Tax Moral in the Haredi Community - V2
Racheli Brecher
2024-09-22
library(MASS)
library(AER)
library(broom)
library(knitr)
library(stargazer)
library(sjPlot)
library(ggplot2)
library(ggpubr)
library(gt)
library(olsrr)
library(arm)
library(coefplot)
library(effects)
library(vtable)
library(flextable)
library(DT)
library(tidyverse)
library(reshape2)
library(corrplot)
library(interflex)
source("wl_graph.R")
source("wl_utils.R")
source("wl_reg_report.R")This document summarizes the quantitative analysis for Brecher research ‘Tax Moral in the Haredi Community’. The research is uses data collected from a quastionaire distributed in Haredi population - see: https://docs.google.com/forms/d/1KdVid-ExT86wjs-nuCrakBeccwNgaDCSu3nhPjIHYqY/edit?ts=648b12b5 .
The analysis was developed using R version 4.1.3 (2022-03-10) and RStudio 2022.07.2+576 release for Windows.
To view the code - click on the ‘Code’ buttons on the right side of the page.
1 Research data
Note: All ordered categorical variables with 5 levels or more are treated as as numeric due to the ‘small number of samples’ limitation.
raw_data=read.csv("tax_data_150223.csv",sep=";")
names(raw_data)[1] <- "id" #fix name of id column - move between pc and mac
# Dependent Variables
raw_data$tax_moral_a <- factor(raw_data$tax_morale_a, ordered = TRUE)
raw_data$tax_moral_b <- factor(raw_data$tax_morale_b, ordered = TRUE)
raw_data$tax_moral_N <- raw_data$tax_morale
raw_data$tax_moral <- factor(raw_data$tax_morale, ordered = TRUE)
# Nominal Covariates
raw_data$tax_returns <- factor(raw_data$tax_returns)
levels(raw_data$tax_returns) <- c("No", "Low", "Yes", "High")
raw_data$charity <- factor(raw_data$charity)
levels(raw_data$charity) <- c("Below 10%", "Around 10%", "Above 10%")
raw_data$tax_social <- factor(raw_data$tax_social)
levels(raw_data$tax_social) <- c("No", "Low", "Yes")
raw_data$gender <- factor(raw_data$isMale)
levels(raw_data$gender) <- c("Female", "Male")
# Create Generation factor field (3 levels and 5 levels)
raw_data$gen3_N <- as.numeric(cut(raw_data$age, breaks = c(20, 30, 40, 67)))
raw_data$gen3 <- factor(cut(raw_data$age, breaks = c(20, 30, 40, 67)),
labels = c("20-30", "30-40", "40-67"))
raw_data$gen7 <- factor(cut(raw_data$age, breaks = c(20, 25, 30, 35, 40, 45, 50, 67)),
labels = c("20-25", "25-30", "30-35", "35-40", "40-45", "45-50", "50-67"))
dependents <- c("Tax Moral" = "tax_moral", #Ordinal (1-6)
"Tax Moral (Numeric)" = "tax_moral_N", #Numeric (1-6)
"Tax Moral A (1:4:1)" = "tax_moral_a", #Ordinal (1-3)
"Tax Moral B (2:2:2)" = "tax_moral_b") #Ordinal (1-3)
cov_full <- c("Strictness" = "strictness", #Numeric (1-7)
"Modernity" = "modernity", #Numeric (1-7)
"Belonging" = "belonging", #Numeric (1-6)
"Egality" = "egality", #Numeric (1-6)
"Shame" = "shame", #Numeric (1-6)
"Tax Returns" = "tax_returns", #Nominal (1-4)
"Social Safety" = "tax_social", #*Nominal (1-4)
"Income Level" = "income_current", #Numeric (1-5)
"Gender" = "gender", #Binary (0/1)
"Age" = "age", #Numeric (0-120)
"Generation(3)" = "gen3", #Ordinal (1-3)
"Generation(3)N" = "gen3_N", #Numeric (1-3)
"Generation(7)" = "gen7" #Ordinal (1-7)
)
cov_full_1 <- wl_setdiff(cov_full, c("gen3", "gen3_N", "gen7"))
# remove factors from VIF calculation
cov_vif <- wl_setdiff(cov_full_1, c("tax_returns", "charity", "tax_social"))
# Handle colinearity between Strictness and Modernity
cov_a <- wl_setdiff(cov_full_1, c("strictness"))
cov_b <- wl_setdiff(cov_full_1, c("modernity"))
data <- raw_data[, c(dependents, cov_full)]
data <- data[complete.cases(data), ]1.1 Descriptive tables
d <- wl_descriptive(data, vars=c(dependents, cov_full_1),
labs=c(names(dependents), names(cov_full_1)), display=FALSE)
save_as_html(d, path = "out/descriptive.html")
dVariable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
Tax Moral | 88 | ||||||
... 1 | 7 | 8% | |||||
... 2 | 15 | 17% | |||||
... 3 | 15 | 17% | |||||
... 4 | 22 | 25% | |||||
... 5 | 12 | 14% | |||||
... 6 | 17 | 19% | |||||
Tax Moral (Numeric) | 88 | 3.8 | 1.6 | 1 | 2.8 | 5 | 6 |
Tax Moral A (1:4:1) | 88 | ||||||
... 1 | 7 | 8% | |||||
... 2 | 64 | 73% | |||||
... 3 | 17 | 19% | |||||
Tax Moral B (2:2:2) | 88 | ||||||
... 1 | 22 | 25% | |||||
... 2 | 37 | 42% | |||||
... 3 | 29 | 33% | |||||
Strictness | 88 | 3.5 | 1.6 | 1 | 2 | 4 | 7 |
Modernity | 88 | 4.2 | 1.7 | 1 | 3 | 6 | 7 |
Belonging | 88 | 3.9 | 1.6 | 1 | 3 | 5 | 6 |
Egality | 88 | 2.4 | 1.4 | 1 | 1 | 3 | 6 |
Shame | 88 | 2.8 | 1.9 | 1 | 1 | 4.2 | 6 |
Tax Returns | 88 | ||||||
... No | 24 | 27% | |||||
... Low | 33 | 38% | |||||
... Yes | 20 | 23% | |||||
... High | 11 | 12% | |||||
Social Safety | 88 | ||||||
... No | 36 | 41% | |||||
... Low | 32 | 36% | |||||
... Yes | 20 | 23% | |||||
Income Level | 88 | 2.3 | 0.87 | 1 | 2 | 3 | 5 |
Gender | 88 | ||||||
... Female | 37 | 42% | |||||
... Male | 51 | 58% | |||||
Age | 88 | 36 | 8.2 | 21 | 31 | 41 | 67 |
1.2 Correlation Matrix
ndata <- data[, c(dependents, cov_full_1)]
ndata <- ndata[, sapply(ndata, is.numeric)] #get only the numeric data
cdata <- cor(ndata)
rownames(cdata) <- colnames(cdata) <- c(names(dependents[2]),
names(cov_full[1:5]),
names(cov_full[8]),
names(cov_full[10]))
png("out/corrplot.png", width = 1600, height = 1600, res = 300)
corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", tl.cex = 0.8, diag=FALSE)
dev.off()
#> png
#> 2
corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE)
# Create a grayscale color palette
grayscale_palette <-
colorRampPalette(c("#FFFFFF", "#FAFAFA", "#F5F5F5", "#EEEEEE", "#E0E0E0",
"#D9D9D9", "#CFCFCF", "#BFBFBF", "#9E9E9E", "#757575"))(10)
png("out/corrplot_bw.png", width = 1600, height = 1600, res = 300)
corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE,
col = grayscale_palette, tl.col = "black")
dev.off()
#> png
#> 2
corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE,
col = grayscale_palette, tl.col = "black")
1.3 Descriptive Plots
1.3.1 Tax Moral & Strictness
wl_plot_xy (data, cov_full[1], dependents[2])
ggsave("out/tax_moral_and_strictness.png", height=3)1.3.2 Tax Moral & Modernity
wl_plot_xy (data, cov_full[2], dependents[2])
ggsave("out/tax_moral_and_modernity.png", height=3)1.3.3 Tax Moral & Belonging
wl_plot_xy (data, cov_full[3], dependents[2])
ggsave("out/tax_moral_and_belongings.png", height=3)1.3.4 Tax Moral & Egality
wl_plot_xy (data, cov_full[4], dependents[2])
ggsave("out/tax_moral_and_egality.png", height=3)1.3.5 Tax Moral & Shame
wl_plot_xy (data, cov_full[5], dependents[2])
ggsave("out/tax_moral_and_shame.png", height=3)1.4 VIF (Multi-Collinearity) Test
The below VIF test table indicates multi-colinearity between strictness and modernity.
mvif <- ols_regression(data, "tax_moral_N", cov_vif)
vif_table (mvif, labs=names(cov_vif))| Tolerance | VIF | |
|---|---|---|
| Strictness | 0.4816570 | 2.076166 |
| Modernity | 0.4569115 | 2.188608 |
| Belonging | 0.7037359 | 1.420988 |
| Egality | 0.7077823 | 1.412864 |
| Shame | 0.7525072 | 1.328891 |
| Income Level | 0.9128725 | 1.095443 |
| Gender | 0.7730160 | 1.293634 |
| Age | 0.7465754 | 1.339449 |
| Tolerance: Percent of variance in the predictor that cannot be accounted for by other predictors | ||
The cor function calculates the correlation coefficient between two variables. The correlation coefficient measures the strength and direction of the linear relationship between two variables. A value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. As can be seen from the bewlow results we have ~70% correlation in the same direction between the two variables.
#Strictness and Modernity correlation
cor(data$strictness, data$modernity)
#> [1] 0.70095642 Regression models
The dependent variable, Tax Moral, is an ordered categorical variable with 6 levels. To analyze the effects of the independent variables and control for additional covariates we use Proportional odds logistic regression (POLR). We also use as a reference Ordinary Least Squares (OLS) regression. In both types of regressions we calculate two models: one with the independent variable Strictness and the other with Modernity (we can not use both due to multi-collinearity).
# Main Regression Models
m1 <- polr_regression(data, "tax_moral", cov_a)
m2 <- polr_regression(data, "tax_moral", cov_b)
m3 <- ols_regression(data, "tax_moral_N", cov_a)
m4 <- ols_regression(data, "tax_moral_N", cov_b)
main_models <- list(m1,m2,m3,m4)
# Regression Table
cov_labels_ab <- c(names(cov_a[1]),
names(cov_b[1]),
names(cov_a[2]),
names(cov_a[3]),
names(cov_a[4]),
paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[2],"]"),
paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[3],"]"),
paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[4],"]"),
paste0(names(cov_a[6])," [", levels(data[,cov_a[7]])[2],"]"),
paste0(names(cov_a[6])," [", levels(data[,cov_a[7]])[3],"]"),
names(cov_a[7]),
paste0(names(cov_a[8])," [", levels(data[,cov_a[9]])[2],"]"),
names(cov_a[9])
)
wl_stargazer (main_models, "tax_moral_reg", cov_labels = cov_labels_ab, coef_exp = TRUE)
#>
#> ----------------------------------------------------------------
#> ordered OLS
#> logistic
#> (1) (2) (3) (4)
#> ----------------------------------------------------------------
#> Modernity 0.907 0.959
#> (0.139) (0.090)
#> Strictness 0.802 0.886
#> (0.146) (0.096)
#> Belonging 2.078*** 2.122*** 1.598*** 1.621***
#> (0.169) (0.167) (0.102) (0.100)
#> Egality 1.463* 1.472* 1.204 1.207
#> (0.190) (0.187) (0.117) (0.115)
#> Shame 1.245 1.249 1.128 1.129
#> (0.132) (0.133) (0.087) (0.086)
#> Tax Returns [Low] 2.251 2.560 1.581 1.700
#> (0.525) (0.535) (0.339) (0.339)
#> Tax Returns [Yes] 1.383 1.544 1.262 1.368
#> (0.601) (0.605) (0.403) (0.405)
#> Tax Returns [High] 1.970 1.942 1.510 1.534
#> (0.802) (0.805) (0.509) (0.504)
#> Social Safety [] 1.016 1.191 0.921 0.993
#> (0.488) (0.500) (0.328) (0.331)
#> Social Safety [] 1.296 1.508 1.203 1.276
#> (0.620) (0.634) (0.397) (0.396)
#> Income Level 0.868 0.843 0.944 0.940
#> (0.255) (0.257) (0.162) (0.160)
#> Gender [] 1.888 2.143 1.408 1.491
#> (0.455) (0.470) (0.303) (0.304)
#> Age 0.918** 0.911** 0.945** 0.942**
#> (0.028) (0.029) (0.018) (0.019)
#> N 88 88 88 88
#> R2 0.473 0.483
#> Adjusted R2 0.389 0.400
#> ----------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.#>
#> ----------------------------------------------------------------
#> ordered OLS
#> logistic
#> (1) (2) (3) (4)
#> ----------------------------------------------------------------
#> Modernity 0.907 0.959
#> (0.139) (0.090)
#> Strictness 0.802 0.886
#> (0.146) (0.096)
#> Belonging 2.078*** 2.122*** 1.598*** 1.621***
#> (0.169) (0.167) (0.102) (0.100)
#> Egality 1.463* 1.472* 1.204 1.207
#> (0.190) (0.187) (0.117) (0.115)
#> Shame 1.245 1.249 1.128 1.129
#> (0.132) (0.133) (0.087) (0.086)
#> Tax Returns [Low] 2.251 2.560 1.581 1.700
#> (0.525) (0.535) (0.339) (0.339)
#> Tax Returns [Yes] 1.383 1.544 1.262 1.368
#> (0.601) (0.605) (0.403) (0.405)
#> Tax Returns [High] 1.970 1.942 1.510 1.534
#> (0.802) (0.805) (0.509) (0.504)
#> Social Safety [] 1.016 1.191 0.921 0.993
#> (0.488) (0.500) (0.328) (0.331)
#> Social Safety [] 1.296 1.508 1.203 1.276
#> (0.620) (0.634) (0.397) (0.396)
#> Income Level 0.868 0.843 0.944 0.940
#> (0.255) (0.257) (0.162) (0.160)
#> Gender [] 1.888 2.143 1.408 1.491
#> (0.455) (0.470) (0.303) (0.304)
#> Age 0.918** 0.911** 0.945** 0.942**
#> (0.028) (0.029) (0.018) (0.019)
#> N 88 88 88 88
#> R2 0.473 0.483
#> Adjusted R2 0.389 0.400
#> ----------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.2.1 Interpretations
2.1.1 Model 1 Interpretation
#> [1] "Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral.<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral to the next one by 107.8%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral to the next one by 46.33%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral to the next one by 8.179%, assuming all other covariates stay constant."2.1.2 Model 2 Interpretation
#> [1] "Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral.<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral to the next one by 112.2%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral to the next one by 47.2%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral to the next one by 8.852%, assuming all other covariates stay constant."2.1.3 Model 3 Interpretation
#> [1] "OLS Linear regression was used to analyze the effects on Tax Moral (Numeric).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases Tax Moral (Numeric) by 0.4687 units, assuming all other covariates stay constant. This represents a change of more than 0.2992 standard deviations (SD[Tax Moral (Numeric)]= 1.566).<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases Tax Moral (Numeric) by 0.05634 units, assuming all other covariates stay constant. This represents a change of more than 0.03597 standard deviations (SD[Tax Moral (Numeric)]= 1.566)."2.1.4 Model 4 INterpretation
#> [1] "OLS Linear regression was used to analyze the effects on Tax Moral (Numeric).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases Tax Moral (Numeric) by 0.4828 units, assuming all other covariates stay constant. This represents a change of more than 0.3082 standard deviations (SD[Tax Moral (Numeric)]= 1.566).<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases Tax Moral (Numeric) by 0.06001 units, assuming all other covariates stay constant. This represents a change of more than 0.03831 standard deviations (SD[Tax Moral (Numeric)]= 1.566)."2.2 Coefficiant plots
The standardized coefficient plots enable to compare the magnitude of the effects. Belonging has the highest positive statistically significant magnitude and age has the highest negative statistically significant magnitude.
Note: The intercepts at the top of the tables represent the probability boundaries between Tax Moral levels.
2.2.1 Model 1 - POLR, Strictness
cov_labels_a <- cov_labels_ab[c(1,3:length(cov_labels_ab))]
cov_labels_b <- cov_labels_ab[2:length(cov_labels_ab)]
cov_labels_i = c("Intercept 5|6","Intercept 4|5","Intercept 3|4","Intercept 2|3","Intercept 1|2")
png("out/model1.png", width = 1200, height = 800)
arm::coefplot(m1, main="",
varnames = c(cov_labels_a, rev(cov_labels_i)),
mar = c(5, 12, 4, 2) + 0.1,
cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
dev.off()
#> png
#> 2 arm::coefplot(m1, main="",
varnames = c(cov_labels_a, rev(cov_labels_i)),
mar = c(5, 12, 4, 2) + 0.1,
cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
2.2.2 Model 2 - POLR, Modernity
png("out/model2.png", width = 1200, height = 800)
arm::coefplot(m2, main="",
varnames = c(cov_labels_a, rev(cov_labels_i)),
mar = c(5, 12, 4, 2) + 0.1,
cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
dev.off()
#> png
#> 2 arm::coefplot(m2, main="",
varnames = c(cov_labels_a, rev(cov_labels_i)),
mar = c(5, 12, 4, 2) + 0.1,
cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
3 Effect Analysis
Make sure to include the data in the model so Effect will work
m1$call$data <- data3.1 Belonging effect on Tax Moral probabilities
png("out/age_effect.png", width = 1200, height = 1200)
plot(Effect(focal.predictors = c("belonging"), m1), rug = FALSE, cex=3.0)
dev.off()
#> png
#> 2 plot(Effect(focal.predictors = c("belonging"), m1), rug = FALSE, cex=3.0)
3.2 Age effect on Tax Moral probabilities
png("out/age_effect.png", width = 1200, height = 1200)
plot(Effect(focal.predictors = c("age"), m1), rug = FALSE, cex=3.0)
dev.off()
#> png
#> 2 plot(Effect(focal.predictors = c("age"), m1), rug = FALSE, cex=3.0)
3.3 Egality effect on Tax Moral probabilities
png("out/egality_effect.png", width = 1200, height = 1200)
plot(Effect(focal.predictors = c("egality"), m1), rug = FALSE, cex=3.0)
dev.off()
#> png
#> 2 plot(Effect(focal.predictors = c("egality"), m1), rug = FALSE, cex=3.0)
3.4 Gender effect on Tax Moral probabilities
png("out/gender_effect.png", width = 1200, height = 1200)
plot(Effect(focal.predictors = c("gender"), m1), rug = FALSE, cex=3.0)
dev.off()
#> png
#> 2 plot(Effect(focal.predictors = c("gender"), m1), rug = FALSE, cex=3.0)
3.5 Income effect om on Tax Moral probabilities
png("out/income_effect.png", width = 1200, height = 1200)
plot(Effect(focal.predictors = c("income_current"), m1), rug = FALSE, cex=3.0)
dev.off()
#> png
#> 2 plot(Effect(focal.predictors = c("income_current"), m1), rug = FALSE, cex=3.0)
4 Interactions
As belonging seems to be the highest magnitude effect, we test it’s interactions with Age, Gender and Income.
