZXePsgFhBAAAEEEEAAAQRcKUAA1JXNSqUQQAABBBBAAAH3C3Tq1Em6d+9uKnrgwAGZMmWKXenrrrtOdCxQTfPmzRNdv+yyy0wgVLelpaVJv379dJGEAAIIIIAAAggg4HIBAqAub2CqhwACCCCAAAIIuFngjTfeEH0StFatWuLbpT05OVm++OILsWaE9zVITU2VMWPGyJlnnum7mWUEEEAAAQQQQAABlwqkuLReVAsBBBBAAAEEEEAgAQR0wiN9kvO+++6TNWvW+NW4Ro0a5unPCRMmmO7v2vVdxwa95557pH79+n55WUEAAQQQQAABBBBwrwABUPe2LTVDAAEEEEAAAQQSRiApKSng055lypQRnSRJXyQEEEAAAQQQQACBxBSgC3xitju1RgABBBBAAAEEEEAAAQQQQAABBBBAICEECIAmRDNTSQQQQAABBBBAAAEEEEAAAQQQQAABBBJTgC7widnu1BoBBBBAAAEEEHC1wI4dO2TixImyatUq8Xg8cs4550i3bt2kcuXKrq43lUMAAQQQQAABBBDIKkAANKsJWxBAAAEEEEAAAQRiUCA9PV0++ugjef/992XZsmWyePFiKVeuXJaSvvDCCzJkyBA5fvy4377BgwdLnz59ZOTIkVKgQAG/fawggAACCCCAAAIIuFeAANCUkzsAAEAASURBVKh725aaIYAAAggggAACrhHYvHmzdOjQQZYvX27Xac+ePVkCoM8995w89NBDdh7fhZMnT8qLL75oZoufPHkyQVBfHJYRQAABBBBAAAEXCzAGqIsbl6ohgAACCCCAAAJuENi9e7e0bNnSL/ip9dq/f79f9dasWSOPPfaYva1WrVpm/YMPPjDd31NTU82+adOmySuvvGLnYwEBBBBAAAEEEEDA3QIEQN3dvtQOAQQQQAABBBCIewHtur5161ZTjxIlSsjLL78sGzZskAsuuMCvbk8++aToU56aGjduLEuWLJGnn35abrrpJvnwww9lwoQJdv7HH39cDh48aK+zgAACCCCAAAIIIOBeAQKg7m1baoYAAggggAACCMS9gE5mNHbsWFOPUqVKycKFC6Vfv35SvXp1v7odOXJEtFu7lUaNGiXFihWzVs17ly5dpGfPnmb58OHDok+CkhBAAAEEEEAAAQTcL0AA1P1tTA0RQAABBBBAAIG4FZgxY4ZkZGSY8g8YMEDq1q0bsC4zZ86Uo0ePmn0aHG3Tpk3AfD169LC3f/bZZ/YyCwgggAACCCCAAALuFSAA6t62pWYIIIAAAggggEDcC8ydO9euw2233WYvZ1745ptv7E3XXHONvZx5oX79+vamjRs32sssIIAAAggggAACCLhXgACoe9uWmiGAAAIIIIAAAnEvoF3gNaWkpEjlypWD1sc3AHr55ZcHzVe6dGmpWLGi2W+dO2hmdiCAAAIIIIAAAgi4QoAAqCuakUoggAACCCCAAALuFNi3b5+pWNmyZaVAgQIBK7lr1y5ZtmyZ2ZecnCytW7cOmM/aeOzYMbNYqFAhaxPvCCCAAAIIIIAAAi4WIADq4salaggggAACCCCAQLwL1KxZ01Rh79694vF4AlZHx/+09p133nmikyUFSzp7/P79+81uDaqSEEAAAQQQQAABBNwvQADU/W1MDRFAAAEE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)
[Figure 1.1. Model k]
diagnostics(mod_lamda, testit = FALSE)![[Figure 1.2. Model `lamda`]](data:image/png;base64,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)
[Figure 1.2. Model lamda]
diagnostics(mod_c, testit = FALSE)![[Figure 1.3. Model `c`]](data:image/png;base64,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)
[Figure 1.3. Model c]
diagnostics(mod_alpha, testit = FALSE)![[Figure 1.4. Model `alpha`]](data:image/png;base64,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)
[Figure 1.4. Model alpha]
diagnostics(mod_omega, testit = FALSE)![[Figure 1.5. Model `omega`]](data:image/png;base64,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)
[Figure 1.5. Model omega]
diagnostics(mod_miu, testit = FALSE)![[Figure 1.6. Model `miu`]](data:image/png;base64,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)
[Figure 1.6. Model miu]
Discussion
See project Homepage.
Appendix
Helper Functions
get_bp_decision = function(model, alpha) {
decide = unname(bptest(model)$p.value < alpha)
ifelse(decide, "Reject", "Fail to Reject")
}
get_bp_pval = function(model) {
bptest(model)$p.value
}
get_sw_decision = function(model, alpha) {
decide = unname(shapiro.test(resid(model))$p.value < alpha)
ifelse(decide, "Reject", "Fail to Reject")
}
get_sw_pval = function(model) {
shapiro.test(resid(model))$p.value
}
get_num_params = function(model) {
length(coef(model))
}
get_loocv_rmse = function(model, is_log, k) {
ifelse(
is_log,
sqrt(mean(na.omit(((data_trn[, k] - exp(fitted(model))) / (1 - hatvalues(model))) ^ 2))),
sqrt(mean((resid(model) / (1 - hatvalues(model))) ^ 2))
)
}
get_adj_r2 = function(model) {
summary(model)$adj.r.squared
}
test_mod = function(model, is_log = FALSE, k = 1){
c(loocv_rmse = get_loocv_rmse(model, is_log, k),
adj_r2 = get_adj_r2(model),
bp_pval = get_bp_pval(model),
sw_pval = get_sw_pval(model),
num_params = get_num_params(model),
test_rmse = get_test_rmse(model, k),
perc_err = get_perc_err(model, k))
}
diagnostics = function(model, pcol = "grey", lcol = "dodgerblue", alpha = 0.05, plotit = TRUE, testit = TRUE){
if (plotit){
par(mfrow = c(1, 2), pty="s")
plot(fitted(model), resid(model), col = "grey", pch = 20,
xlab = "Fitted", ylab = "Residual",
main = "Fitted versus Residuals")
abline(h = 0, col = "darkorange", lwd = 2)
qqnorm(resid(model), col = pcol)
qqline(resid(model), col = lcol, lwd = 2)
}
if (testit){
list(p_val = shapiro.test(resid(model))$p,
decision = ifelse(test = shapiro.test(resid(model))$p < alpha,
yes = "Reject", no = "Fail to Reject"))
}
}
get_test_rmse = function(model, k) {
sqrt(mean((data_tst[, k] - predict(model, newdata = data_tst))^ 2))
}
get_perc_err = function(model, k) {
actual = data_tst[, k]
predicted = predict(model, newdata = data_tst)
100 * mean((abs(actual - predicted)) / actual)
}