cov_int_a <- c(cov_a, "belonging*age")
mi_age <- polr_regression(data, "tax_moral", cov_int_a)
cov_int_a <- c(cov_a, "belonging*egality")
mi_egality<- polr_regression(data, "tax_moral", cov_int_a)
cov_int_a <- c(cov_a, "egality*age")
mi_age_egality<- polr_regression(data, "tax_moral", cov_int_a)
cov_int_a <- c(cov_a, "belonging*gender")
mi_gender <- polr_regression(data, "tax_moral", cov_int_a)
cov_int_a <- c(cov_a, "belonging*income_current")
mi_income <- polr_regression(data, "tax_moral", cov_int_a)
int_models <- list(mi_age, mi_egality, mi_age_egality)
names(int_models) <- c("Belonging*Age", "Belonging*Egality", "Egality*Age")
cov_labels_ia <- c(cov_labels_a, names(int_models))
wl_stargazer (int_models, "interaction_reg", cov_labels=cov_labels_ia, coef_exp=TRUE)
#>
#> ---------------------------------------------------------------
#> Belonging*Age Belonging*Egality Egality*Age
#> (1) (2) (3)
#> ---------------------------------------------------------------
#> Modernity 0.908 0.896 0.886
#> (0.141) (0.140) (0.141)
#> Belonging 8.276** 2.532** 2.096***
#> (0.681) (0.295) (0.173)
#> Egality 1.404 2.224 5.732
#> (0.194) (0.534) (0.940)
#> Shame 1.269 1.245 1.276
#> (0.133) (0.132) (0.135)
#> Tax Returns [Low] 2.285 2.274 2.053
#> (0.532) (0.528) (0.530)
#> Tax Returns [Yes] 1.345 1.313 1.148
#> (0.601) (0.606) (0.615)
#> Tax Returns [High] 1.793 2.185 1.626
#> (0.820) (0.808) (0.822)
#> Social Safety [] 1.162 0.969 0.999
#> (0.500) (0.492) (0.492)
#> Social Safety [] 1.455 1.275 1.459
#> (0.624) (0.617) (0.623)
#> Income Level 0.836 0.847 0.852
#> (0.261) (0.256) (0.256)
#> Gender [] 1.921 1.945 1.907
#> (0.462) (0.456) (0.459)
#> Age 1.036 0.917** 0.992
#> (0.063) (0.029) (0.058)
#> Belonging*Age 0.963*
#> (0.018)
#> Belonging*Egality 0.912
#> (0.110)
#> Egality*Age 0.962
#> (0.026)
#> N 88 88 88
#> ---------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.#>
#> ---------------------------------------------------------------
#> Belonging*Age Belonging*Egality Egality*Age
#> (1) (2) (3)
#> ---------------------------------------------------------------
#> Modernity 0.908 0.896 0.886
#> (0.141) (0.140) (0.141)
#> Belonging 8.276** 2.532** 2.096***
#> (0.681) (0.295) (0.173)
#> Egality 1.404 2.224 5.732
#> (0.194) (0.534) (0.940)
#> Shame 1.269 1.245 1.276
#> (0.133) (0.132) (0.135)
#> Tax Returns [Low] 2.285 2.274 2.053
#> (0.532) (0.528) (0.530)
#> Tax Returns [Yes] 1.345 1.313 1.148
#> (0.601) (0.606) (0.615)
#> Tax Returns [High] 1.793 2.185 1.626
#> (0.820) (0.808) (0.822)
#> Social Safety [] 1.162 0.969 0.999
#> (0.500) (0.492) (0.492)
#> Social Safety [] 1.455 1.275 1.459
#> (0.624) (0.617) (0.623)
#> Income Level 0.836 0.847 0.852
#> (0.261) (0.256) (0.256)
#> Gender [] 1.921 1.945 1.907
#> (0.462) (0.456) (0.459)
#> Age 1.036 0.917** 0.992
#> (0.063) (0.029) (0.058)
#> Belonging*Age 0.963*
#> (0.018)
#> Belonging*Egality 0.912
#> (0.110)
#> Egality*Age 0.962
#> (0.026)
#> N 88 88 88
#> ---------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.4.1 Interaction Plots
4.1.1 Age & Belonging Interaction Plot
sjPlot::plot_model(mi_age, type = "int")
ggsave("out/age_interaction.png")4.1.2 egality & Belonging Interaction Plot
sjPlot::plot_model(mi_egality, type = "int")
ggsave("out/egality_interaction.png")4.1.3 egality & age Interaction Plot
sjPlot::plot_model(mi_age_egality, type = "int")
ggsave("out/egality_interaction.png")4.1.4 Gender & Belonging Interaction Plot
sjPlot::plot_model(mi_gender, type = "int")
ggsave("out/gender_interaction.png")4.1.5 Income & Belonging Interaction Plot
sjPlot::plot_model(mi_income, type = "int")
ggsave("out/income_interaction.png")5 Robustness
We use two different coding schemes to test the robustness of our results. The first scheme (1:4:1) emphasize the edges of Tax Moral scale, re-coding level 1 as 1, levels 2-5 as 2, and level 6 as 3. The second scheme (2:2:2) reduces the granularity of Tax Moral levels, re-coding levels 1-2 as 1, levels 3-4 as 2, and levels 5-6 as 3.
# Robustness Models with different tax_moral coding
m1_ra <- polr_regression(data, "tax_moral_a", cov_a)
m2_ra <- polr_regression(data, "tax_moral_a", cov_b)
m3_rb <- polr_regression(data, "tax_moral_b", cov_a)
m4_rb <- polr_regression(data, "tax_moral_b", cov_b)
coding_models <- list(m1_ra, m2_ra, m3_rb, m4_rb)
names(coding_models) <- c("1:4:1 Coding", "1:4:1 Coding",
"2:2:2 Coding", "2:2:2 Coding")
wl_stargazer (coding_models, "coding", cov_labels = cov_labels_ab, coef_exp = TRUE)
#>
#> ----------------------------------------------------------------------
#> 1:4:1 Coding 1:4:1 Coding 2:2:2 Coding 2:2:2 Coding
#> (1) (2) (3) (4)
#> ----------------------------------------------------------------------
#> Modernity 0.781 0.982
#> (0.206) (0.150)
#> Strictness 0.867 0.804
#> (0.201) (0.170)
#> Belonging 2.974*** 2.817*** 2.091*** 2.196***
#> (0.289) (0.282) (0.192) (0.194)
#> Egality 1.113 1.079 1.355 1.386
#> (0.242) (0.238) (0.207) (0.205)
#> Shame 1.456* 1.482* 1.253 1.253
#> (0.190) (0.189) (0.143) (0.144)
#> Tax Returns [Low] 2.578 2.931 1.782 2.056
#> (0.746) (0.754) (0.567) (0.584)
#> Tax Returns [Yes] 1.439 1.355 1.193 1.433
#> (0.852) (0.855) (0.670) (0.679)
#> Tax Returns [High] 4.672 4.443 1.292 1.388
#> (1.067) (1.034) (0.857) (0.858)
#> Social Safety [] 1.234 1.206 0.821 0.996
#> (0.690) (0.696) (0.548) (0.566)
#> Social Safety [] 0.546 0.537 1.558 1.847
#> (0.867) (0.863) (0.670) (0.686)
#> Income Level 0.656 0.699 0.958 0.916
#> (0.352) (0.344) (0.272) (0.272)
#> Gender [] 1.383 1.436 1.211 1.410
#> (0.632) (0.634) (0.506) (0.522)
#> Age 0.921* 0.919* 0.902** 0.894**
#> (0.041) (0.041) (0.036) (0.037)
#> N 88 88 88 88
#> ----------------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.#>
#> ----------------------------------------------------------------------
#> 1:4:1 Coding 1:4:1 Coding 2:2:2 Coding 2:2:2 Coding
#> (1) (2) (3) (4)
#> ----------------------------------------------------------------------
#> Modernity 0.781 0.982
#> (0.206) (0.150)
#> Strictness 0.867 0.804
#> (0.201) (0.170)
#> Belonging 2.974*** 2.817*** 2.091*** 2.196***
#> (0.289) (0.282) (0.192) (0.194)
#> Egality 1.113 1.079 1.355 1.386
#> (0.242) (0.238) (0.207) (0.205)
#> Shame 1.456* 1.482* 1.253 1.253
#> (0.190) (0.189) (0.143) (0.144)
#> Tax Returns [Low] 2.578 2.931 1.782 2.056
#> (0.746) (0.754) (0.567) (0.584)
#> Tax Returns [Yes] 1.439 1.355 1.193 1.433
#> (0.852) (0.855) (0.670) (0.679)
#> Tax Returns [High] 4.672 4.443 1.292 1.388
#> (1.067) (1.034) (0.857) (0.858)
#> Social Safety [] 1.234 1.206 0.821 0.996
#> (0.690) (0.696) (0.548) (0.566)
#> Social Safety [] 0.546 0.537 1.558 1.847
#> (0.867) (0.863) (0.670) (0.686)
#> Income Level 0.656 0.699 0.958 0.916
#> (0.352) (0.344) (0.272) (0.272)
#> Gender [] 1.383 1.436 1.211 1.410
#> (0.632) (0.634) (0.506) (0.522)
#> Age 0.921* 0.919* 0.902** 0.894**
#> (0.041) (0.041) (0.036) (0.037)
#> N 88 88 88 88
#> ----------------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001
#> All estimates of oredered logistic models are exponentiated.5.1 Interpretation
5.1.1 ‘1:4:1 Coding’ - model 1
#> Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral A (1:4:1).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 197.4%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 11.33%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 7.874%, assuming all other covariates stay constant.5.1.2 ‘1:4:1 Coding’ - model 2
#> Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral A (1:4:1).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 181.7%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 7.894%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral A (1:4:1) to the next one by 8.101%, assuming all other covariates stay constant.5.1.3 ‘2:2:2 Coding’ - model 3
#> Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral B (2:2:2).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 109.1%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 35.48%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 9.769%, assuming all other covariates stay constant.5.1.4 ‘2:2:2 Coding’ - model 4
#> Ordinal Proportional Odds Logistic regression was used to analyze the effects on Tax Moral B (2:2:2).<br/>The Belonging covariate has statistically significant effect. An increase of 1 unit in Belonging increases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 119.6%, assuming all other covariates stay constant.<br/>The Egality covariate has statistically significant effect. An increase of 1 unit in Egality increases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 38.63%, assuming all other covariates stay constant.<br/>The Age covariate has statistically significant effect. An increase of 1 unit in Age decreases the odds to move from one category of Tax Moral B (2:2:2) to the next one by 10.57%, assuming all other covariates stay constant.6 Belonging and Generation Analysis
6.1 Descriptive
6.1.1 Generation (3 Levels)
# Calculate mean and standard error
grouped_data <- data %>%
group_by(gen3) %>%
summarise(
mean_tax_moral = mean(tax_moral_N),
count = n(),
se = sd(tax_moral_N) / sqrt(n()) # Standard error
)
# Sort the grouped data by gen3
grouped_data <- grouped_data %>%
arrange(gen3)
# Create the bar chart with error bars and display the SE value
ggplot(grouped_data, aes(x = gen3, y = mean_tax_moral)) +
geom_bar(stat = "identity", fill = "lightblue") +
geom_errorbar(aes(ymin = mean_tax_moral - se, ymax = mean_tax_moral + se),
width = 0.2) + # Add error bars
labs(title = "Average Tax Moral by Gen3",
x = "Gen3",
y = "Mean Tax Moral") +
geom_text(aes(label = paste0("n=", count, ", SE=", round(se, 2))),
vjust = -0.5,
hjust = 0.5)
ggsave("out/gen3_barchart.png")6.1.2 Generation (7 levels)
grouped_data <- data %>%
group_by(gen7) %>%
summarise(mean_tax_moral = mean(tax_moral_N), count = n())
# Sort the grouped data by gen7
grouped_data <- grouped_data %>%
arrange(gen7)
ggplot(grouped_data, aes(x = gen7, y = mean_tax_moral)) +
geom_bar(stat = "identity") +
labs(title = "Average Tax Moral by Gen7",
x = "Gen7",
y = "Mean Tax Moral") +
geom_text(aes(label = paste0("n=", count)),
vjust = -0.5,
hjust = 0.5)
6.2 Interaction (Regression)
6.2.1 Interaction (Regression)
cov_gen3 <- c(cov_a[1:8],"Generation(3)" = "gen3")
cov_gen3_N <- c(cov_a[1:8],"Generation(3)N" = "gen3_N")
cov_gen7 <- c(cov_a[1:8],"Generation(7)" = "gen7")
cov_int_gen3 <- c(cov_a, "belonging*gen3")
mi_gen3 <- ols_regression(data, "tax_moral_N", cov_int_gen3)
mi_gen3_F <- ols_regression(data, "tax_moral_N", cov_gen3)
mi_gen3_N <- ols_regression(data, "tax_moral_N", cov_gen3_N)
int_models <- list(mi_gen3_F, mi_gen3_N, mi_gen3)
names(int_models) <- c("Gen3 (Factor)", "Gen3 (Numeric)", "Belonging*Gen3")
cov_labels <- c(names(cov_full[2]),
names(cov_full[3]),
names(cov_full[4]),
names(cov_full[5]),
paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[2],"]"),
paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[3],"]"),
paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[4],"]"),
paste0(names(cov_full[7])," [", levels(data[,cov_full[7]])[2],"]"),
paste0(names(cov_full[7])," [", levels(data[,cov_full[7]])[3],"]"),
names(cov_full[8]),
paste0(names(cov_full[9])," [", levels(data[,cov_full[9]])[2],"]"),
names(cov_full[10]),
paste0(names(cov_full[11])," [", levels(data[,cov_full[11]])[2],"]"),
paste0(names(cov_full[11])," [", levels(data[,cov_full[11]])[3],"]"),
names(cov_full[12]),
paste0(names(int_models[3])," [", levels(data[,cov_full[11]])[2],"]"),
paste0(names(int_models[3])," [", levels(data[,cov_full[11]])[3],"]")
)
wl_stargazer (int_models, "interaction_reg_gen", cov_labels=cov_labels, coef_exp=FALSE)
#>
#> ------------------------------------------------------------------
#> Gen3 (Factor) Gen3 (Numeric) Belonging*Gen3
#> (1) (2) (3)
#> ------------------------------------------------------------------
#> Modernity -0.009 -0.038 -0.033
#> (0.092) (0.092) (0.087)
#> Belonging 0.437*** 0.466*** 0.622**
#> (0.105) (0.105) (0.197)
#> Egality 0.199 0.213 0.115
#> (0.118) (0.119) (0.112)
#> Shame 0.122 0.099 0.146
#> (0.089) (0.089) (0.083)
#> Tax Returns [Low] 0.659 0.508 0.416
#> (0.355) (0.349) (0.339)
#> Tax Returns [Yes] 0.341 0.165 0.279
#> (0.420) (0.413) (0.393)
#> Tax Returns [High] 0.570 0.377 0.517
#> (0.528) (0.524) (0.495)
#> Social Safety [Low] -0.096 -0.023 -0.085
#> (0.336) (0.338) (0.316)
#> Social Safety [Yes] 0.204 0.295 0.170
#> (0.405) (0.407) (0.383)
#> Income Level -0.010 -0.008 -0.021
#> (0.163) (0.165) (0.155)
#> Gender [Male] 0.208 0.315 0.287
#> (0.316) (0.314) (0.300)
#> Age -0.085*
#> (0.035)
#> Generation(3) [30-40] 0.048 0.638
#> (0.372) (0.931)
#> Generation(3) [40-67] -0.907* 3.351*
#> (0.435) (1.376)
#> Generation(3)N -0.482*
#> (0.220)
#> Belonging*Gen3 [30-40] -0.014
#> (0.215)
#> Belonging*Gen3 [40-67] -0.680*
#> (0.263)
#> N 88 88 88
#> R2 0.466 0.444 0.554
#> Adjusted R2 0.372 0.354 0.453
#> ------------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .001#>
#> ------------------------------------------------------------------
#> Gen3 (Factor) Gen3 (Numeric) Belonging*Gen3
#> (1) (2) (3)
#> ------------------------------------------------------------------
#> Modernity -0.009 -0.038 -0.033
#> (0.092) (0.092) (0.087)
#> Belonging 0.437*** 0.466*** 0.622**
#> (0.105) (0.105) (0.197)
#> Egality 0.199 0.213 0.115
#> (0.118) (0.119) (0.112)
#> Shame 0.122 0.099 0.146
#> (0.089) (0.089) (0.083)
#> Tax Returns [Low] 0.659 0.508 0.416
#> (0.355) (0.349) (0.339)
#> Tax Returns [Yes] 0.341 0.165 0.279
#> (0.420) (0.413) (0.393)
#> Tax Returns [High] 0.570 0.377 0.517
#> (0.528) (0.524) (0.495)
#> Social Safety [Low] -0.096 -0.023 -0.085
#> (0.336) (0.338) (0.316)
#> Social Safety [Yes] 0.204 0.295 0.170
#> (0.405) (0.407) (0.383)
#> Income Level -0.010 -0.008 -0.021
#> (0.163) (0.165) (0.155)
#> Gender [Male] 0.208 0.315 0.287
#> (0.316) (0.314) (0.300)
#> Age -0.085*
#> (0.035)
#> Generation(3) [30-40] 0.048 0.638
#> (0.372) (0.931)
#> Generation(3) [40-67] -0.907* 3.351*
#> (0.435) (1.376)
#> Generation(3)N -0.482*
#> (0.220)
#> Belonging*Gen3 [30-40] -0.014
#> (0.215)
#> Belonging*Gen3 [40-67] -0.680*
#> (0.263)
#> N 88 88 88
#> R2 0.466 0.444 0.554
#> Adjusted R2 0.372 0.354 0.453
#> ------------------------------------------------------------------
#> *p < .05; **p < .01; ***p < .0016.2.2 Generation (3 levels)
# Colored
sjPlot::plot_model(mi_gen3, type = "int") 
ggsave("out/gen3_interaction_ols.png")
# Black & White
sjPlot::plot_model(mi_gen3, type = "int", colors="bw")
ggsave("out/gen3_interaction_ols_bw.png")6.3 Robustness (Interflex)
6.3.1 Generation (3 Levels)
df <- data[, c("Tax Moral" = "tax_moral_N", cov_gen3)]
interflex(estimator = "raw",
Y = "tax_moral_N", D = "gen3", X = "belonging", Z = cov_a[1:8], data = df,
base="20-30", treat.type="discrete",
weights = NULL, Ylabel = "Tax Moral",
Dlabel = "Generation", Xlabel="Belonging",
main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE,
file = "out/interflex_raw_gen3.png")
#> Baseline group: treat = 20-30
interflex(estimator = "linear",
Y = "tax_moral_N", D = "gen3", X = "belonging", Z = cov_a[1:8], data = df,
base="20-30", treat.type="discrete",
weights = NULL, Ylabel = "Tax Moral",
Dlabel = "Generation", Xlabel="Belonging",
main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE,
file = "out/interflex_linear_gen3.png")
#> Baseline group: treat = 20-30
#> $diff.info
#> $diff.info$diff.values
#> 25% 50% 75%
#> 3 4 5
#>
#> $diff.info$difference.name
#> [1] "50% vs 25%" "75% vs 50%" "75% vs 25%"
#>
#>
#> $treat.info
#> $treat.info$treat.type
#> [1] "discrete"
#>
#> $treat.info$other.treat
#> 30-40 40-67
#> "Group.2" "Group.3"
#>
#> $treat.info$all.treat
#> 20-30 30-40 40-67
#> "Group.1" "Group.2" "Group.3"
#>
#> $treat.info$base
#> 20-30
#> "Group.1"
#>
#> $treat.info$ncols
#> [1] 2
#>
#>
#> $est.lin
#> $est.lin$`30-40`
#> X ME sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 -0.1847329620 0.5404521 -1.2621046 0.8926387
#> [2,] 1.102041 -0.1800020725 0.5273556 -1.2312662 0.8712620
#> [3,] 1.204082 -0.1752711829 0.5144264 -1.2007615 0.8502192
#> [4,] 1.306122 -0.1705402934 0.5016777 -1.1706165 0.8295359
#> [5,] 1.408163 -0.1658094039 0.4891234 -1.1408591 0.8092403
#> [6,] 1.510204 -0.1610785144 0.4767790 -1.1115202 0.7893631
#> [7,] 1.612245 -0.1563476249 0.4646612 -1.0826329 0.7699376
#> [8,] 1.714286 -0.1516167354 0.4527882 -1.0542335 0.7510001
#> [9,] 1.816327 -0.1468858459 0.4411797 -1.0263615 0.7325899
#> [10,] 1.918367 -0.1421549564 0.4298572 -0.9990596 0.7147497
#> [11,] 2.020408 -0.1374240669 0.4188439 -0.9723740 0.6975259
#> [12,] 2.122449 -0.1326931774 0.4081647 -0.9463546 0.6809683
#> [13,] 2.224490 -0.1279622879 0.3978467 -0.9210551 0.6651305
#> [14,] 2.326531 -0.1232313983 0.3879185 -0.8965328 0.6500700
#> [15,] 2.428571 -0.1185005088 0.3784110 -0.8728491 0.6358480
#> [16,] 2.530612 -0.1137696193 0.3693566 -0.8500685 0.6225293
#> [17,] 2.632653 -0.1090387298 0.3607894 -0.8282592 0.6101817
#> [18,] 2.734694 -0.1043078403 0.3527449 -0.8074919 0.5988762
#> [19,] 2.836735 -0.0995769508 0.3452596 -0.7878394 0.5886855
#> [20,] 2.938776 -0.0948460613 0.3383707 -0.7693757 0.5796836
#> [21,] 3.040816 -0.0901151718 0.3321153 -0.7521749 0.5719446
#> [22,] 3.142857 -0.0853842823 0.3265298 -0.7363095 0.5655409
#> [23,] 3.244898 -0.0806533928 0.3216490 -0.7218490 0.5605422
#> [24,] 3.346939 -0.0759225032 0.3175056 -0.7088583 0.5570133
#> [25,] 3.448980 -0.0711916137 0.3141286 -0.6973955 0.5550123
#> [26,] 3.551020 -0.0664607242 0.3115430 -0.6875103 0.5545889
#> [27,] 3.653061 -0.0617298347 0.3097686 -0.6792423 0.5557826
#> [28,] 3.755102 -0.0569989452 0.3088194 -0.6726192 0.5586213
#> [29,] 3.857143 -0.0522680557 0.3087030 -0.6676563 0.5631202
#> [30,] 3.959184 -0.0475371662 0.3094204 -0.6643554 0.5692811
#> [31,] 4.061224 -0.0428062767 0.3109657 -0.6627051 0.5770926
#> [32,] 4.163265 -0.0380753872 0.3133268 -0.6626809 0.5865301
#> [33,] 4.265306 -0.0333444977 0.3164853 -0.6642464 0.5975574
#> [34,] 4.367347 -0.0286136081 0.3204177 -0.6673547 0.6101274
#> [35,] 4.469388 -0.0238827186 0.3250959 -0.6719496 0.6241842
#> [36,] 4.571429 -0.0191518291 0.3304883 -0.6779682 0.6396645
#> [37,] 4.673469 -0.0144209396 0.3365604 -0.6853419 0.6565000
#> [38,] 4.775510 -0.0096900501 0.3432763 -0.6939989 0.6746188
#> [39,] 4.877551 -0.0049591606 0.3505990 -0.7038655 0.6939471
#> [40,] 4.979592 -0.0002282711 0.3584912 -0.7148674 0.7144109
#> [41,] 5.081633 0.0045026184 0.3669162 -0.7269315 0.7359367
#> [42,] 5.183673 0.0092335079 0.3758382 -0.7399863 0.7584533
#> [43,] 5.285714 0.0139643974 0.3852227 -0.7539630 0.7818918
#> [44,] 5.387755 0.0186952870 0.3950367 -0.7687959 0.8061865
#> [45,] 5.489796 0.0234261765 0.4052489 -0.7844228 0.8312751
#> [46,] 5.591837 0.0281570660 0.4158301 -0.8007851 0.8570993
#> [47,] 5.693878 0.0328879555 0.4267528 -0.8178283 0.8836042
#> [48,] 5.795918 0.0376188450 0.4379915 -0.8355013 0.9107390
#> [49,] 5.897959 0.0423497345 0.4495225 -0.8537569 0.9384564
#> [50,] 6.000000 0.0470806240 0.4613238 -0.8725515 0.9667127
#>
#> $est.lin$`40-67`
#> X ME sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 0.63553408 0.9199842 -1.198421 2.46948897
#> [2,] 1.102041 0.57979900 0.8941456 -1.202648 2.36224575
#> [3,] 1.204082 0.52406393 0.8684814 -1.207222 2.25535004
#> [4,] 1.306122 0.46832886 0.8430075 -1.212176 2.14883357
#> [5,] 1.408163 0.41259378 0.8177416 -1.217544 2.04273178
#> [6,] 1.510204 0.35685871 0.7927035 -1.223367 1.93708433
#> [7,] 1.612245 0.30112364 0.7679157 -1.229688 1.83193566
#> [8,] 1.714286 0.24538856 0.7434032 -1.236559 1.72733566
#> [9,] 1.816327 0.18965349 0.7191940 -1.244033 1.62334044
#> [10,] 1.918367 0.13391842 0.6953198 -1.252176 1.52001316
#> [11,] 2.020408 0.07818334 0.6718165 -1.261058 1.41742505
#> [12,] 2.122449 0.02244827 0.6487243 -1.270760 1.31565644
#> [13,] 2.224490 -0.03328680 0.6260886 -1.281372 1.21479801
#> [14,] 2.326531 -0.08902187 0.6039609 -1.292996 1.11495211
#> [15,] 2.428571 -0.14475695 0.5823990 -1.305748 1.01623414
#> [16,] 2.530612 -0.20049202 0.5614680 -1.319758 0.91877408
#> [17,] 2.632653 -0.25622709 0.5412414 -1.335172 0.82271785
#> [18,] 2.734694 -0.31196217 0.5218008 -1.352153 0.72822873
#> [19,] 2.836735 -0.36769724 0.5032375 -1.370883 0.63548835
#> [20,] 2.938776 -0.42343231 0.4856520 -1.391562 0.54469727
#> [21,] 3.040816 -0.47916739 0.4691543 -1.414409 0.45607469
#> [22,] 3.142857 -0.53490246 0.4538631 -1.439662 0.36985713
#> [23,] 3.244898 -0.59063753 0.4399042 -1.467570 0.28629543
#> [24,] 3.346939 -0.64637261 0.4274081 -1.498395 0.20564983
#> [25,] 3.448980 -0.70210768 0.4165065 -1.532398 0.12818284
#> [26,] 3.551020 -0.75784275 0.4073275 -1.569835 0.05414973
#> [27,] 3.653061 -0.81357783 0.3999896 -1.610943 -0.01621307
#> [28,] 3.755102 -0.86931290 0.3945957 -1.655925 -0.08270078
#> [29,] 3.857143 -0.92504797 0.3912261 -1.704943 -0.14515309
#> [30,] 3.959184 -0.98078304 0.3899332 -1.758101 -0.20346539
#> [31,] 4.061224 -1.03651812 0.3907378 -1.815440 -0.25759659
#> [32,] 4.163265 -1.09225319 0.3936269 -1.876934 -0.30757231
#> [33,] 4.265306 -1.14798826 0.3985552 -1.942494 -0.35348292
#> [34,] 4.367347 -1.20372334 0.4054484 -2.011970 -0.39547664
#> [35,] 4.469388 -1.25945841 0.4142084 -2.085168 -0.43374901
#> [36,] 4.571429 -1.31519348 0.4247197 -2.161857 -0.46853026
#> [37,] 4.673469 -1.37092856 0.4368558 -2.241785 -0.50007235
#> [38,] 4.775510 -1.42666363 0.4504856 -2.324690 -0.52863703
#> [39,] 4.877551 -1.48239870 0.4654777 -2.410312 -0.55448582
#> [40,] 4.979592 -1.53813378 0.4817051 -2.498395 -0.57787225
#> [41,] 5.081633 -1.59386885 0.4990472 -2.588701 -0.59903651
#> [42,] 5.183673 -1.64960392 0.5173919 -2.681006 -0.61820202
#> [43,] 5.285714 -1.70533899 0.5366365 -2.775104 -0.63557374
#> [44,] 5.387755 -1.76107407 0.5566876 -2.870810 -0.65133769
#> [45,] 5.489796 -1.81680914 0.5774612 -2.967957 -0.66566133
#> [46,] 5.591837 -1.87254421 0.5988821 -3.066394 -0.67869454
#> [47,] 5.693878 -1.92827929 0.6208834 -3.165988 -0.69057087
#> [48,] 5.795918 -1.98401436 0.6434055 -3.266620 -0.70140900
#> [49,] 5.897959 -2.03974943 0.6663956 -3.368185 -0.71131417
#> [50,] 6.000000 -2.09548451 0.6898068 -3.470589 -0.72037969
#>
#>
#> $pred.lin
#> $pred.lin$`30-40`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 3.867950 0.6993409 2.473840 5.262061
#> [2,] 1.102041 3.928262 0.6947158 2.543371 5.313152
#> [3,] 1.204082 3.988573 0.6903283 2.612429 5.364717
#> [4,] 1.306122 4.048885 0.6861828 2.681004 5.416765
#> [5,] 1.408163 4.109196 0.6822840 2.749088 5.469304
#> [6,] 1.510204 4.169508 0.6786358 2.816672 5.522344
#> [7,] 1.612245 4.229819 0.6752426 2.883748 5.575891
#> [8,] 1.714286 4.290131 0.6721080 2.950308 5.629954
#> [9,] 1.816327 4.350442 0.6692358 3.016345 5.684539
#> [10,] 1.918367 4.410754 0.6666293 3.081853 5.739655
#> [11,] 2.020408 4.471065 0.6642916 3.146824 5.795307
#> [12,] 2.122449 4.531377 0.6622257 3.211254 5.851500
#> [13,] 2.224490 4.591688 0.6604339 3.275137 5.908239
#> [14,] 2.326531 4.652000 0.6589187 3.338470 5.965530
#> [15,] 2.428571 4.712312 0.6576819 3.401247 6.023376
#> [16,] 2.530612 4.772623 0.6567251 3.463466 6.081781
#> [17,] 2.632653 4.832935 0.6560494 3.525124 6.140745
#> [18,] 2.734694 4.893246 0.6556558 3.586220 6.200272
#> [19,] 2.836735 4.953558 0.6555448 3.646753 6.260362
#> [20,] 2.938776 5.013869 0.6557165 3.706722 6.321016
#> [21,] 3.040816 5.074181 0.6561707 3.766128 6.382233
#> [22,] 3.142857 5.134492 0.6569069 3.824972 6.444012
#> [23,] 3.244898 5.194804 0.6579239 3.883256 6.506351
#> [24,] 3.346939 5.255115 0.6592207 3.940983 6.569248
#> [25,] 3.448980 5.315427 0.6607954 3.998155 6.632699
#> [26,] 3.551020 5.375738 0.6626462 4.054777 6.696700
#> [27,] 3.653061 5.436050 0.6647708 4.110854 6.761246
#> [28,] 3.755102 5.496361 0.6671664 4.166390 6.826333
#> [29,] 3.857143 5.556673 0.6698302 4.221391 6.891955
#> [30,] 3.959184 5.616985 0.6727591 4.275864 6.958105
#> [31,] 4.061224 5.677296 0.6759495 4.329815 7.024777
#> [32,] 4.163265 5.737608 0.6793979 4.383253 7.091962
#> [33,] 4.265306 5.797919 0.6831002 4.436184 7.159654
#> [34,] 4.367347 5.858231 0.6870524 4.488617 7.227845
#> [35,] 4.469388 5.918542 0.6912501 4.540560 7.296524
#> [36,] 4.571429 5.978854 0.6956891 4.592023 7.365685
#> [37,] 4.673469 6.039165 0.7003645 4.643014 7.435316
#> [38,] 4.775510 6.099477 0.7052719 4.693543 7.505411
#> [39,] 4.877551 6.159788 0.7104063 4.743619 7.575957
#> [40,] 4.979592 6.220100 0.7157628 4.793253 7.646947
#> [41,] 5.081633 6.280411 0.7213366 4.842453 7.718370
#> [42,] 5.183673 6.340723 0.7271226 4.891231 7.790215
#> [43,] 5.285714 6.401034 0.7331158 4.939595 7.862474
#> [44,] 5.387755 6.461346 0.7393112 4.987556 7.935136
#> [45,] 5.489796 6.521658 0.7457036 5.035125 8.008191
#> [46,] 5.591837 6.581969 0.7522882 5.082310 8.081628
#> [47,] 5.693878 6.642281 0.7590598 5.129122 8.155439
#> [48,] 5.795918 6.702592 0.7660136 5.175572 8.229612
#> [49,] 5.897959 6.762904 0.7731446 5.221668 8.304139
#> [50,] 6.000000 6.823215 0.7804480 5.267421 8.379010
#>
#> $pred.lin$`40-67`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 4.688217 1.0235043 2.647899 6.728536
#> [2,] 1.102041 4.688063 1.0046280 2.685373 6.690752
#> [3,] 1.204082 4.687908 0.9860782 2.722197 6.653619
#> [4,] 1.306122 4.687754 0.9678737 2.758333 6.617175
#> [5,] 1.408163 4.687599 0.9500343 2.793741 6.581458
#> [6,] 1.510204 4.687445 0.9325810 2.828379 6.546511
#> [7,] 1.612245 4.687291 0.9155358 2.862203 6.512378
#> [8,] 1.714286 4.687136 0.8989221 2.895168 6.479105
#> [9,] 1.816327 4.686982 0.8827641 2.927224 6.446740
#> [10,] 1.918367 4.686827 0.8670874 2.958320 6.415334
#> [11,] 2.020408 4.686673 0.8519184 2.988405 6.384941
#> [12,] 2.122449 4.686518 0.8372848 3.017422 6.355615
#> [13,] 2.224490 4.686364 0.8232151 3.045315 6.327413
#> [14,] 2.326531 4.686210 0.8097388 3.072025 6.300394
#> [15,] 2.428571 4.686055 0.7968860 3.097492 6.274618
#> [16,] 2.530612 4.685901 0.7846872 3.121655 6.250146
#> [17,] 2.632653 4.685746 0.7731734 3.144453 6.227039
#> [18,] 2.734694 4.685592 0.7623758 3.165824 6.205360
#> [19,] 2.836735 4.685437 0.7523250 3.185705 6.185170
#> [20,] 2.938776 4.685283 0.7430514 3.204037 6.166529
#> [21,] 3.040816 4.685129 0.7345845 3.220761 6.149496
#> [22,] 3.142857 4.684974 0.7269525 3.235821 6.134127
#> [23,] 3.244898 4.684820 0.7201818 3.249163 6.120476
#> [24,] 3.346939 4.684665 0.7142970 3.260740 6.108590
#> [25,] 3.448980 4.684511 0.7093201 3.270507 6.098515
#> [26,] 3.551020 4.684356 0.7052704 3.278426 6.090287
#> [27,] 3.653061 4.684202 0.7021639 3.284464 6.083940
#> [28,] 3.755102 4.684048 0.7000131 3.288597 6.079498
#> [29,] 3.857143 4.683893 0.6988268 3.290807 6.076979
#> [30,] 3.959184 4.683739 0.6986101 3.291085 6.076392
#> [31,] 4.061224 4.683584 0.6993637 3.289428 6.077740
#> [32,] 4.163265 4.683430 0.7010846 3.285843 6.081016
#> [33,] 4.265306 4.683275 0.7037657 3.280344 6.086207
#> [34,] 4.367347 4.683121 0.7073960 3.272953 6.093289
#> [35,] 4.469388 4.682967 0.7119610 3.263698 6.102235
#> [36,] 4.571429 4.682812 0.7174429 3.252616 6.113008
#> [37,] 4.673469 4.682658 0.7238209 3.239747 6.125568
#> [38,] 4.775510 4.682503 0.7310714 3.225139 6.139867
#> [39,] 4.877551 4.682349 0.7391689 3.208843 6.155855
#> [40,] 4.979592 4.682194 0.7480858 3.190913 6.173476
#> [41,] 5.081633 4.682040 0.7577932 3.171407 6.192673
#> [42,] 5.183673 4.681886 0.7682611 3.150385 6.213386
#> [43,] 5.285714 4.681731 0.7794589 3.127908 6.235554
#> [44,] 5.387755 4.681577 0.7913556 3.104038 6.259115
#> [45,] 5.489796 4.681422 0.8039201 3.078837 6.284008
#> [46,] 5.591837 4.681268 0.8171218 3.052365 6.310170
#> [47,] 5.693878 4.681113 0.8309301 3.024684 6.337542
#> [48,] 5.795918 4.680959 0.8453155 2.995853 6.366065
#> [49,] 5.897959 4.680805 0.8602488 2.965930 6.395679
#> [50,] 6.000000 4.680650 0.8757022 2.934970 6.426330
#>
#> $pred.lin$`20-30`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 4.052683 0.3965357 3.262204 4.843162
#> [2,] 1.102041 4.108264 0.4047686 3.301372 4.915155
#> [3,] 1.204082 4.163844 0.4133131 3.339920 4.987769
#> [4,] 1.306122 4.219425 0.4221502 3.377884 5.060966
#> [5,] 1.408163 4.275006 0.4312620 3.415301 5.134711
#> [6,] 1.510204 4.330586 0.4406314 3.452204 5.208969
#> [7,] 1.612245 4.386167 0.4502423 3.488625 5.283708
#> [8,] 1.714286 4.441748 0.4600796 3.524596 5.358899
#> [9,] 1.816327 4.497328 0.4701290 3.560143 5.434513
#> [10,] 1.918367 4.552909 0.4803774 3.595294 5.510524
#> [11,] 2.020408 4.608489 0.4908121 3.630073 5.586906
#> [12,] 2.122449 4.664070 0.5014216 3.664504 5.663636
#> [13,] 2.224490 4.719651 0.5121950 3.698609 5.740693
#> [14,] 2.326531 4.775231 0.5231222 3.732406 5.818056
#> [15,] 2.428571 4.830812 0.5341938 3.765916 5.895708
#> [16,] 2.530612 4.886393 0.5454009 3.799156 5.973629
#> [17,] 2.632653 4.941973 0.5567353 3.832142 6.051805
#> [18,] 2.734694 4.997554 0.5681895 3.864889 6.130219
#> [19,] 2.836735 5.053135 0.5797564 3.897411 6.208858
#> [20,] 2.938776 5.108715 0.5914293 3.929723 6.287708
#> [21,] 3.040816 5.164296 0.6032021 3.961835 6.366757
#> [22,] 3.142857 5.219877 0.6150690 3.993759 6.445994
#> [23,] 3.244898 5.275457 0.6270247 4.025506 6.525408
#> [24,] 3.346939 5.331038 0.6390642 4.057087 6.604989
#> [25,] 3.448980 5.386618 0.6511830 4.088509 6.684728
#> [26,] 3.551020 5.442199 0.6633765 4.119782 6.764616
#> [27,] 3.653061 5.497780 0.6756408 4.150914 6.844645
#> [28,] 3.755102 5.553360 0.6879721 4.181913 6.924808
#> [29,] 3.857143 5.608941 0.7003669 4.212785 7.005097
#> [30,] 3.959184 5.664522 0.7128218 4.243537 7.085506
#> [31,] 4.061224 5.720102 0.7253338 4.274176 7.166029
#> [32,] 4.163265 5.775683 0.7378999 4.304706 7.246660
#> [33,] 4.265306 5.831264 0.7505174 4.335134 7.327393
#> [34,] 4.367347 5.886844 0.7631838 4.365465 7.408223
#> [35,] 4.469388 5.942425 0.7758967 4.395703 7.489147
#> [36,] 4.571429 5.998006 0.7886539 4.425853 7.570158
#> [37,] 4.673469 6.053586 0.8014531 4.455919 7.651254
#> [38,] 4.775510 6.109167 0.8142925 4.485904 7.732429
#> [39,] 4.877551 6.164748 0.8271701 4.515814 7.813681
#> [40,] 4.979592 6.220328 0.8400843 4.545651 7.895006
#> [41,] 5.081633 6.275909 0.8530333 4.575418 7.976400
#> [42,] 5.183673 6.331489 0.8660155 4.605119 8.057860
#> [43,] 5.285714 6.387070 0.8790296 4.634757 8.139384
#> [44,] 5.387755 6.442651 0.8920741 4.664333 8.220968
#> [45,] 5.489796 6.498231 0.9051477 4.693852 8.302610
#> [46,] 5.591837 6.553812 0.9182492 4.723316 8.384308
#> [47,] 5.693878 6.609393 0.9313774 4.752726 8.466060
#> [48,] 5.795918 6.664973 0.9445311 4.782085 8.547862
#> [49,] 5.897959 6.720554 0.9577093 4.811395 8.629713
#> [50,] 6.000000 6.776135 0.9709111 4.840659 8.711611
#>
#>
#> $link.lin
#> $link.lin$`30-40`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 3.867950 0.6993409 2.473840 5.262061
#> [2,] 1.102041 3.928262 0.6947158 2.543371 5.313152
#> [3,] 1.204082 3.988573 0.6903283 2.612429 5.364717
#> [4,] 1.306122 4.048885 0.6861828 2.681004 5.416765
#> [5,] 1.408163 4.109196 0.6822840 2.749088 5.469304
#> [6,] 1.510204 4.169508 0.6786358 2.816672 5.522344
#> [7,] 1.612245 4.229819 0.6752426 2.883748 5.575891
#> [8,] 1.714286 4.290131 0.6721080 2.950308 5.629954
#> [9,] 1.816327 4.350442 0.6692358 3.016345 5.684539
#> [10,] 1.918367 4.410754 0.6666293 3.081853 5.739655
#> [11,] 2.020408 4.471065 0.6642916 3.146824 5.795307
#> [12,] 2.122449 4.531377 0.6622257 3.211254 5.851500
#> [13,] 2.224490 4.591688 0.6604339 3.275137 5.908239
#> [14,] 2.326531 4.652000 0.6589187 3.338470 5.965530
#> [15,] 2.428571 4.712312 0.6576819 3.401247 6.023376
#> [16,] 2.530612 4.772623 0.6567251 3.463466 6.081781
#> [17,] 2.632653 4.832935 0.6560494 3.525124 6.140745
#> [18,] 2.734694 4.893246 0.6556558 3.586220 6.200272
#> [19,] 2.836735 4.953558 0.6555448 3.646753 6.260362
#> [20,] 2.938776 5.013869 0.6557165 3.706722 6.321016
#> [21,] 3.040816 5.074181 0.6561707 3.766128 6.382233
#> [22,] 3.142857 5.134492 0.6569069 3.824972 6.444012
#> [23,] 3.244898 5.194804 0.6579239 3.883256 6.506351
#> [24,] 3.346939 5.255115 0.6592207 3.940983 6.569248
#> [25,] 3.448980 5.315427 0.6607954 3.998155 6.632699
#> [26,] 3.551020 5.375738 0.6626462 4.054777 6.696700
#> [27,] 3.653061 5.436050 0.6647708 4.110854 6.761246
#> [28,] 3.755102 5.496361 0.6671664 4.166390 6.826333
#> [29,] 3.857143 5.556673 0.6698302 4.221391 6.891955
#> [30,] 3.959184 5.616985 0.6727591 4.275864 6.958105
#> [31,] 4.061224 5.677296 0.6759495 4.329815 7.024777
#> [32,] 4.163265 5.737608 0.6793979 4.383253 7.091962
#> [33,] 4.265306 5.797919 0.6831002 4.436184 7.159654
#> [34,] 4.367347 5.858231 0.6870524 4.488617 7.227845
#> [35,] 4.469388 5.918542 0.6912501 4.540560 7.296524
#> [36,] 4.571429 5.978854 0.6956891 4.592023 7.365685
#> [37,] 4.673469 6.039165 0.7003645 4.643014 7.435316
#> [38,] 4.775510 6.099477 0.7052719 4.693543 7.505411
#> [39,] 4.877551 6.159788 0.7104063 4.743619 7.575957
#> [40,] 4.979592 6.220100 0.7157628 4.793253 7.646947
#> [41,] 5.081633 6.280411 0.7213366 4.842453 7.718370
#> [42,] 5.183673 6.340723 0.7271226 4.891231 7.790215
#> [43,] 5.285714 6.401034 0.7331158 4.939595 7.862474
#> [44,] 5.387755 6.461346 0.7393112 4.987556 7.935136
#> [45,] 5.489796 6.521658 0.7457036 5.035125 8.008191
#> [46,] 5.591837 6.581969 0.7522882 5.082310 8.081628
#> [47,] 5.693878 6.642281 0.7590598 5.129122 8.155439
#> [48,] 5.795918 6.702592 0.7660136 5.175572 8.229612
#> [49,] 5.897959 6.762904 0.7731446 5.221668 8.304139
#> [50,] 6.000000 6.823215 0.7804480 5.267421 8.379010
#>
#> $link.lin$`40-67`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 4.688217 1.0235043 2.647899 6.728536
#> [2,] 1.102041 4.688063 1.0046280 2.685373 6.690752
#> [3,] 1.204082 4.687908 0.9860782 2.722197 6.653619
#> [4,] 1.306122 4.687754 0.9678737 2.758333 6.617175
#> [5,] 1.408163 4.687599 0.9500343 2.793741 6.581458
#> [6,] 1.510204 4.687445 0.9325810 2.828379 6.546511
#> [7,] 1.612245 4.687291 0.9155358 2.862203 6.512378
#> [8,] 1.714286 4.687136 0.8989221 2.895168 6.479105
#> [9,] 1.816327 4.686982 0.8827641 2.927224 6.446740
#> [10,] 1.918367 4.686827 0.8670874 2.958320 6.415334
#> [11,] 2.020408 4.686673 0.8519184 2.988405 6.384941
#> [12,] 2.122449 4.686518 0.8372848 3.017422 6.355615
#> [13,] 2.224490 4.686364 0.8232151 3.045315 6.327413
#> [14,] 2.326531 4.686210 0.8097388 3.072025 6.300394
#> [15,] 2.428571 4.686055 0.7968860 3.097492 6.274618
#> [16,] 2.530612 4.685901 0.7846872 3.121655 6.250146
#> [17,] 2.632653 4.685746 0.7731734 3.144453 6.227039
#> [18,] 2.734694 4.685592 0.7623758 3.165824 6.205360
#> [19,] 2.836735 4.685437 0.7523250 3.185705 6.185170
#> [20,] 2.938776 4.685283 0.7430514 3.204037 6.166529
#> [21,] 3.040816 4.685129 0.7345845 3.220761 6.149496
#> [22,] 3.142857 4.684974 0.7269525 3.235821 6.134127
#> [23,] 3.244898 4.684820 0.7201818 3.249163 6.120476
#> [24,] 3.346939 4.684665 0.7142970 3.260740 6.108590
#> [25,] 3.448980 4.684511 0.7093201 3.270507 6.098515
#> [26,] 3.551020 4.684356 0.7052704 3.278426 6.090287
#> [27,] 3.653061 4.684202 0.7021639 3.284464 6.083940
#> [28,] 3.755102 4.684048 0.7000131 3.288597 6.079498
#> [29,] 3.857143 4.683893 0.6988268 3.290807 6.076979
#> [30,] 3.959184 4.683739 0.6986101 3.291085 6.076392
#> [31,] 4.061224 4.683584 0.6993637 3.289428 6.077740
#> [32,] 4.163265 4.683430 0.7010846 3.285843 6.081016
#> [33,] 4.265306 4.683275 0.7037657 3.280344 6.086207
#> [34,] 4.367347 4.683121 0.7073960 3.272953 6.093289
#> [35,] 4.469388 4.682967 0.7119610 3.263698 6.102235
#> [36,] 4.571429 4.682812 0.7174429 3.252616 6.113008
#> [37,] 4.673469 4.682658 0.7238209 3.239747 6.125568
#> [38,] 4.775510 4.682503 0.7310714 3.225139 6.139867
#> [39,] 4.877551 4.682349 0.7391689 3.208843 6.155855
#> [40,] 4.979592 4.682194 0.7480858 3.190913 6.173476
#> [41,] 5.081633 4.682040 0.7577932 3.171407 6.192673
#> [42,] 5.183673 4.681886 0.7682611 3.150385 6.213386
#> [43,] 5.285714 4.681731 0.7794589 3.127908 6.235554
#> [44,] 5.387755 4.681577 0.7913556 3.104038 6.259115
#> [45,] 5.489796 4.681422 0.8039201 3.078837 6.284008
#> [46,] 5.591837 4.681268 0.8171218 3.052365 6.310170
#> [47,] 5.693878 4.681113 0.8309301 3.024684 6.337542
#> [48,] 5.795918 4.680959 0.8453155 2.995853 6.366065
#> [49,] 5.897959 4.680805 0.8602488 2.965930 6.395679
#> [50,] 6.000000 4.680650 0.8757022 2.934970 6.426330
#>
#> $link.lin$`20-30`
#> X E(Y) sd lower CI(95%) upper CI(95%)
#> [1,] 1.000000 4.052683 0.3965357 3.262204 4.843162
#> [2,] 1.102041 4.108264 0.4047686 3.301372 4.915155
#> [3,] 1.204082 4.163844 0.4133131 3.339920 4.987769
#> [4,] 1.306122 4.219425 0.4221502 3.377884 5.060966
#> [5,] 1.408163 4.275006 0.4312620 3.415301 5.134711
#> [6,] 1.510204 4.330586 0.4406314 3.452204 5.208969
#> [7,] 1.612245 4.386167 0.4502423 3.488625 5.283708
#> [8,] 1.714286 4.441748 0.4600796 3.524596 5.358899
#> [9,] 1.816327 4.497328 0.4701290 3.560143 5.434513
#> [10,] 1.918367 4.552909 0.4803774 3.595294 5.510524
#> [11,] 2.020408 4.608489 0.4908121 3.630073 5.586906
#> [12,] 2.122449 4.664070 0.5014216 3.664504 5.663636
#> [13,] 2.224490 4.719651 0.5121950 3.698609 5.740693
#> [14,] 2.326531 4.775231 0.5231222 3.732406 5.818056
#> [15,] 2.428571 4.830812 0.5341938 3.765916 5.895708
#> [16,] 2.530612 4.886393 0.5454009 3.799156 5.973629
#> [17,] 2.632653 4.941973 0.5567353 3.832142 6.051805
#> [18,] 2.734694 4.997554 0.5681895 3.864889 6.130219
#> [19,] 2.836735 5.053135 0.5797564 3.897411 6.208858
#> [20,] 2.938776 5.108715 0.5914293 3.929723 6.287708
#> [21,] 3.040816 5.164296 0.6032021 3.961835 6.366757
#> [22,] 3.142857 5.219877 0.6150690 3.993759 6.445994
#> [23,] 3.244898 5.275457 0.6270247 4.025506 6.525408
#> [24,] 3.346939 5.331038 0.6390642 4.057087 6.604989
#> [25,] 3.448980 5.386618 0.6511830 4.088509 6.684728
#> [26,] 3.551020 5.442199 0.6633765 4.119782 6.764616
#> [27,] 3.653061 5.497780 0.6756408 4.150914 6.844645
#> [28,] 3.755102 5.553360 0.6879721 4.181913 6.924808
#> [29,] 3.857143 5.608941 0.7003669 4.212785 7.005097
#> [30,] 3.959184 5.664522 0.7128218 4.243537 7.085506
#> [31,] 4.061224 5.720102 0.7253338 4.274176 7.166029
#> [32,] 4.163265 5.775683 0.7378999 4.304706 7.246660
#> [33,] 4.265306 5.831264 0.7505174 4.335134 7.327393
#> [34,] 4.367347 5.886844 0.7631838 4.365465 7.408223
#> [35,] 4.469388 5.942425 0.7758967 4.395703 7.489147
#> [36,] 4.571429 5.998006 0.7886539 4.425853 7.570158
#> [37,] 4.673469 6.053586 0.8014531 4.455919 7.651254
#> [38,] 4.775510 6.109167 0.8142925 4.485904 7.732429
#> [39,] 4.877551 6.164748 0.8271701 4.515814 7.813681
#> [40,] 4.979592 6.220328 0.8400843 4.545651 7.895006
#> [41,] 5.081633 6.275909 0.8530333 4.575418 7.976400
#> [42,] 5.183673 6.331489 0.8660155 4.605119 8.057860
#> [43,] 5.285714 6.387070 0.8790296 4.634757 8.139384
#> [44,] 5.387755 6.442651 0.8920741 4.664333 8.220968
#> [45,] 5.489796 6.498231 0.9051477 4.693852 8.302610
#> [46,] 5.591837 6.553812 0.9182492 4.723316 8.384308
#> [47,] 5.693878 6.609393 0.9313774 4.752726 8.466060
#> [48,] 5.795918 6.664973 0.9445311 4.782085 8.547862
#> [49,] 5.897959 6.720554 0.9577093 4.811395 8.629713
#> [50,] 6.000000 6.776135 0.9709111 4.840659 8.711611
#>
#>
#> $diff.estimate
#> $diff.estimate$`30-40`
#> diff.estimate sd z-value p-value lower CI(95%) upper CI(95%)
#> 50% vs 25% 0.046 0.157 0.295 0.768 -0.267 0.360
#> 75% vs 50% 0.046 0.157 0.295 0.768 -0.267 0.360
#> 75% vs 25% 0.093 0.315 0.295 0.768 -0.534 0.720
#>
#> $diff.estimate$`40-67`
#> diff.estimate sd z-value p-value lower CI(95%) upper CI(95%)
#> 50% vs 25% -0.546 0.280 -1.948 0.051 -1.105 0.013
#> 75% vs 50% -0.546 0.280 -1.948 0.051 -1.105 0.013
#> 75% vs 25% -1.092 0.561 -1.948 0.051 -2.211 0.026
#>
#>
#> $vcov.matrix
#> $vcov.matrix$`30-40`
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.2920885088 0.284967374 0.2778462398 0.270725105 0.263603971
#> [2,] 0.2849673743 0.278103883 0.2712403925 0.264376902 0.257513411
#> [3,] 0.2778462398 0.271240393 0.2646345452 0.258028698 0.251422851
#> [4,] 0.2707251053 0.264376902 0.2580286979 0.251680494 0.245332291
#> [5,] 0.2636039708 0.257513411 0.2514228507 0.245332291 0.239241730
#> [6,] 0.2564828363 0.250649920 0.2448170034 0.238984087 0.233151170
#> [7,] 0.2493617019 0.243786429 0.2382111561 0.232635883 0.227060610
#> [8,] 0.2422405674 0.236922938 0.2316053088 0.226287680 0.220970050
#> [9,] 0.2351194329 0.230059447 0.2249994615 0.219939476 0.214879490
#> [10,] 0.2279982984 0.223195956 0.2183936142 0.213591272 0.208788930
#> [11,] 0.2208771639 0.216332465 0.2117877669 0.207243068 0.202698370
#> [12,] 0.2137560294 0.209468975 0.2051819197 0.200894865 0.196607810
#> [13,] 0.2066348950 0.202605484 0.1985760724 0.194546661 0.190517250
#> [14,] 0.1995137605 0.195741993 0.1919702251 0.188198457 0.184426690
#> [15,] 0.1923926260 0.188878502 0.1853643778 0.181850254 0.178336130
#> [16,] 0.1852714915 0.182015011 0.1787585305 0.175502050 0.172245570
#> [17,] 0.1781503570 0.175151520 0.1721526832 0.169153846 0.166155009
#> [18,] 0.1710292225 0.168288029 0.1655468360 0.162805643 0.160064449
#> [19,] 0.1639080881 0.161424538 0.1589409887 0.156457439 0.153973889
#> [20,] 0.1567869536 0.154561047 0.1523351414 0.150109235 0.147883329
#> [21,] 0.1496658191 0.147697557 0.1457292941 0.143761032 0.141792769
#> [22,] 0.1425446846 0.140834066 0.1391234468 0.137412828 0.135702209
#> [23,] 0.1354235501 0.133970575 0.1325175995 0.131064624 0.129611649
#> [24,] 0.1283024156 0.127107084 0.1259117522 0.124716421 0.123521089
#> [25,] 0.1211812812 0.120243593 0.1193059050 0.118368217 0.117430529
#> [26,] 0.1140601467 0.113380102 0.1127000577 0.112020013 0.111339969
#> [27,] 0.1069390122 0.106516611 0.1060942104 0.105671809 0.105249409
#> [28,] 0.0998178777 0.099653120 0.0994883631 0.099323606 0.099158848
#> [29,] 0.0926967432 0.092789630 0.0928825158 0.092975402 0.093068288
#> [30,] 0.0855756087 0.085926139 0.0862766685 0.086627198 0.086977728
#> [31,] 0.0784544743 0.079062648 0.0796708212 0.080278995 0.080887168
#> [32,] 0.0713333398 0.072199157 0.0730649740 0.073930791 0.074796608
#> [33,] 0.0642122053 0.065335666 0.0664591267 0.067582587 0.068706048
#> [34,] 0.0570910708 0.058472175 0.0598532794 0.061234384 0.062615488
#> [35,] 0.0499699363 0.051608684 0.0532474321 0.054886180 0.056524928
#> [36,] 0.0428488018 0.044745193 0.0466415848 0.048537976 0.050434368
#> [37,] 0.0357276674 0.037881702 0.0400357375 0.042189773 0.044343808
#> [38,] 0.0286065329 0.031018212 0.0334298903 0.035841569 0.038253248
#> [39,] 0.0214853984 0.024154721 0.0268240430 0.029493365 0.032162688
#> [40,] 0.0143642639 0.017291230 0.0202181957 0.023145162 0.026072127
#> [41,] 0.0072431294 0.010427739 0.0136123484 0.016796958 0.019981567
#> [42,] 0.0001219949 0.003564248 0.0070065011 0.010448754 0.013891007
#> [43,] -0.0069991395 -0.003299243 0.0004006538 0.004100551 0.007800447
#> [44,] -0.0141202740 -0.010162734 -0.0062051935 -0.002247653 0.001709887
#> [45,] -0.0212414085 -0.017026225 -0.0128110407 -0.008595857 -0.004380673
#> [46,] -0.0283625430 -0.023889716 -0.0194168880 -0.014944061 -0.010471233
#> [47,] -0.0354836775 -0.030753206 -0.0260227353 -0.021292264 -0.016561793
#> [48,] -0.0426048120 -0.037616697 -0.0326285826 -0.027640468 -0.022652353
#> [49,] -0.0497259464 -0.044480188 -0.0392344299 -0.033988672 -0.028742913
#> [50,] -0.0568470809 -0.051343679 -0.0458402772 -0.040336875 -0.034833473
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 0.2564828363 0.249361702 0.242240567 0.235119433 0.227998298
#> [2,] 0.2506499199 0.243786429 0.236922938 0.230059447 0.223195956
#> [3,] 0.2448170034 0.238211156 0.231605309 0.224999462 0.218393614
#> [4,] 0.2389840869 0.232635883 0.226287680 0.219939476 0.213591272
#> [5,] 0.2331511704 0.227060610 0.220970050 0.214879490 0.208788930
#> [6,] 0.2273182539 0.221485337 0.215652421 0.209819504 0.203986588
#> [7,] 0.2214853374 0.215910065 0.210334792 0.204759519 0.199184246
#> [8,] 0.2156524209 0.210334792 0.205017162 0.199699533 0.194381904
#> [9,] 0.2098195044 0.204759519 0.199699533 0.194639547 0.189579562
#> [10,] 0.2039865880 0.199184246 0.194381904 0.189579562 0.184777220
#> [11,] 0.1981536715 0.193608973 0.189064274 0.184519576 0.179974878
#> [12,] 0.1923207550 0.188033700 0.183746645 0.179459590 0.175172535
#> [13,] 0.1864878385 0.182458427 0.178429016 0.174399605 0.170370193
#> [14,] 0.1806549220 0.176883154 0.173111387 0.169339619 0.165567851
#> [15,] 0.1748220055 0.171307881 0.167793757 0.164279633 0.160765509
#> [16,] 0.1689890890 0.165732609 0.162476128 0.159219648 0.155963167
#> [17,] 0.1631561725 0.160157336 0.157158499 0.154159662 0.151160825
#> [18,] 0.1573232561 0.154582063 0.151840869 0.149099676 0.146358483
#> [19,] 0.1514903396 0.149006790 0.146523240 0.144039690 0.141556141
#> [20,] 0.1456574231 0.143431517 0.141205611 0.138979705 0.136753799
#> [21,] 0.1398245066 0.137856244 0.135887982 0.133919719 0.131951457
#> [22,] 0.1339915901 0.132280971 0.130570352 0.128859733 0.127149115
#> [23,] 0.1281586736 0.126705698 0.125252723 0.123799748 0.122346772
#> [24,] 0.1223257571 0.121130425 0.119935094 0.118739762 0.117544430
#> [25,] 0.1164928407 0.115555153 0.114617464 0.113679776 0.112742088
#> [26,] 0.1106599242 0.109979880 0.109299835 0.108619791 0.107939746
#> [27,] 0.1048270077 0.104404607 0.103982206 0.103559805 0.103137404
#> [28,] 0.0989940912 0.098829334 0.098664577 0.098499819 0.098335062
#> [29,] 0.0931611747 0.093254061 0.093346947 0.093439834 0.093532720
#> [30,] 0.0873282582 0.087678788 0.088029318 0.088379848 0.088730378
#> [31,] 0.0814953417 0.082103515 0.082711689 0.083319862 0.083928036
#> [32,] 0.0756624252 0.076528242 0.077394059 0.078259877 0.079125694
#> [33,] 0.0698295088 0.070952969 0.072076430 0.073199891 0.074323352
#> [34,] 0.0639965923 0.065377697 0.066758801 0.068139905 0.069521009
#> [35,] 0.0581636758 0.059802424 0.061441172 0.063079919 0.064718667
#> [36,] 0.0523307593 0.054227151 0.056123542 0.058019934 0.059916325
#> [37,] 0.0464978428 0.048651878 0.050805913 0.052959948 0.055113983
#> [38,] 0.0406649263 0.043076605 0.045488284 0.047899962 0.050311641
#> [39,] 0.0348320098 0.037501332 0.040170654 0.042839977 0.045509299
#> [40,] 0.0289990933 0.031926059 0.034853025 0.037779991 0.040706957
#> [41,] 0.0231661769 0.026350786 0.029535396 0.032720005 0.035904615
#> [42,] 0.0173332604 0.020775513 0.024217767 0.027660020 0.031102273
#> [43,] 0.0115003439 0.015200241 0.018900137 0.022600034 0.026299931
#> [44,] 0.0056674274 0.009624968 0.013582508 0.017540048 0.021497589
#> [45,] -0.0001654891 0.004049695 0.008264879 0.012480063 0.016695246
#> [46,] -0.0059984056 -0.001525578 0.002947249 0.007420077 0.011892904
#> [47,] -0.0118313221 -0.007100851 -0.002370380 0.002360091 0.007090562
#> [48,] -0.0176642386 -0.012676124 -0.007688009 -0.002699895 0.002288220
#> [49,] -0.0234971550 -0.018251397 -0.013005638 -0.007759880 -0.002514122
#> [50,] -0.0293300715 -0.023826670 -0.018323268 -0.012819866 -0.007316464
#> [,11] [,12] [,13] [,14] [,15] [,16]
#> [1,] 0.220877164 0.213756029 0.206634895 0.19951376 0.19239263 0.18527149
#> [2,] 0.216332465 0.209468975 0.202605484 0.19574199 0.18887850 0.18201501
#> [3,] 0.211787767 0.205181920 0.198576072 0.19197023 0.18536438 0.17875853
#> [4,] 0.207243068 0.200894865 0.194546661 0.18819846 0.18185025 0.17550205
#> [5,] 0.202698370 0.196607810 0.190517250 0.18442669 0.17833613 0.17224557
#> [6,] 0.198153671 0.192320755 0.186487838 0.18065492 0.17482201 0.16898909
#> [7,] 0.193608973 0.188033700 0.182458427 0.17688315 0.17130788 0.16573261
#> [8,] 0.189064274 0.183746645 0.178429016 0.17311139 0.16779376 0.16247613
#> [9,] 0.184519576 0.179459590 0.174399605 0.16933962 0.16427963 0.15921965
#> [10,] 0.179974878 0.175172535 0.170370193 0.16556785 0.16076551 0.15596317
#> [11,] 0.175430179 0.170885481 0.166340782 0.16179608 0.15725139 0.15270669
#> [12,] 0.170885481 0.166598426 0.162311371 0.15802432 0.15373726 0.14945021
#> [13,] 0.166340782 0.162311371 0.158281959 0.15425255 0.15022314 0.14619373
#> [14,] 0.161796084 0.158024316 0.154252548 0.15048078 0.14670901 0.14293725
#> [15,] 0.157251385 0.153737261 0.150223137 0.14670901 0.14319489 0.13968076
#> [16,] 0.152706687 0.149450206 0.146193726 0.14293725 0.13968076 0.13642428
#> [17,] 0.148161988 0.145163151 0.142164314 0.13916548 0.13616664 0.13316780
#> [18,] 0.143617290 0.140876096 0.138134903 0.13539371 0.13265252 0.12991132
#> [19,] 0.139072591 0.136589041 0.134105492 0.13162194 0.12913839 0.12665484
#> [20,] 0.134527893 0.132301986 0.130076080 0.12785017 0.12562427 0.12339836
#> [21,] 0.129983194 0.128014932 0.126046669 0.12407841 0.12211014 0.12014188
#> [22,] 0.125438496 0.123727877 0.122017258 0.12030664 0.11859602 0.11688540
#> [23,] 0.120893797 0.119440822 0.117987847 0.11653487 0.11508190 0.11362892
#> [24,] 0.116349099 0.115153767 0.113958435 0.11276310 0.11156777 0.11037244
#> [25,] 0.111804400 0.110866712 0.109929024 0.10899134 0.10805365 0.10711596
#> [26,] 0.107259702 0.106579657 0.105899613 0.10521957 0.10453952 0.10385948
#> [27,] 0.102715003 0.102292602 0.101870201 0.10144780 0.10102540 0.10060300
#> [28,] 0.098170305 0.098005547 0.097840790 0.09767603 0.09751128 0.09734652
#> [29,] 0.093625606 0.093718492 0.093811379 0.09390427 0.09399715 0.09409004
#> [30,] 0.089080908 0.089431438 0.089781967 0.09013250 0.09048303 0.09083356
#> [31,] 0.084536209 0.085144383 0.085752556 0.08636073 0.08696890 0.08757708
#> [32,] 0.079991511 0.080857328 0.081723145 0.08258896 0.08345478 0.08432060
#> [33,] 0.075446812 0.076570273 0.077693734 0.07881719 0.07994065 0.08106412
#> [34,] 0.070902114 0.072283218 0.073664322 0.07504543 0.07642653 0.07780764
#> [35,] 0.066357415 0.067996163 0.069634911 0.07127366 0.07291241 0.07455115
#> [36,] 0.061812717 0.063709108 0.065605500 0.06750189 0.06939828 0.07129467
#> [37,] 0.057268018 0.059422053 0.061576088 0.06373012 0.06588416 0.06803819
#> [38,] 0.052723320 0.055134998 0.057546677 0.05995836 0.06237003 0.06478171
#> [39,] 0.048178621 0.050847944 0.053517266 0.05618659 0.05885591 0.06152523
#> [40,] 0.043633923 0.046560889 0.049487855 0.05241482 0.05534179 0.05826875
#> [41,] 0.039089224 0.042273834 0.045458443 0.04864305 0.05182766 0.05501227
#> [42,] 0.034544526 0.037986779 0.041429032 0.04487129 0.04831354 0.05175579
#> [43,] 0.029999827 0.033699724 0.037399621 0.04109952 0.04479941 0.04849931
#> [44,] 0.025455129 0.029412669 0.033370209 0.03732775 0.04128529 0.04524283
#> [45,] 0.020910430 0.025125614 0.029340798 0.03355598 0.03777117 0.04198635
#> [46,] 0.016365732 0.020838559 0.025311387 0.02978421 0.03425704 0.03872987
#> [47,] 0.011821033 0.016551504 0.021281975 0.02601245 0.03074292 0.03547339
#> [48,] 0.007276335 0.012264450 0.017252564 0.02224068 0.02722879 0.03221691
#> [49,] 0.002731636 0.007977395 0.013223153 0.01846891 0.02371467 0.02896043
#> [50,] -0.001813062 0.003690340 0.009193742 0.01469714 0.02020055 0.02570395
#> [,17] [,18] [,19] [,20] [,21] [,22]
#> [1,] 0.17815036 0.17102922 0.16390809 0.15678695 0.14966582 0.14254468
#> [2,] 0.17515152 0.16828803 0.16142454 0.15456105 0.14769756 0.14083407
#> [3,] 0.17215268 0.16554684 0.15894099 0.15233514 0.14572929 0.13912345
#> [4,] 0.16915385 0.16280564 0.15645744 0.15010924 0.14376103 0.13741283
#> [5,] 0.16615501 0.16006445 0.15397389 0.14788333 0.14179277 0.13570221
#> [6,] 0.16315617 0.15732326 0.15149034 0.14565742 0.13982451 0.13399159
#> [7,] 0.16015734 0.15458206 0.14900679 0.14343152 0.13785624 0.13228097
#> [8,] 0.15715850 0.15184087 0.14652324 0.14120561 0.13588798 0.13057035
#> [9,] 0.15415966 0.14909968 0.14403969 0.13897970 0.13391972 0.12885973
#> [10,] 0.15116082 0.14635848 0.14155614 0.13675380 0.13195146 0.12714911
#> [11,] 0.14816199 0.14361729 0.13907259 0.13452789 0.12998319 0.12543850
#> [12,] 0.14516315 0.14087610 0.13658904 0.13230199 0.12801493 0.12372788
#> [13,] 0.14216431 0.13813490 0.13410549 0.13007608 0.12604667 0.12201726
#> [14,] 0.13916548 0.13539371 0.13162194 0.12785017 0.12407841 0.12030664
#> [15,] 0.13616664 0.13265252 0.12913839 0.12562427 0.12211014 0.11859602
#> [16,] 0.13316780 0.12991132 0.12665484 0.12339836 0.12014188 0.11688540
#> [17,] 0.13016897 0.12717013 0.12417129 0.12117246 0.11817362 0.11517478
#> [18,] 0.12717013 0.12442894 0.12168774 0.11894655 0.11620536 0.11346416
#> [19,] 0.12417129 0.12168774 0.11920419 0.11672064 0.11423709 0.11175354
#> [20,] 0.12117246 0.11894655 0.11672064 0.11449474 0.11226883 0.11004293
#> [21,] 0.11817362 0.11620536 0.11423709 0.11226883 0.11030057 0.10833231
#> [22,] 0.11517478 0.11346416 0.11175354 0.11004293 0.10833231 0.10662169
#> [23,] 0.11217595 0.11072297 0.10926999 0.10781702 0.10636404 0.10491107
#> [24,] 0.10917711 0.10798178 0.10678645 0.10559111 0.10439578 0.10320045
#> [25,] 0.10617827 0.10524058 0.10430290 0.10336521 0.10242752 0.10148983
#> [26,] 0.10317943 0.10249939 0.10181935 0.10113930 0.10045926 0.09977921
#> [27,] 0.10018060 0.09975820 0.09933580 0.09891340 0.09849099 0.09806859
#> [28,] 0.09718176 0.09701700 0.09685225 0.09668749 0.09652273 0.09635797
#> [29,] 0.09418292 0.09427581 0.09436870 0.09446158 0.09455447 0.09464736
#> [30,] 0.09118409 0.09153462 0.09188515 0.09223568 0.09258621 0.09293674
#> [31,] 0.08818525 0.08879342 0.08940160 0.09000977 0.09061794 0.09122612
#> [32,] 0.08518641 0.08605223 0.08691805 0.08778386 0.08864968 0.08951550
#> [33,] 0.08218758 0.08331104 0.08443450 0.08555796 0.08668142 0.08780488
#> [34,] 0.07918874 0.08056984 0.08195095 0.08333205 0.08471316 0.08609426
#> [35,] 0.07618990 0.07782865 0.07946740 0.08110615 0.08274489 0.08438364
#> [36,] 0.07319107 0.07508746 0.07698385 0.07888024 0.08077663 0.08267302
#> [37,] 0.07019223 0.07234626 0.07450030 0.07665433 0.07880837 0.08096240
#> [38,] 0.06719339 0.06960507 0.07201675 0.07442843 0.07684011 0.07925179
#> [39,] 0.06419455 0.06686388 0.06953320 0.07220252 0.07487184 0.07754117
#> [40,] 0.06119572 0.06412268 0.06704965 0.06997662 0.07290358 0.07583055
#> [41,] 0.05819688 0.06138149 0.06456610 0.06775071 0.07093532 0.07411993
#> [42,] 0.05519804 0.05864030 0.06208255 0.06552480 0.06896706 0.07240931
#> [43,] 0.05219921 0.05589910 0.05959900 0.06329890 0.06699879 0.07069869
#> [44,] 0.04920037 0.05315791 0.05711545 0.06107299 0.06503053 0.06898807
#> [45,] 0.04620153 0.05041672 0.05463190 0.05884709 0.06306227 0.06727745
#> [46,] 0.04320270 0.04767552 0.05214835 0.05662118 0.06109401 0.06556683
#> [47,] 0.04020386 0.04493433 0.04966480 0.05439527 0.05912574 0.06385622
#> [48,] 0.03720502 0.04219314 0.04718125 0.05216937 0.05715748 0.06214560
#> [49,] 0.03420619 0.03945194 0.04469770 0.04994346 0.05518922 0.06043498
#> [50,] 0.03120735 0.03671075 0.04221415 0.04771755 0.05322096 0.05872436
#> [,23] [,24] [,25] [,26] [,27] [,28]
#> [1,] 0.13542355 0.12830242 0.12118128 0.11406015 0.10693901 0.09981788
#> [2,] 0.13397057 0.12710708 0.12024359 0.11338010 0.10651661 0.09965312
#> [3,] 0.13251760 0.12591175 0.11930590 0.11270006 0.10609421 0.09948836
#> [4,] 0.13106462 0.12471642 0.11836822 0.11202001 0.10567181 0.09932361
#> [5,] 0.12961165 0.12352109 0.11743053 0.11133997 0.10524941 0.09915885
#> [6,] 0.12815867 0.12232576 0.11649284 0.11065992 0.10482701 0.09899409
#> [7,] 0.12670570 0.12113043 0.11555515 0.10997988 0.10440461 0.09882933
#> [8,] 0.12525272 0.11993509 0.11461746 0.10929984 0.10398221 0.09866458
#> [9,] 0.12379975 0.11873976 0.11367978 0.10861979 0.10355980 0.09849982
#> [10,] 0.12234677 0.11754443 0.11274209 0.10793975 0.10313740 0.09833506
#> [11,] 0.12089380 0.11634910 0.11180440 0.10725970 0.10271500 0.09817030
#> [12,] 0.11944082 0.11515377 0.11086671 0.10657966 0.10229260 0.09800555
#> [13,] 0.11798785 0.11395844 0.10992902 0.10589961 0.10187020 0.09784079
#> [14,] 0.11653487 0.11276310 0.10899134 0.10521957 0.10144780 0.09767603
#> [15,] 0.11508190 0.11156777 0.10805365 0.10453952 0.10102540 0.09751128
#> [16,] 0.11362892 0.11037244 0.10711596 0.10385948 0.10060300 0.09734652
#> [17,] 0.11217595 0.10917711 0.10617827 0.10317943 0.10018060 0.09718176
#> [18,] 0.11072297 0.10798178 0.10524058 0.10249939 0.09975820 0.09701700
#> [19,] 0.10926999 0.10678645 0.10430290 0.10181935 0.09933580 0.09685225
#> [20,] 0.10781702 0.10559111 0.10336521 0.10113930 0.09891340 0.09668749
#> [21,] 0.10636404 0.10439578 0.10242752 0.10045926 0.09849099 0.09652273
#> [22,] 0.10491107 0.10320045 0.10148983 0.09977921 0.09806859 0.09635797
#> [23,] 0.10345809 0.10200512 0.10055214 0.09909917 0.09764619 0.09619322
#> [24,] 0.10200512 0.10080979 0.09961445 0.09841912 0.09722379 0.09602846
#> [25,] 0.10055214 0.09961445 0.09867677 0.09773908 0.09680139 0.09586370
#> [26,] 0.09909917 0.09841912 0.09773908 0.09705903 0.09637899 0.09569895
#> [27,] 0.09764619 0.09722379 0.09680139 0.09637899 0.09595659 0.09553419
#> [28,] 0.09619322 0.09602846 0.09586370 0.09569895 0.09553419 0.09536943
#> [29,] 0.09474024 0.09483313 0.09492601 0.09501890 0.09511179 0.09520467
#> [30,] 0.09328727 0.09363780 0.09398833 0.09433886 0.09468939 0.09503992
#> [31,] 0.09183429 0.09244246 0.09305064 0.09365881 0.09426699 0.09487516
#> [32,] 0.09038132 0.09124713 0.09211295 0.09297877 0.09384458 0.09471040
#> [33,] 0.08892834 0.09005180 0.09117526 0.09229872 0.09342218 0.09454564
#> [34,] 0.08747537 0.08885647 0.09023757 0.09161868 0.09299978 0.09438089
#> [35,] 0.08602239 0.08766114 0.08929989 0.09093863 0.09257738 0.09421613
#> [36,] 0.08456941 0.08646581 0.08836220 0.09025859 0.09215498 0.09405137
#> [37,] 0.08311644 0.08527047 0.08742451 0.08957854 0.09173258 0.09388661
#> [38,] 0.08166346 0.08407514 0.08648682 0.08889850 0.09131018 0.09372186
#> [39,] 0.08021049 0.08287981 0.08554913 0.08821846 0.09088778 0.09355710
#> [40,] 0.07875751 0.08168448 0.08461145 0.08753841 0.09046538 0.09339234
#> [41,] 0.07730454 0.08048915 0.08367376 0.08685837 0.09004298 0.09322759
#> [42,] 0.07585156 0.07929382 0.08273607 0.08617832 0.08962058 0.09306283
#> [43,] 0.07439859 0.07809848 0.08179838 0.08549828 0.08919817 0.09289807
#> [44,] 0.07294561 0.07690315 0.08086069 0.08481823 0.08877577 0.09273331
#> [45,] 0.07149264 0.07570782 0.07992300 0.08413819 0.08835337 0.09256856
#> [46,] 0.07003966 0.07451249 0.07898532 0.08345814 0.08793097 0.09240380
#> [47,] 0.06858669 0.07331716 0.07804763 0.08277810 0.08750857 0.09223904
#> [48,] 0.06713371 0.07212183 0.07710994 0.08209806 0.08708617 0.09207428
#> [49,] 0.06568074 0.07092649 0.07617225 0.08141801 0.08666377 0.09190953
#> [50,] 0.06422776 0.06973116 0.07523456 0.08073797 0.08624137 0.09174477
#> [,29] [,30] [,31] [,32] [,33] [,34]
#> [1,] 0.09269674 0.08557561 0.07845447 0.07133334 0.06421221 0.05709107
#> [2,] 0.09278963 0.08592614 0.07906265 0.07219916 0.06533567 0.05847218
#> [3,] 0.09288252 0.08627667 0.07967082 0.07306497 0.06645913 0.05985328
#> [4,] 0.09297540 0.08662720 0.08027899 0.07393079 0.06758259 0.06123438
#> [5,] 0.09306829 0.08697773 0.08088717 0.07479661 0.06870605 0.06261549
#> [6,] 0.09316117 0.08732826 0.08149534 0.07566243 0.06982951 0.06399659
#> [7,] 0.09325406 0.08767879 0.08210352 0.07652824 0.07095297 0.06537770
#> [8,] 0.09334695 0.08802932 0.08271169 0.07739406 0.07207643 0.06675880
#> [9,] 0.09343983 0.08837985 0.08331986 0.07825988 0.07319989 0.06813991
#> [10,] 0.09353272 0.08873038 0.08392804 0.07912569 0.07432335 0.06952101
#> [11,] 0.09362561 0.08908091 0.08453621 0.07999151 0.07544681 0.07090211
#> [12,] 0.09371849 0.08943144 0.08514438 0.08085733 0.07657027 0.07228322
#> [13,] 0.09381138 0.08978197 0.08575256 0.08172314 0.07769373 0.07366432
#> [14,] 0.09390427 0.09013250 0.08636073 0.08258896 0.07881719 0.07504543
#> [15,] 0.09399715 0.09048303 0.08696890 0.08345478 0.07994065 0.07642653
#> [16,] 0.09409004 0.09083356 0.08757708 0.08432060 0.08106412 0.07780764
#> [17,] 0.09418292 0.09118409 0.08818525 0.08518641 0.08218758 0.07918874
#> [18,] 0.09427581 0.09153462 0.08879342 0.08605223 0.08331104 0.08056984
#> [19,] 0.09436870 0.09188515 0.08940160 0.08691805 0.08443450 0.08195095
#> [20,] 0.09446158 0.09223568 0.09000977 0.08778386 0.08555796 0.08333205
#> [21,] 0.09455447 0.09258621 0.09061794 0.08864968 0.08668142 0.08471316
#> [22,] 0.09464736 0.09293674 0.09122612 0.08951550 0.08780488 0.08609426
#> [23,] 0.09474024 0.09328727 0.09183429 0.09038132 0.08892834 0.08747537
#> [24,] 0.09483313 0.09363780 0.09244246 0.09124713 0.09005180 0.08885647
#> [25,] 0.09492601 0.09398833 0.09305064 0.09211295 0.09117526 0.09023757
#> [26,] 0.09501890 0.09433886 0.09365881 0.09297877 0.09229872 0.09161868
#> [27,] 0.09511179 0.09468939 0.09426699 0.09384458 0.09342218 0.09299978
#> [28,] 0.09520467 0.09503992 0.09487516 0.09471040 0.09454564 0.09438089
#> [29,] 0.09529756 0.09539045 0.09548333 0.09557622 0.09566910 0.09576199
#> [30,] 0.09539045 0.09574098 0.09609151 0.09644204 0.09679257 0.09714310
#> [31,] 0.09548333 0.09609151 0.09669968 0.09730785 0.09791603 0.09852420
#> [32,] 0.09557622 0.09644204 0.09730785 0.09817367 0.09903949 0.09990530
#> [33,] 0.09566910 0.09679257 0.09791603 0.09903949 0.10016295 0.10128641
#> [34,] 0.09576199 0.09714310 0.09852420 0.09990530 0.10128641 0.10266751
#> [35,] 0.09585488 0.09749363 0.09913237 0.10077112 0.10240987 0.10404862
#> [36,] 0.09594776 0.09784416 0.09974055 0.10163694 0.10353333 0.10542972
#> [37,] 0.09604065 0.09819468 0.10034872 0.10250276 0.10465679 0.10681083
#> [38,] 0.09613354 0.09854521 0.10095689 0.10336857 0.10578025 0.10819193
#> [39,] 0.09622642 0.09889574 0.10156507 0.10423439 0.10690371 0.10957303
#> [40,] 0.09631931 0.09924627 0.10217324 0.10510021 0.10802717 0.11095414
#> [41,] 0.09641220 0.09959680 0.10278141 0.10596602 0.10915063 0.11233524
#> [42,] 0.09650508 0.09994733 0.10338959 0.10683184 0.11027409 0.11371635
#> [43,] 0.09659797 0.10029786 0.10399776 0.10769766 0.11139755 0.11509745
#> [44,] 0.09669085 0.10064839 0.10460593 0.10856347 0.11252102 0.11647856
#> [45,] 0.09678374 0.10099892 0.10521411 0.10942929 0.11364448 0.11785966
#> [46,] 0.09687663 0.10134945 0.10582228 0.11029511 0.11476794 0.11924076
#> [47,] 0.09696951 0.10169998 0.10643045 0.11116093 0.11589140 0.12062187
#> [48,] 0.09706240 0.10205051 0.10703863 0.11202674 0.11701486 0.12200297
#> [49,] 0.09715529 0.10240104 0.10764680 0.11289256 0.11813832 0.12338408
#> [50,] 0.09724817 0.10275157 0.10825498 0.11375838 0.11926178 0.12476518
#> [,35] [,36] [,37] [,38] [,39] [,40]
#> [1,] 0.04996994 0.04284880 0.03572767 0.02860653 0.02148540 0.01436426
#> [2,] 0.05160868 0.04474519 0.03788170 0.03101821 0.02415472 0.01729123
#> [3,] 0.05324743 0.04664158 0.04003574 0.03342989 0.02682404 0.02021820
#> [4,] 0.05488618 0.04853798 0.04218977 0.03584157 0.02949337 0.02314516
#> [5,] 0.05652493 0.05043437 0.04434381 0.03825325 0.03216269 0.02607213
#> [6,] 0.05816368 0.05233076 0.04649784 0.04066493 0.03483201 0.02899909
#> [7,] 0.05980242 0.05422715 0.04865188 0.04307661 0.03750133 0.03192606
#> [8,] 0.06144117 0.05612354 0.05080591 0.04548828 0.04017065 0.03485303
#> [9,] 0.06307992 0.05801993 0.05295995 0.04789996 0.04283998 0.03777999
#> [10,] 0.06471867 0.05991633 0.05511398 0.05031164 0.04550930 0.04070696
#> [11,] 0.06635742 0.06181272 0.05726802 0.05272332 0.04817862 0.04363392
#> [12,] 0.06799616 0.06370911 0.05942205 0.05513500 0.05084794 0.04656089
#> [13,] 0.06963491 0.06560550 0.06157609 0.05754668 0.05351727 0.04948785
#> [14,] 0.07127366 0.06750189 0.06373012 0.05995836 0.05618659 0.05241482
#> [15,] 0.07291241 0.06939828 0.06588416 0.06237003 0.05885591 0.05534179
#> [16,] 0.07455115 0.07129467 0.06803819 0.06478171 0.06152523 0.05826875
#> [17,] 0.07618990 0.07319107 0.07019223 0.06719339 0.06419455 0.06119572
#> [18,] 0.07782865 0.07508746 0.07234626 0.06960507 0.06686388 0.06412268
#> [19,] 0.07946740 0.07698385 0.07450030 0.07201675 0.06953320 0.06704965
#> [20,] 0.08110615 0.07888024 0.07665433 0.07442843 0.07220252 0.06997662
#> [21,] 0.08274489 0.08077663 0.07880837 0.07684011 0.07487184 0.07290358
#> [22,] 0.08438364 0.08267302 0.08096240 0.07925179 0.07754117 0.07583055
#> [23,] 0.08602239 0.08456941 0.08311644 0.08166346 0.08021049 0.07875751
#> [24,] 0.08766114 0.08646581 0.08527047 0.08407514 0.08287981 0.08168448
#> [25,] 0.08929989 0.08836220 0.08742451 0.08648682 0.08554913 0.08461145
#> [26,] 0.09093863 0.09025859 0.08957854 0.08889850 0.08821846 0.08753841
#> [27,] 0.09257738 0.09215498 0.09173258 0.09131018 0.09088778 0.09046538
#> [28,] 0.09421613 0.09405137 0.09388661 0.09372186 0.09355710 0.09339234
#> [29,] 0.09585488 0.09594776 0.09604065 0.09613354 0.09622642 0.09631931
#> [30,] 0.09749363 0.09784416 0.09819468 0.09854521 0.09889574 0.09924627
#> [31,] 0.09913237 0.09974055 0.10034872 0.10095689 0.10156507 0.10217324
#> [32,] 0.10077112 0.10163694 0.10250276 0.10336857 0.10423439 0.10510021
#> [33,] 0.10240987 0.10353333 0.10465679 0.10578025 0.10690371 0.10802717
#> [34,] 0.10404862 0.10542972 0.10681083 0.10819193 0.10957303 0.11095414
#> [35,] 0.10568736 0.10732611 0.10896486 0.11060361 0.11224236 0.11388110
#> [36,] 0.10732611 0.10922250 0.11111890 0.11301529 0.11491168 0.11680807
#> [37,] 0.10896486 0.11111890 0.11327293 0.11542697 0.11758100 0.11973504
#> [38,] 0.11060361 0.11301529 0.11542697 0.11783864 0.12025032 0.12266200
#> [39,] 0.11224236 0.11491168 0.11758100 0.12025032 0.12291965 0.12558897
#> [40,] 0.11388110 0.11680807 0.11973504 0.12266200 0.12558897 0.12851593
#> [41,] 0.11551985 0.11870446 0.12188907 0.12507368 0.12825829 0.13144290
#> [42,] 0.11715860 0.12060085 0.12404311 0.12748536 0.13092761 0.13436987
#> [43,] 0.11879735 0.12249724 0.12619714 0.12989704 0.13359693 0.13729683
#> [44,] 0.12043610 0.12439364 0.12835118 0.13230872 0.13626626 0.14022380
#> [45,] 0.12207484 0.12629003 0.13050521 0.13472040 0.13893558 0.14315076
#> [46,] 0.12371359 0.12818642 0.13265925 0.13713207 0.14160490 0.14607773
#> [47,] 0.12535234 0.13008281 0.13481328 0.13954375 0.14427422 0.14900469
#> [48,] 0.12699109 0.13197920 0.13696732 0.14195543 0.14694355 0.15193166
#> [49,] 0.12862984 0.13387559 0.13912135 0.14436711 0.14961287 0.15485863
#> [50,] 0.13026858 0.13577198 0.14127539 0.14677879 0.15228219 0.15778559
#> [,41] [,42] [,43] [,44] [,45]
#> [1,] 0.007243129 0.0001219949 -0.0069991395 -0.014120274 -0.0212414085
#> [2,] 0.010427739 0.0035642480 -0.0032992429 -0.010162734 -0.0170262246
#> [3,] 0.013612348 0.0070065011 0.0004006538 -0.006205193 -0.0128110407
#> [4,] 0.016796958 0.0104487542 0.0041005505 -0.002247653 -0.0085958569
#> [5,] 0.019981567 0.0138910073 0.0078004472 0.001709887 -0.0043806730
#> [6,] 0.023166177 0.0173332604 0.0115003439 0.005667427 -0.0001654891
#> [7,] 0.026350786 0.0207755135 0.0152002406 0.009624968 0.0040496948
#> [8,] 0.029535396 0.0242177665 0.0189001372 0.013582508 0.0082648787
#> [9,] 0.032720005 0.0276600196 0.0226000339 0.017540048 0.0124800626
#> [10,] 0.035904615 0.0311022727 0.0262999306 0.021497589 0.0166952464
#> [11,] 0.039089224 0.0345445258 0.0299998273 0.025455129 0.0209104303
#> [12,] 0.042273834 0.0379867789 0.0336997240 0.029412669 0.0251256142
#> [13,] 0.045458443 0.0414290320 0.0373996207 0.033370209 0.0293407981
#> [14,] 0.048643053 0.0448712850 0.0410995174 0.037327750 0.0335559820
#> [15,] 0.051827662 0.0483135381 0.0447994140 0.041285290 0.0377711658
#> [16,] 0.055012272 0.0517557912 0.0484993107 0.045242830 0.0419863497
#> [17,] 0.058196881 0.0551980443 0.0521992074 0.049200371 0.0462015336
#> [18,] 0.061381491 0.0586402974 0.0558991041 0.053157911 0.0504167175
#> [19,] 0.064566100 0.0620825505 0.0595990008 0.057115451 0.0546319014
#> [20,] 0.067750710 0.0655248036 0.0632988975 0.061072991 0.0588470853
#> [21,] 0.070935319 0.0689670566 0.0669987941 0.065030532 0.0630622691
#> [22,] 0.074119929 0.0724093097 0.0706986908 0.068988072 0.0672774530
#> [23,] 0.077304538 0.0758515628 0.0743985875 0.072945612 0.0714926369
#> [24,] 0.080489148 0.0792938159 0.0780984842 0.076903152 0.0757078208
#> [25,] 0.083673757 0.0827360690 0.0817983809 0.080860693 0.0799230047
#> [26,] 0.086858367 0.0861783221 0.0854982776 0.084818233 0.0841381885
#> [27,] 0.090042976 0.0896205751 0.0891981742 0.088775773 0.0883533724
#> [28,] 0.093227586 0.0930628282 0.0928980709 0.092733314 0.0925685563
#> [29,] 0.096412195 0.0965050813 0.0965979676 0.096690854 0.0967837402
#> [30,] 0.099596805 0.0999473344 0.1002978643 0.100648394 0.1009989241
#> [31,] 0.102781414 0.1033895875 0.1039977610 0.104605934 0.1052141080
#> [32,] 0.105966023 0.1068318406 0.1076976577 0.108563475 0.1094292918
#> [33,] 0.109150633 0.1102740936 0.1113975543 0.112521015 0.1136444757
#> [34,] 0.112335242 0.1137163467 0.1150974510 0.116478555 0.1178596596
#> [35,] 0.115519852 0.1171585998 0.1187973477 0.120436096 0.1220748435
#> [36,] 0.118704461 0.1206008529 0.1224972444 0.124393636 0.1262900274
#> [37,] 0.121889071 0.1240431060 0.1261971411 0.128351176 0.1305052112
#> [38,] 0.125073680 0.1274853591 0.1298970378 0.132308716 0.1347203951
#> [39,] 0.128258290 0.1309276122 0.1335969344 0.136266257 0.1389355790
#> [40,] 0.131442899 0.1343698652 0.1372968311 0.140223797 0.1431507629
#> [41,] 0.134627509 0.1378121183 0.1409967278 0.144181337 0.1473659468
#> [42,] 0.137812118 0.1412543714 0.1446966245 0.148138878 0.1515811307
#> [43,] 0.140996728 0.1446966245 0.1483965212 0.152096418 0.1557963145
#> [44,] 0.144181337 0.1481388776 0.1520964179 0.156053958 0.1600114984
#> [45,] 0.147365947 0.1515811307 0.1557963145 0.160011498 0.1642266823
#> [46,] 0.150550556 0.1550233837 0.1594962112 0.163969039 0.1684418662
#> [47,] 0.153735166 0.1584656368 0.1631961079 0.167926579 0.1726570501
#> [48,] 0.156919775 0.1619078899 0.1668960046 0.171884119 0.1768722340
#> [49,] 0.160104385 0.1653501430 0.1705959013 0.175841660 0.1810874178
#> [50,] 0.163288994 0.1687923961 0.1742957980 0.179799200 0.1853026017
#> [,46] [,47] [,48] [,49] [,50]
#> [1,] -0.028362543 -0.035483677 -0.042604812 -0.049725946 -0.056847081
#> [2,] -0.023889716 -0.030753206 -0.037616697 -0.044480188 -0.051343679
#> [3,] -0.019416888 -0.026022735 -0.032628583 -0.039234430 -0.045840277
#> [4,] -0.014944061 -0.021292264 -0.027640468 -0.033988672 -0.040336875
#> [5,] -0.010471233 -0.016561793 -0.022652353 -0.028742913 -0.034833473
#> [6,] -0.005998406 -0.011831322 -0.017664239 -0.023497155 -0.029330072
#> [7,] -0.001525578 -0.007100851 -0.012676124 -0.018251397 -0.023826670
#> [8,] 0.002947249 -0.002370380 -0.007688009 -0.013005638 -0.018323268
#> [9,] 0.007420077 0.002360091 -0.002699895 -0.007759880 -0.012819866
#> [10,] 0.011892904 0.007090562 0.002288220 -0.002514122 -0.007316464
#> [11,] 0.016365732 0.011821033 0.007276335 0.002731636 -0.001813062
#> [12,] 0.020838559 0.016551504 0.012264450 0.007977395 0.003690340
#> [13,] 0.025311387 0.021281975 0.017252564 0.013223153 0.009193742
#> [14,] 0.029784214 0.026012447 0.022240679 0.018468911 0.014697143
#> [15,] 0.034257042 0.030742918 0.027228794 0.023714669 0.020200545
#> [16,] 0.038729869 0.035473389 0.032216908 0.028960428 0.025703947
#> [17,] 0.043202697 0.040203860 0.037205023 0.034206186 0.031207349
#> [18,] 0.047675524 0.044934331 0.042193138 0.039451944 0.036710751
#> [19,] 0.052148352 0.049664802 0.047181252 0.044697703 0.042214153
#> [20,] 0.056621179 0.054395273 0.052169367 0.049943461 0.047717555
#> [21,] 0.061094007 0.059125744 0.057157482 0.055189219 0.053220957
#> [22,] 0.065566834 0.063856215 0.062145596 0.060434977 0.058724359
#> [23,] 0.070039662 0.068586686 0.067133711 0.065680736 0.064227760
#> [24,] 0.074512489 0.073317157 0.072121826 0.070926494 0.069731162
#> [25,] 0.078985317 0.078047628 0.077109940 0.076172252 0.075234564
#> [26,] 0.083458144 0.082778100 0.082098055 0.081418011 0.080737966
#> [27,] 0.087930972 0.087508571 0.087086170 0.086663769 0.086241368
#> [28,] 0.092403799 0.092239042 0.092074284 0.091909527 0.091744770
#> [29,] 0.096876626 0.096969513 0.097062399 0.097155285 0.097248172
#> [30,] 0.101349454 0.101699984 0.102050514 0.102401044 0.102751574
#> [31,] 0.105822281 0.106430455 0.107038628 0.107646802 0.108254975
#> [32,] 0.110295109 0.111160926 0.112026743 0.112892560 0.113758377
#> [33,] 0.114767936 0.115891397 0.117014858 0.118138318 0.119261779
#> [34,] 0.119240764 0.120621868 0.122002972 0.123384077 0.124765181
#> [35,] 0.123713591 0.125352339 0.126991087 0.128629835 0.130268583
#> [36,] 0.128186419 0.130082810 0.131979202 0.133875593 0.135771985
#> [37,] 0.132659246 0.134813281 0.136967317 0.139121352 0.141275387
#> [38,] 0.137132074 0.139543753 0.141955431 0.144367110 0.146778789
#> [39,] 0.141604901 0.144274224 0.146943546 0.149612868 0.152282190
#> [40,] 0.146077729 0.149004695 0.151931661 0.154858626 0.157785592
#> [41,] 0.150550556 0.153735166 0.156919775 0.160104385 0.163288994
#> [42,] 0.155023384 0.158465637 0.161907890 0.165350143 0.168792396
#> [43,] 0.159496211 0.163196108 0.166896005 0.170595901 0.174295798
#> [44,] 0.163969039 0.167926579 0.171884119 0.175841660 0.179799200
#> [45,] 0.168441866 0.172657050 0.176872234 0.181087418 0.185302602
#> [46,] 0.172914694 0.177387521 0.181860349 0.186333176 0.190806004
#> [47,] 0.177387521 0.182117992 0.186848463 0.191578934 0.196309405
#> [48,] 0.181860349 0.186848463 0.191836578 0.196824693 0.201812807
#> [49,] 0.186333176 0.191578934 0.196824693 0.202070451 0.207316209
#> [50,] 0.190806004 0.196309405 0.201812807 0.207316209 0.212819611
#>
#> $vcov.matrix$`40-67`
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.84637085 0.822524130 0.7986774122 0.774830694 0.75098398
#> [2,] 0.82252413 0.799496421 0.7764687124 0.753441003 0.73041329
#> [3,] 0.79867741 0.776468712 0.7542600126 0.732051313 0.70984261
#> [4,] 0.77483069 0.753441003 0.7320513129 0.710661622 0.68927193
#> [5,] 0.75098398 0.730413295 0.7098426131 0.689271932 0.66870125
#> [6,] 0.72713726 0.707385586 0.6876339133 0.667882241 0.64813057
#> [7,] 0.70329054 0.684357877 0.6654252136 0.646492550 0.62755989
#> [8,] 0.67944382 0.661330168 0.6432165138 0.625102860 0.60698921
#> [9,] 0.65559710 0.638302459 0.6210078140 0.603713169 0.58641852
#> [10,] 0.63175039 0.615274750 0.5987991143 0.582323479 0.56584784
#> [11,] 0.60790367 0.592247041 0.5765904145 0.560933788 0.54527716
#> [12,] 0.58405695 0.569219332 0.5543817147 0.539544097 0.52470648
#> [13,] 0.56021023 0.546191623 0.5321730150 0.518154407 0.50413580
#> [14,] 0.53636351 0.523163914 0.5099643152 0.496764716 0.48356512
#> [15,] 0.51251679 0.500136205 0.4877556155 0.475375026 0.46299444
#> [16,] 0.48867008 0.477108496 0.4655469157 0.453985335 0.44242375
#> [17,] 0.46482336 0.454080787 0.4433382159 0.432595645 0.42185307
#> [18,] 0.44097664 0.431053078 0.4211295162 0.411205954 0.40128239
#> [19,] 0.41712992 0.408025369 0.3989208164 0.389816263 0.38071171
#> [20,] 0.39328320 0.384997661 0.3767121166 0.368426573 0.36014103
#> [21,] 0.36943649 0.361969952 0.3545034169 0.347036882 0.33957035
#> [22,] 0.34558977 0.338942243 0.3322947171 0.325647192 0.31899967
#> [23,] 0.32174305 0.315914534 0.3100860173 0.304257501 0.29842898
#> [24,] 0.29789633 0.292886825 0.2878773176 0.282867810 0.27785830
#> [25,] 0.27404961 0.269859116 0.2656686178 0.261478120 0.25728762
#> [26,] 0.25020290 0.246831407 0.2434599180 0.240088429 0.23671694
#> [27,] 0.22635618 0.223803698 0.2212512183 0.218698739 0.21614626
#> [28,] 0.20250946 0.200775989 0.1990425185 0.197309048 0.19557558
#> [29,] 0.17866274 0.177748280 0.1768338187 0.175919357 0.17500490
#> [30,] 0.15481602 0.154720571 0.1546251190 0.154529667 0.15443421
#> [31,] 0.13096931 0.131692862 0.1324164192 0.133139976 0.13386353
#> [32,] 0.10712259 0.108665153 0.1102077195 0.111750286 0.11329285
#> [33,] 0.08327587 0.085637444 0.0879990197 0.090360595 0.09272217
#> [34,] 0.05942915 0.062609735 0.0657903199 0.068970904 0.07215149
#> [35,] 0.03558243 0.039582027 0.0435816202 0.047581214 0.05158081
#> [36,] 0.01173571 0.016554318 0.0213729204 0.026191523 0.03101013
#> [37,] -0.01211100 -0.006473391 -0.0008357794 0.004801833 0.01043944
#> [38,] -0.03595772 -0.029501100 -0.0230444791 -0.016587858 -0.01013124
#> [39,] -0.05980444 -0.052528809 -0.0452531789 -0.037977549 -0.03070192
#> [40,] -0.08365116 -0.075556518 -0.0674618787 -0.059367239 -0.05127260
#> [41,] -0.10749788 -0.098584227 -0.0896705784 -0.080756930 -0.07184328
#> [42,] -0.13134459 -0.121611936 -0.1118792782 -0.102146620 -0.09241396
#> [43,] -0.15519131 -0.144639645 -0.1340879780 -0.123536311 -0.11298464
#> [44,] -0.17903803 -0.167667354 -0.1562966777 -0.144926002 -0.13355533
#> [45,] -0.20288475 -0.190695063 -0.1785053775 -0.166315692 -0.15412601
#> [46,] -0.22673147 -0.213722772 -0.2007140773 -0.187705383 -0.17469669
#> [47,] -0.25057818 -0.236750481 -0.2229227770 -0.209095073 -0.19526737
#> [48,] -0.27442490 -0.259778190 -0.2451314768 -0.230484764 -0.21583805
#> [49,] -0.29827162 -0.282805899 -0.2673401765 -0.251874455 -0.23640873
#> [50,] -0.32211834 -0.305833607 -0.2895488763 -0.273264145 -0.25697941
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 0.727137258 0.703290540 0.679443822 0.655597104 0.631750385
#> [2,] 0.707385586 0.684357877 0.661330168 0.638302459 0.615274750
#> [3,] 0.687633913 0.665425214 0.643216514 0.621007814 0.598799114
#> [4,] 0.667882241 0.646492550 0.625102860 0.603713169 0.582323479
#> [5,] 0.648130569 0.627559887 0.606989206 0.586418525 0.565847843
#> [6,] 0.628378897 0.608627224 0.588875552 0.569123880 0.549372208
#> [7,] 0.608627224 0.589694561 0.570761898 0.551829235 0.532896572
#> [8,] 0.588875552 0.570761898 0.552648244 0.534534590 0.516420936
#> [9,] 0.569123880 0.551829235 0.534534590 0.517239946 0.499945301
#> [10,] 0.549372208 0.532896572 0.516420936 0.499945301 0.483469665
#> [11,] 0.529620535 0.513963909 0.498307282 0.482650656 0.466994030
#> [12,] 0.509868863 0.495031246 0.480193629 0.465356011 0.450518394
#> [13,] 0.490117191 0.476098583 0.462079975 0.448061367 0.434042758
#> [14,] 0.470365518 0.457165920 0.443966321 0.430766722 0.417567123
#> [15,] 0.450613846 0.438233256 0.425852667 0.413472077 0.401091487
#> [16,] 0.430862174 0.419300593 0.407739013 0.396177432 0.384615852
#> [17,] 0.411110502 0.400367930 0.389625359 0.378882787 0.368140216
#> [18,] 0.391358829 0.381435267 0.371511705 0.361588143 0.351664580
#> [19,] 0.371607157 0.362502604 0.353398051 0.344293498 0.335188945
#> [20,] 0.351855485 0.343569941 0.335284397 0.326998853 0.318713309
#> [21,] 0.332103813 0.324637278 0.317170743 0.309704208 0.302237674
#> [22,] 0.312352140 0.305704615 0.299057089 0.292409564 0.285762038
#> [23,] 0.292600468 0.286771952 0.280943435 0.275114919 0.269286403
#> [24,] 0.272848796 0.267839289 0.262829781 0.257820274 0.252810767
#> [25,] 0.253097124 0.248906626 0.244716128 0.240525629 0.236335131
#> [26,] 0.233345451 0.229973962 0.226602474 0.223230985 0.219859496
#> [27,] 0.213593779 0.211041299 0.208488820 0.205936340 0.203383860
#> [28,] 0.193842107 0.192108636 0.190375166 0.188641695 0.186908225
#> [29,] 0.174090435 0.173175973 0.172261512 0.171347050 0.170432589
#> [30,] 0.154338762 0.154243310 0.154147858 0.154052406 0.153956953
#> [31,] 0.134587090 0.135310647 0.136034204 0.136757761 0.137481318
#> [32,] 0.114835418 0.116377984 0.117920550 0.119463116 0.121005682
#> [33,] 0.095083746 0.097445321 0.099806896 0.102168471 0.104530047
#> [34,] 0.075332073 0.078512658 0.081693242 0.084873827 0.088054411
#> [35,] 0.055580401 0.059579995 0.063579588 0.067579182 0.071578776
#> [36,] 0.035828729 0.040647332 0.045465934 0.050284537 0.055103140
#> [37,] 0.016077057 0.021714668 0.027352280 0.032989892 0.038627504
#> [38,] -0.003674616 0.002782005 0.009238627 0.015695248 0.022151869
#> [39,] -0.023426288 -0.016150658 -0.008875027 -0.001599397 0.005676233
#> [40,] -0.043177960 -0.035083321 -0.026988681 -0.018894042 -0.010799402
#> [41,] -0.062929633 -0.054015984 -0.045102335 -0.036188687 -0.027275038
#> [42,] -0.082681305 -0.072948647 -0.063215989 -0.053483331 -0.043750674
#> [43,] -0.102432977 -0.091881310 -0.081329643 -0.070777976 -0.060226309
#> [44,] -0.122184649 -0.110813973 -0.099443297 -0.088072621 -0.076701945
#> [45,] -0.141936322 -0.129746636 -0.117556951 -0.105367266 -0.093177580
#> [46,] -0.161687994 -0.148679299 -0.135670605 -0.122661910 -0.109653216
#> [47,] -0.181439666 -0.167611962 -0.153784259 -0.139956555 -0.126128851
#> [48,] -0.201191338 -0.186544626 -0.171897913 -0.157251200 -0.142604487
#> [49,] -0.220943011 -0.205477289 -0.190011567 -0.174545845 -0.159080123
#> [50,] -0.240694683 -0.224409952 -0.208125221 -0.191840489 -0.175555758
#> [,11] [,12] [,13] [,14] [,15]
#> [1,] 0.607903667 0.584056949 0.5602102311 0.536363513 0.512516795
#> [2,] 0.592247041 0.569219332 0.5461916231 0.523163914 0.500136205
#> [3,] 0.576590415 0.554381715 0.5321730150 0.509964315 0.487755615
#> [4,] 0.560933788 0.539544097 0.5181544069 0.496764716 0.475375026
#> [5,] 0.545277162 0.524706480 0.5041357988 0.483565117 0.462994436
#> [6,] 0.529620535 0.509868863 0.4901171907 0.470365518 0.450613846
#> [7,] 0.513963909 0.495031246 0.4760985827 0.457165920 0.438233256
#> [8,] 0.498307282 0.480193629 0.4620799746 0.443966321 0.425852667
#> [9,] 0.482650656 0.465356011 0.4480613665 0.430766722 0.413472077
#> [10,] 0.466994030 0.450518394 0.4340427584 0.417567123 0.401091487
#> [11,] 0.451337403 0.435680777 0.4200241503 0.404367524 0.388710898
#> [12,] 0.435680777 0.420843160 0.4060055423 0.391167925 0.376330308
#> [13,] 0.420024150 0.406005542 0.3919869342 0.377968326 0.363949718
#> [14,] 0.404367524 0.391167925 0.3779683261 0.364768727 0.351569128
#> [15,] 0.388710898 0.376330308 0.3639497180 0.351569128 0.339188539
#> [16,] 0.373054271 0.361492691 0.3499311099 0.338369529 0.326807949
#> [17,] 0.357397645 0.346655073 0.3359125019 0.325169930 0.314427359
#> [18,] 0.341741018 0.331817456 0.3218938938 0.311970332 0.302046769
#> [19,] 0.326084392 0.316979839 0.3078752857 0.298770733 0.289666180
#> [20,] 0.310427765 0.302142222 0.2938566776 0.285571134 0.277285590
#> [21,] 0.294771139 0.287304604 0.2798380695 0.272371535 0.264905000
#> [22,] 0.279114513 0.272466987 0.2658194615 0.259171936 0.252524410
#> [23,] 0.263457886 0.257629370 0.2518008534 0.245972337 0.240143821
#> [24,] 0.247801260 0.242791753 0.2377822453 0.232772738 0.227763231
#> [25,] 0.232144633 0.227954135 0.2237636372 0.219573139 0.215382641
#> [26,] 0.216488007 0.213116518 0.2097450291 0.206373540 0.203002051
#> [27,] 0.200831381 0.198278901 0.1957264211 0.193173941 0.190621462
#> [28,] 0.185174754 0.183441284 0.1817078130 0.179974342 0.178240872
#> [29,] 0.169518128 0.168603666 0.1676892049 0.166774744 0.165860282
#> [30,] 0.153861501 0.153766049 0.1536705968 0.153575145 0.153479692
#> [31,] 0.138204875 0.138928432 0.1396519887 0.140375546 0.141099103
#> [32,] 0.122548248 0.124090815 0.1256333807 0.127175947 0.128718513
#> [33,] 0.106891622 0.109253197 0.1116147726 0.113976348 0.116337923
#> [34,] 0.091234996 0.094415580 0.0975961645 0.100776749 0.103957333
#> [35,] 0.075578369 0.079577963 0.0835775564 0.087577150 0.091576744
#> [36,] 0.059921743 0.064740346 0.0695589483 0.074377551 0.079196154
#> [37,] 0.044265116 0.049902728 0.0555403403 0.061177952 0.066815564
#> [38,] 0.028608490 0.035065111 0.0415217322 0.047978353 0.054434974
#> [39,] 0.012951864 0.020227494 0.0275031241 0.034778754 0.042054385
#> [40,] -0.002704763 0.005389877 0.0134845160 0.021579155 0.029673795
#> [41,] -0.018361389 -0.009447741 -0.0005340921 0.008379557 0.017293205
#> [42,] -0.034018016 -0.024285358 -0.0145527001 -0.004820042 0.004912615
#> [43,] -0.049674642 -0.039122975 -0.0285713082 -0.018019641 -0.007467974
#> [44,] -0.065331269 -0.053960592 -0.0425899163 -0.031219240 -0.019848564
#> [45,] -0.080987895 -0.068798210 -0.0566085244 -0.044418839 -0.032229154
#> [46,] -0.096644521 -0.083635827 -0.0706271324 -0.057618438 -0.044609743
#> [47,] -0.112301148 -0.098473444 -0.0846457405 -0.070818037 -0.056990333
#> [48,] -0.127957774 -0.113311061 -0.0986643486 -0.084017636 -0.069370923
#> [49,] -0.143614401 -0.128148679 -0.1126829567 -0.097217235 -0.081751513
#> [50,] -0.159271027 -0.142986296 -0.1267015648 -0.110416834 -0.094132102
#> [,16] [,17] [,18] [,19] [,20]
#> [1,] 0.488670077 0.464823359 0.440976641 0.417129923 0.393283204
#> [2,] 0.477108496 0.454080787 0.431053078 0.408025369 0.384997661
#> [3,] 0.465546916 0.443338216 0.421129516 0.398920816 0.376712117
#> [4,] 0.453985335 0.432595645 0.411205954 0.389816263 0.368426573
#> [5,] 0.442423755 0.421853073 0.401282392 0.380711710 0.360141029
#> [6,] 0.430862174 0.411110502 0.391358829 0.371607157 0.351855485
#> [7,] 0.419300593 0.400367930 0.381435267 0.362502604 0.343569941
#> [8,] 0.407739013 0.389625359 0.371511705 0.353398051 0.335284397
#> [9,] 0.396177432 0.378882787 0.361588143 0.344293498 0.326998853
#> [10,] 0.384615852 0.368140216 0.351664580 0.335188945 0.318713309
#> [11,] 0.373054271 0.357397645 0.341741018 0.326084392 0.310427765
#> [12,] 0.361492691 0.346655073 0.331817456 0.316979839 0.302142222
#> [13,] 0.349931110 0.335912502 0.321893894 0.307875286 0.293856678
#> [14,] 0.338369529 0.325169930 0.311970332 0.298770733 0.285571134
#> [15,] 0.326807949 0.314427359 0.302046769 0.289666180 0.277285590
#> [16,] 0.315246368 0.303684788 0.292123207 0.280561626 0.269000046
#> [17,] 0.303684788 0.292942216 0.282199645 0.271457073 0.260714502
#> [18,] 0.292123207 0.282199645 0.272276083 0.262352520 0.252428958
#> [19,] 0.280561626 0.271457073 0.262352520 0.253247967 0.244143414
#> [20,] 0.269000046 0.260714502 0.252428958 0.244143414 0.235857870
#> [21,] 0.257438465 0.249971931 0.242505396 0.235038861 0.227572326
#> [22,] 0.245876885 0.239229359 0.232581834 0.225934308 0.219286783
#> [23,] 0.234315304 0.228486788 0.222658271 0.216829755 0.211001239
#> [24,] 0.222753724 0.217744216 0.212734709 0.207725202 0.202715695
#> [25,] 0.211192143 0.207001645 0.202811147 0.198620649 0.194430151
#> [26,] 0.199630562 0.196259074 0.192887585 0.189516096 0.186144607
#> [27,] 0.188068982 0.185516502 0.182964022 0.180411543 0.177859063
#> [28,] 0.176507401 0.174773931 0.173040460 0.171306990 0.169573519
#> [29,] 0.164945821 0.164031359 0.163116898 0.162202437 0.161287975
#> [30,] 0.153384240 0.153288788 0.153193336 0.153097884 0.153002431
#> [31,] 0.141822660 0.142546217 0.143269774 0.143993330 0.144716887
#> [32,] 0.130261079 0.131803645 0.133346211 0.134888777 0.136431344
#> [33,] 0.118699498 0.121061074 0.123422649 0.125784224 0.128145800
#> [34,] 0.107137918 0.110318502 0.113499087 0.116679671 0.119860256
#> [35,] 0.095576337 0.099575931 0.103575525 0.107575118 0.111574712
#> [36,] 0.084014757 0.088833360 0.093651962 0.098470565 0.103289168
#> [37,] 0.072453176 0.078090788 0.083728400 0.089366012 0.095003624
#> [38,] 0.060891596 0.067348217 0.073804838 0.080261459 0.086718080
#> [39,] 0.049330015 0.056605645 0.063881276 0.071156906 0.078432536
#> [40,] 0.037768434 0.045863074 0.053957713 0.062052353 0.070146992
#> [41,] 0.026206854 0.035120503 0.044034151 0.052947800 0.061861448
#> [42,] 0.014645273 0.024377931 0.034110589 0.043843247 0.053575905
#> [43,] 0.003083693 0.013635360 0.024187027 0.034738694 0.045290361
#> [44,] -0.008477888 0.002892788 0.014263464 0.025634141 0.037004817
#> [45,] -0.020039468 -0.007849783 0.004339902 0.016529588 0.028719273
#> [46,] -0.031601049 -0.018592355 -0.005583660 0.007425034 0.020433729
#> [47,] -0.043162630 -0.029334926 -0.015507222 -0.001679519 0.012148185
#> [48,] -0.054724210 -0.040077497 -0.025430785 -0.010784072 0.003862641
#> [49,] -0.066285791 -0.050820069 -0.035354347 -0.019888625 -0.004422903
#> [50,] -0.077847371 -0.061562640 -0.045277909 -0.028993178 -0.012708447
#> [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,] 0.369436486 0.34558977 0.32174305 0.29789633 0.27404961 0.25020290
#> [2,] 0.361969952 0.33894224 0.31591453 0.29288682 0.26985912 0.24683141
#> [3,] 0.354503417 0.33229472 0.31008602 0.28787732 0.26566862 0.24345992
#> [4,] 0.347036882 0.32564719 0.30425750 0.28286781 0.26147812 0.24008843
#> [5,] 0.339570347 0.31899967 0.29842898 0.27785830 0.25728762 0.23671694
#> [6,] 0.332103813 0.31235214 0.29260047 0.27284880 0.25309712 0.23334545
#> [7,] 0.324637278 0.30570461 0.28677195 0.26783929 0.24890663 0.22997396
#> [8,] 0.317170743 0.29905709 0.28094344 0.26282978 0.24471613 0.22660247
#> [9,] 0.309704208 0.29240956 0.27511492 0.25782027 0.24052563 0.22323098
#> [10,] 0.302237674 0.28576204 0.26928640 0.25281077 0.23633513 0.21985950
#> [11,] 0.294771139 0.27911451 0.26345789 0.24780126 0.23214463 0.21648801
#> [12,] 0.287304604 0.27246699 0.25762937 0.24279175 0.22795414 0.21311652
#> [13,] 0.279838070 0.26581946 0.25180085 0.23778225 0.22376364 0.20974503
#> [14,] 0.272371535 0.25917194 0.24597234 0.23277274 0.21957314 0.20637354
#> [15,] 0.264905000 0.25252441 0.24014382 0.22776323 0.21538264 0.20300205
#> [16,] 0.257438465 0.24587688 0.23431530 0.22275372 0.21119214 0.19963056
#> [17,] 0.249971931 0.23922936 0.22848679 0.21774422 0.20700164 0.19625907
#> [18,] 0.242505396 0.23258183 0.22265827 0.21273471 0.20281115 0.19288758
#> [19,] 0.235038861 0.22593431 0.21682976 0.20772520 0.19862065 0.18951610
#> [20,] 0.227572326 0.21928678 0.21100124 0.20271569 0.19443015 0.18614461
#> [21,] 0.220105792 0.21263926 0.20517272 0.19770619 0.19023965 0.18277312
#> [22,] 0.212639257 0.20599173 0.19934421 0.19269668 0.18604915 0.17940163
#> [23,] 0.205172722 0.19934421 0.19351569 0.18768717 0.18185866 0.17603014
#> [24,] 0.197706187 0.19269668 0.18768717 0.18267767 0.17766816 0.17265865
#> [25,] 0.190239653 0.18604915 0.18185866 0.17766816 0.17347766 0.16928716
#> [26,] 0.182773118 0.17940163 0.17603014 0.17265865 0.16928716 0.16591567
#> [27,] 0.175306583 0.17275410 0.17020162 0.16764914 0.16509666 0.16254418
#> [28,] 0.167840049 0.16610658 0.16437311 0.16263964 0.16090617 0.15917270
#> [29,] 0.160373514 0.15945905 0.15854459 0.15763013 0.15671567 0.15580121
#> [30,] 0.152906979 0.15281153 0.15271607 0.15262062 0.15252517 0.15242972
#> [31,] 0.145440444 0.14616400 0.14688756 0.14761112 0.14833467 0.14905823
#> [32,] 0.137973910 0.13951648 0.14105904 0.14260161 0.14414417 0.14568674
#> [33,] 0.130507375 0.13286895 0.13523053 0.13759210 0.13995368 0.14231525
#> [34,] 0.123040840 0.12622142 0.12940201 0.13258259 0.13576318 0.13894376
#> [35,] 0.115574305 0.11957390 0.12357349 0.12757309 0.13157268 0.13557227
#> [36,] 0.108107771 0.11292637 0.11774498 0.12256358 0.12738218 0.13220078
#> [37,] 0.100641236 0.10627885 0.11191646 0.11755407 0.12319168 0.12882930
#> [38,] 0.093174701 0.09963132 0.10608794 0.11254456 0.11900119 0.12545781
#> [39,] 0.085708167 0.09298380 0.10025943 0.10753506 0.11481069 0.12208632
#> [40,] 0.078241632 0.08633627 0.09443091 0.10252555 0.11062019 0.11871483
#> [41,] 0.070775097 0.07968875 0.08860239 0.09751604 0.10642969 0.11534334
#> [42,] 0.063308562 0.07304122 0.08277388 0.09250654 0.10223919 0.11197185
#> [43,] 0.055842028 0.06639369 0.07694536 0.08749703 0.09804870 0.10860036
#> [44,] 0.048375493 0.05974617 0.07111685 0.08248752 0.09385820 0.10522887
#> [45,] 0.040908958 0.05309864 0.06528833 0.07747801 0.08966770 0.10185738
#> [46,] 0.033442423 0.04645112 0.05945981 0.07246851 0.08547720 0.09848590
#> [47,] 0.025975889 0.03980359 0.05363130 0.06745900 0.08128670 0.09511441
#> [48,] 0.018509354 0.03315607 0.04780278 0.06244949 0.07709621 0.09174292
#> [49,] 0.011042819 0.02650854 0.04197426 0.05743999 0.07290571 0.08837143
#> [50,] 0.003576284 0.01986102 0.03614575 0.05243048 0.06871521 0.08499994
#> [,27] [,28] [,29] [,30] [,31] [,32] [,33]
#> [1,] 0.2263562 0.2025095 0.1786627 0.1548160 0.1309693 0.1071226 0.08327587
#> [2,] 0.2238037 0.2007760 0.1777483 0.1547206 0.1316929 0.1086652 0.08563744
#> [3,] 0.2212512 0.1990425 0.1768338 0.1546251 0.1324164 0.1102077 0.08799902
#> [4,] 0.2186987 0.1973090 0.1759194 0.1545297 0.1331400 0.1117503 0.09036059
#> [5,] 0.2161463 0.1955756 0.1750049 0.1544342 0.1338635 0.1132929 0.09272217
#> [6,] 0.2135938 0.1938421 0.1740904 0.1543388 0.1345871 0.1148354 0.09508375
#> [7,] 0.2110413 0.1921086 0.1731760 0.1542433 0.1353106 0.1163780 0.09744532
#> [8,] 0.2084888 0.1903752 0.1722615 0.1541479 0.1360342 0.1179206 0.09980690
#> [9,] 0.2059363 0.1886417 0.1713471 0.1540524 0.1367578 0.1194631 0.10216847
#> [10,] 0.2033839 0.1869082 0.1704326 0.1539570 0.1374813 0.1210057 0.10453005
#> [11,] 0.2008314 0.1851748 0.1695181 0.1538615 0.1382049 0.1225482 0.10689162
#> [12,] 0.1982789 0.1834413 0.1686037 0.1537660 0.1389284 0.1240908 0.10925320
#> [13,] 0.1957264 0.1817078 0.1676892 0.1536706 0.1396520 0.1256334 0.11161477
#> [14,] 0.1931739 0.1799743 0.1667747 0.1535751 0.1403755 0.1271759 0.11397635
#> [15,] 0.1906215 0.1782409 0.1658603 0.1534797 0.1410991 0.1287185 0.11633792
#> [16,] 0.1880690 0.1765074 0.1649458 0.1533842 0.1418227 0.1302611 0.11869950
#> [17,] 0.1855165 0.1747739 0.1640314 0.1532888 0.1425462 0.1318036 0.12106107
#> [18,] 0.1829640 0.1730405 0.1631169 0.1531933 0.1432698 0.1333462 0.12342265
#> [19,] 0.1804115 0.1713070 0.1622024 0.1530979 0.1439933 0.1348888 0.12578422
#> [20,] 0.1778591 0.1695735 0.1612880 0.1530024 0.1447169 0.1364313 0.12814580
#> [21,] 0.1753066 0.1678400 0.1603735 0.1529070 0.1454404 0.1379739 0.13050737
#> [22,] 0.1727541 0.1661066 0.1594591 0.1528115 0.1461640 0.1395165 0.13286895
#> [23,] 0.1702016 0.1643731 0.1585446 0.1527161 0.1468876 0.1410590 0.13523053
#> [24,] 0.1676491 0.1626396 0.1576301 0.1526206 0.1476111 0.1426016 0.13759210
#> [25,] 0.1650967 0.1609062 0.1567157 0.1525252 0.1483347 0.1441442 0.13995368
#> [26,] 0.1625442 0.1591727 0.1558012 0.1524297 0.1490582 0.1456867 0.14231525
#> [27,] 0.1599917 0.1574392 0.1548867 0.1523343 0.1497818 0.1472293 0.14467683
#> [28,] 0.1574392 0.1557058 0.1539723 0.1522388 0.1505053 0.1487719 0.14703840
#> [29,] 0.1548867 0.1539723 0.1530578 0.1521434 0.1512289 0.1503144 0.14939998
#> [30,] 0.1523343 0.1522388 0.1521434 0.1520479 0.1519525 0.1518570 0.15176155
#> [31,] 0.1497818 0.1505053 0.1512289 0.1519525 0.1526760 0.1533996 0.15412313
#> [32,] 0.1472293 0.1487719 0.1503144 0.1518570 0.1533996 0.1549421 0.15648470
#> [33,] 0.1446768 0.1470384 0.1494000 0.1517616 0.1541231 0.1564847 0.15884628
#> [34,] 0.1421243 0.1453049 0.1484855 0.1516661 0.1548467 0.1580273 0.16120785
#> [35,] 0.1395719 0.1435715 0.1475711 0.1515706 0.1555702 0.1595698 0.16356943
#> [36,] 0.1370194 0.1418380 0.1466566 0.1514752 0.1562938 0.1611124 0.16593100
#> [37,] 0.1344669 0.1401045 0.1457421 0.1513797 0.1570174 0.1626550 0.16829258
#> [38,] 0.1319144 0.1383710 0.1448277 0.1512843 0.1577409 0.1641975 0.17065415
#> [39,] 0.1293619 0.1366376 0.1439132 0.1511888 0.1584645 0.1657401 0.17301573
#> [40,] 0.1268095 0.1349041 0.1429987 0.1510934 0.1591880 0.1672827 0.17537731
#> [41,] 0.1242570 0.1331706 0.1420843 0.1509979 0.1599116 0.1688252 0.17773888
#> [42,] 0.1217045 0.1314372 0.1411698 0.1509025 0.1606351 0.1703678 0.18010046
#> [43,] 0.1191520 0.1297037 0.1402554 0.1508070 0.1613587 0.1719104 0.18246203
#> [44,] 0.1165995 0.1279702 0.1393409 0.1507116 0.1620823 0.1734529 0.18482361
#> [45,] 0.1140471 0.1262368 0.1384264 0.1506161 0.1628058 0.1749955 0.18718518
#> [46,] 0.1114946 0.1245033 0.1375120 0.1505207 0.1635294 0.1765381 0.18954676
#> [47,] 0.1089421 0.1227698 0.1365975 0.1504252 0.1642529 0.1780806 0.19190833
#> [48,] 0.1063896 0.1210363 0.1356831 0.1503298 0.1649765 0.1796232 0.19426991
#> [49,] 0.1038372 0.1193029 0.1347686 0.1502343 0.1657000 0.1811658 0.19663148
#> [50,] 0.1012847 0.1175694 0.1338541 0.1501389 0.1664236 0.1827083 0.19899306
#> [,34] [,35] [,36] [,37] [,38] [,39]
#> [1,] 0.05942915 0.03558243 0.01173571 -0.0121110033 -0.035957721 -0.059804440
#> [2,] 0.06260974 0.03958203 0.01655432 -0.0064733913 -0.029501100 -0.052528809
#> [3,] 0.06579032 0.04358162 0.02137292 -0.0008357794 -0.023044479 -0.045253179
#> [4,] 0.06897090 0.04758121 0.02619152 0.0048018326 -0.016587858 -0.037977549
#> [5,] 0.07215149 0.05158081 0.03101013 0.0104394446 -0.010131237 -0.030701918
#> [6,] 0.07533207 0.05558040 0.03582873 0.0160770565 -0.003674616 -0.023426288
#> [7,] 0.07851266 0.05957999 0.04064733 0.0217146685 0.002782005 -0.016150658
#> [8,] 0.08169324 0.06357959 0.04546593 0.0273522804 0.009238627 -0.008875027
#> [9,] 0.08487383 0.06757918 0.05028454 0.0329898924 0.015695248 -0.001599397
#> [10,] 0.08805441 0.07157878 0.05510314 0.0386275044 0.022151869 0.005676233
#> [11,] 0.09123500 0.07557837 0.05992174 0.0442651163 0.028608490 0.012951864
#> [12,] 0.09441558 0.07957796 0.06474035 0.0499027283 0.035065111 0.020227494
#> [13,] 0.09759616 0.08357756 0.06955895 0.0555403403 0.041521732 0.027503124
#> [14,] 0.10077675 0.08757715 0.07437755 0.0611779522 0.047978353 0.034778754
#> [15,] 0.10395733 0.09157674 0.07919615 0.0668155642 0.054434974 0.042054385
#> [16,] 0.10713792 0.09557634 0.08401476 0.0724531762 0.060891596 0.049330015
#> [17,] 0.11031850 0.09957593 0.08883336 0.0780907881 0.067348217 0.056605645
#> [18,] 0.11349909 0.10357552 0.09365196 0.0837284001 0.073804838 0.063881276
#> [19,] 0.11667967 0.10757512 0.09847057 0.0893660121 0.080261459 0.071156906
#> [20,] 0.11986026 0.11157471 0.10328917 0.0950036240 0.086718080 0.078432536
#> [21,] 0.12304084 0.11557431 0.10810777 0.1006412360 0.093174701 0.085708167
#> [22,] 0.12622142 0.11957390 0.11292637 0.1062788479 0.099631322 0.092983797
#> [23,] 0.12940201 0.12357349 0.11774498 0.1119164599 0.106087944 0.100259427
#> [24,] 0.13258259 0.12757309 0.12256358 0.1175540719 0.112544565 0.107535057
#> [25,] 0.13576318 0.13157268 0.12738218 0.1231916838 0.119001186 0.114810688
#> [26,] 0.13894376 0.13557227 0.13220078 0.1288292958 0.125457807 0.122086318
#> [27,] 0.14212435 0.13957187 0.13701939 0.1344669078 0.131914428 0.129361948
#> [28,] 0.14530493 0.14357146 0.14183799 0.1401045197 0.138371049 0.136637579
#> [29,] 0.14848552 0.14757105 0.14665659 0.1457421317 0.144827670 0.143913209
#> [30,] 0.15166610 0.15157065 0.15147520 0.1513797437 0.151284291 0.151188839
#> [31,] 0.15484668 0.15557024 0.15629380 0.1570173556 0.157740913 0.158464470
#> [32,] 0.15802727 0.15956984 0.16111240 0.1626549676 0.164197534 0.165740100
#> [33,] 0.16120785 0.16356943 0.16593100 0.1682925795 0.170654155 0.173015730
#> [34,] 0.16438844 0.16756902 0.17074961 0.1739301915 0.177110776 0.180291360
#> [35,] 0.16756902 0.17156862 0.17556821 0.1795678035 0.183567397 0.187566991
#> [36,] 0.17074961 0.17556821 0.18038681 0.1852054154 0.190024018 0.194842621
#> [37,] 0.17393019 0.17956780 0.18520542 0.1908430274 0.196480639 0.202118251
#> [38,] 0.17711078 0.18356740 0.19002402 0.1964806394 0.202937260 0.209393882
#> [39,] 0.18029136 0.18756699 0.19484262 0.2021182513 0.209393882 0.216669512
#> [40,] 0.18347194 0.19156658 0.19966122 0.2077558633 0.215850503 0.223945142
#> [41,] 0.18665253 0.19556618 0.20447983 0.2133934753 0.222307124 0.231220773
#> [42,] 0.18983311 0.19956577 0.20929843 0.2190310872 0.228763745 0.238496403
#> [43,] 0.19301370 0.20356537 0.21411703 0.2246686992 0.235220366 0.245772033
#> [44,] 0.19619428 0.20756496 0.21893564 0.2303063111 0.241676987 0.253047663
#> [45,] 0.19937487 0.21156455 0.22375424 0.2359439231 0.248133608 0.260323294
#> [46,] 0.20255545 0.21556415 0.22857284 0.2415815351 0.254590230 0.267598924
#> [47,] 0.20573604 0.21956374 0.23339144 0.2472191470 0.261046851 0.274874554
#> [48,] 0.20891662 0.22356333 0.23821005 0.2528567590 0.267503472 0.282150185
#> [49,] 0.21209721 0.22756293 0.24302865 0.2584943710 0.273960093 0.289425815
#> [50,] 0.21527779 0.23156252 0.24784725 0.2641319829 0.280416714 0.296701445
#> [,40] [,41] [,42] [,43] [,44]
#> [1,] -0.083651158 -0.1074978757 -0.131344594 -0.155191312 -0.179038030
#> [2,] -0.075556518 -0.0985842271 -0.121611936 -0.144639645 -0.167667354
#> [3,] -0.067461879 -0.0896705784 -0.111879278 -0.134087978 -0.156296678
#> [4,] -0.059367239 -0.0807569298 -0.102146620 -0.123536311 -0.144926002
#> [5,] -0.051272600 -0.0718432812 -0.092413963 -0.112984644 -0.133555325
#> [6,] -0.043177960 -0.0629296325 -0.082681305 -0.102432977 -0.122184649
#> [7,] -0.035083321 -0.0540159839 -0.072948647 -0.091881310 -0.110813973
#> [8,] -0.026988681 -0.0451023352 -0.063215989 -0.081329643 -0.099443297
#> [9,] -0.018894042 -0.0361886866 -0.053483331 -0.070777976 -0.088072621
#> [10,] -0.010799402 -0.0272750380 -0.043750674 -0.060226309 -0.076701945
#> [11,] -0.002704763 -0.0183613893 -0.034018016 -0.049674642 -0.065331269
#> [12,] 0.005389877 -0.0094477407 -0.024285358 -0.039122975 -0.053960592
#> [13,] 0.013484516 -0.0005340921 -0.014552700 -0.028571308 -0.042589916
#> [14,] 0.021579155 0.0083795566 -0.004820042 -0.018019641 -0.031219240
#> [15,] 0.029673795 0.0172932052 0.004912615 -0.007467974 -0.019848564
#> [16,] 0.037768434 0.0262068539 0.014645273 0.003083693 -0.008477888
#> [17,] 0.045863074 0.0351205025 0.024377931 0.013635360 0.002892788
#> [18,] 0.053957713 0.0440341511 0.034110589 0.024187027 0.014263464
#> [19,] 0.062052353 0.0529477998 0.043843247 0.034738694 0.025634141
#> [20,] 0.070146992 0.0618614484 0.053575905 0.045290361 0.037004817
#> [21,] 0.078241632 0.0707750971 0.063308562 0.055842028 0.048375493
#> [22,] 0.086336271 0.0796887457 0.073041220 0.066393695 0.059746169
#> [23,] 0.094430911 0.0886023943 0.082773878 0.076945362 0.071116845
#> [24,] 0.102525550 0.0975160430 0.092506536 0.087497029 0.082487521
#> [25,] 0.110620190 0.1064296916 0.102239194 0.098048695 0.093858197
#> [26,] 0.118714829 0.1153433402 0.111971851 0.108600362 0.105228874
#> [27,] 0.126809469 0.1242569889 0.121704509 0.119152029 0.116599550
#> [28,] 0.134904108 0.1331706375 0.131437167 0.129703696 0.127970226
#> [29,] 0.142998748 0.1420842862 0.141169825 0.140255363 0.139340902
#> [30,] 0.151093387 0.1509979348 0.150902483 0.150807030 0.150711578
#> [31,] 0.159188026 0.1599115834 0.160635140 0.161358697 0.162082254
#> [32,] 0.167282666 0.1688252321 0.170367798 0.171910364 0.173452930
#> [33,] 0.175377305 0.1777388807 0.180100456 0.182462031 0.184823607
#> [34,] 0.183471945 0.1866525293 0.189833114 0.193013698 0.196194283
#> [35,] 0.191566584 0.1955661780 0.199565772 0.203565365 0.207564959
#> [36,] 0.199661224 0.2044798266 0.209298429 0.214117032 0.218935635
#> [37,] 0.207755863 0.2133934753 0.219031087 0.224668699 0.230306311
#> [38,] 0.215850503 0.2223071239 0.228763745 0.235220366 0.241676987
#> [39,] 0.223945142 0.2312207725 0.238496403 0.245772033 0.253047663
#> [40,] 0.232039782 0.2401344212 0.248229061 0.256323700 0.264418340
#> [41,] 0.240134421 0.2490480698 0.257961718 0.266875367 0.275789016
#> [42,] 0.248229061 0.2579617184 0.267694376 0.277427034 0.287159692
#> [43,] 0.256323700 0.2668753671 0.277427034 0.287978701 0.298530368
#> [44,] 0.264418340 0.2757890157 0.287159692 0.298530368 0.309901044
#> [45,] 0.272512979 0.2847026644 0.296892350 0.309082035 0.321271720
#> [46,] 0.280607619 0.2936163130 0.306625007 0.319633702 0.332642396
#> [47,] 0.288702258 0.3025299616 0.316357665 0.330185369 0.344013073
#> [48,] 0.296796897 0.3114436103 0.326090323 0.340737036 0.355383749
#> [49,] 0.304891537 0.3203572589 0.335822981 0.351288703 0.366754425
#> [50,] 0.312986176 0.3292709076 0.345555639 0.361840370 0.378125101
#> [,45] [,46] [,47] [,48] [,49]
#> [1,] -0.202884748 -0.226731466 -0.250578184 -0.274424902 -0.298271621
#> [2,] -0.190695063 -0.213722772 -0.236750481 -0.259778190 -0.282805899
#> [3,] -0.178505377 -0.200714077 -0.222922777 -0.245131477 -0.267340177
#> [4,] -0.166315692 -0.187705383 -0.209095073 -0.230484764 -0.251874455
#> [5,] -0.154126007 -0.174696688 -0.195267370 -0.215838051 -0.236408733
#> [6,] -0.141936322 -0.161687994 -0.181439666 -0.201191338 -0.220943011
#> [7,] -0.129746636 -0.148679299 -0.167611962 -0.186544626 -0.205477289
#> [8,] -0.117556951 -0.135670605 -0.153784259 -0.171897913 -0.190011567
#> [9,] -0.105367266 -0.122661910 -0.139956555 -0.157251200 -0.174545845
#> [10,] -0.093177580 -0.109653216 -0.126128851 -0.142604487 -0.159080123
#> [11,] -0.080987895 -0.096644521 -0.112301148 -0.127957774 -0.143614401
#> [12,] -0.068798210 -0.083635827 -0.098473444 -0.113311061 -0.128148679
#> [13,] -0.056608524 -0.070627132 -0.084645741 -0.098664349 -0.112682957
#> [14,] -0.044418839 -0.057618438 -0.070818037 -0.084017636 -0.097217235
#> [15,] -0.032229154 -0.044609743 -0.056990333 -0.069370923 -0.081751513
#> [16,] -0.020039468 -0.031601049 -0.043162630 -0.054724210 -0.066285791
#> [17,] -0.007849783 -0.018592355 -0.029334926 -0.040077497 -0.050820069
#> [18,] 0.004339902 -0.005583660 -0.015507222 -0.025430785 -0.035354347
#> [19,] 0.016529588 0.007425034 -0.001679519 -0.010784072 -0.019888625
#> [20,] 0.028719273 0.020433729 0.012148185 0.003862641 -0.004422903
#> [21,] 0.040908958 0.033442423 0.025975889 0.018509354 0.011042819
#> [22,] 0.053098643 0.046451118 0.039803592 0.033156067 0.026508541
#> [23,] 0.065288329 0.059459812 0.053631296 0.047802780 0.041974263
#> [24,] 0.077478014 0.072468507 0.067459000 0.062449492 0.057439985
#> [25,] 0.089667699 0.085477201 0.081286703 0.077096205 0.072905707
#> [26,] 0.101857385 0.098485896 0.095114407 0.091742918 0.088371429
#> [27,] 0.114047070 0.111494590 0.108942111 0.106389631 0.103837151
#> [28,] 0.126236755 0.124503285 0.122769814 0.121036344 0.119302873
#> [29,] 0.138426441 0.137511979 0.136597518 0.135683056 0.134768595
#> [30,] 0.150616126 0.150520674 0.150425221 0.150329769 0.150234317
#> [31,] 0.162805811 0.163529368 0.164252925 0.164976482 0.165700039
#> [32,] 0.174995497 0.176538063 0.178080629 0.179623195 0.181165761
#> [33,] 0.187185182 0.189546757 0.191908332 0.194269908 0.196631483
#> [34,] 0.199374867 0.202555452 0.205736036 0.208916621 0.212097205
#> [35,] 0.211564552 0.215564146 0.219563740 0.223563333 0.227562927
#> [36,] 0.223754238 0.228572841 0.233391443 0.238210046 0.243028649
#> [37,] 0.235943923 0.241581535 0.247219147 0.252856759 0.258494371
#> [38,] 0.248133608 0.254590230 0.261046851 0.267503472 0.273960093
#> [39,] 0.260323294 0.267598924 0.274874554 0.282150185 0.289425815
#> [40,] 0.272512979 0.280607619 0.288702258 0.296796897 0.304891537
#> [41,] 0.284702664 0.293616313 0.302529962 0.311443610 0.320357259
#> [42,] 0.296892350 0.306625007 0.316357665 0.326090323 0.335822981
#> [43,] 0.309082035 0.319633702 0.330185369 0.340737036 0.351288703
#> [44,] 0.321271720 0.332642396 0.344013073 0.355383749 0.366754425
#> [45,] 0.333461406 0.345651091 0.357840776 0.370030462 0.382220147
#> [46,] 0.345651091 0.358659785 0.371668480 0.384677174 0.397685869
#> [47,] 0.357840776 0.371668480 0.385496184 0.399323887 0.413151591
#> [48,] 0.370030462 0.384677174 0.399323887 0.413970600 0.428617313
#> [49,] 0.382220147 0.397685869 0.413151591 0.428617313 0.444083035
#> [50,] 0.394409832 0.410694563 0.426979294 0.443264026 0.459548757
#> [,50]
#> [1,] -0.322118339
#> [2,] -0.305833607
#> [3,] -0.289548876
#> [4,] -0.273264145
#> [5,] -0.256979414
#> [6,] -0.240694683
#> [7,] -0.224409952
#> [8,] -0.208125221
#> [9,] -0.191840489
#> [10,] -0.175555758
#> [11,] -0.159271027
#> [12,] -0.142986296
#> [13,] -0.126701565
#> [14,] -0.110416834
#> [15,] -0.094132102
#> [16,] -0.077847371
#> [17,] -0.061562640
#> [18,] -0.045277909
#> [19,] -0.028993178
#> [20,] -0.012708447
#> [21,] 0.003576284
#> [22,] 0.019861016
#> [23,] 0.036145747
#> [24,] 0.052430478
#> [25,] 0.068715209
#> [26,] 0.084999940
#> [27,] 0.101284671
#> [28,] 0.117569403
#> [29,] 0.133854134
#> [30,] 0.150138865
#> [31,] 0.166423596
#> [32,] 0.182708327
#> [33,] 0.198993058
#> [34,] 0.215277789
#> [35,] 0.231562521
#> [36,] 0.247847252
#> [37,] 0.264131983
#> [38,] 0.280416714
#> [39,] 0.296701445
#> [40,] 0.312986176
#> [41,] 0.329270908
#> [42,] 0.345555639
#> [43,] 0.361840370
#> [44,] 0.378125101
#> [45,] 0.394409832
#> [46,] 0.410694563
#> [47,] 0.426979294
#> [48,] 0.443264026
#> [49,] 0.459548757
#> [50,] 0.475833488
#>
#>
#> $Avg.estimate
#> $Avg.estimate$`30-40`
#> ATE sd z-value p-value lower CI(95%)
#> Average Treatment Effect -0.048 0.309 -0.154 0.877 -0.664
#> upper CI(95%)
#> Average Treatment Effect 0.569
#>
#> $Avg.estimate$`40-67`
#> ATE sd z-value p-value lower CI(95%)
#> Average Treatment Effect -1.003 0.390 -2.572 0.010 -1.781
#> upper CI(95%)
#> Average Treatment Effect -0.226
#>
#>
#> $Xlabel
#> [1] "Belonging"
#>
#> $Dlabel
#> [1] "Generation"
#>
#> $Ylabel
#> [1] "Tax Moral"
#>
#> $de
#>
#> Call:
#> density.default(x = data[, X])
#>
#> Data: data[, X] (88 obs.); Bandwidth 'bw' = 0.5486
#>
#> x y
#> Min. :-0.6459 Min. :0.0007379
#> 1st Qu.: 1.4271 1st Qu.:0.0508774
#> Median : 3.5000 Median :0.1329117
#> Mean : 3.5000 Mean :0.1203226
#> 3rd Qu.: 5.5729 3rd Qu.:0.1744728
#> Max. : 7.6459 Max. :0.2478022
#>
#> $de.tr
#> $de.tr$`20-30`
#>
#> Call:
#> density.default(x = data[data[, D] == char, X])
#>
#> Data: data[data[, D] == char, X] (20 obs.); Bandwidth 'bw' = 0.4611
#>
#> x y
#> Min. :-0.3834 Min. :0.0009631
#> 1st Qu.: 1.5583 1st Qu.:0.0693888
#> Median : 3.5000 Median :0.1016576
#> Mean : 3.5000 Mean :0.1284589
#> 3rd Qu.: 5.4417 3rd Qu.:0.1384904
#> Max. : 7.3834 Max. :0.4448267
#>
#> $de.tr$`30-40`
#>
#> Call:
#> density.default(x = data[data[, D] == char, X])
#>
#> Data: data[data[, D] == char, X] (45 obs.); Bandwidth 'bw' = 0.7054
#>
#> x y
#> Min. :-1.116 Min. :0.0005647
#> 1st Qu.: 1.192 1st Qu.:0.0347552
#> Median : 3.500 Median :0.1310716
#> Mean : 3.500 Mean :0.1080534
#> 3rd Qu.: 5.808 3rd Qu.:0.1771081
#> Max. : 8.116 Max. :0.1908699
#>
#> $de.tr$`40-67`
#>
#> Call:
#> density.default(x = data[data[, D] == char, X])
#>
#> Data: data[data[, D] == char, X] (23 obs.); Bandwidth 'bw' = 0.6798
#>
#> x y
#> Min. :-1.04 Min. :0.0002878
#> 1st Qu.: 1.23 1st Qu.:0.0194554
#> Median : 3.50 Median :0.1028165
#> Mean : 3.50 Mean :0.1099031
#> 3rd Qu.: 5.77 3rd Qu.:0.1903006
#> Max. : 8.04 Max. :0.2541633
#>
#>
#> $hist.out
#> $breaks
#> [1] 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70
#> [16] 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45
#> [31] 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00 3.05 3.10 3.15 3.20
#> [46] 3.25 3.30 3.35 3.40 3.45 3.50 3.55 3.60 3.65 3.70 3.75 3.80 3.85 3.90 3.95
#> [61] 4.00 4.05 4.10 4.15 4.20 4.25 4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70
#> [76] 4.75 4.80 4.85 4.90 4.95 5.00 5.05 5.10 5.15 5.20 5.25 5.30 5.35 5.40 5.45
#> [91] 5.50 5.55 5.60 5.65 5.70 5.75 5.80 5.85 5.90 5.95 6.00
#>
#> $counts
#> [1] 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0
#> [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0
#> [51] 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [76] 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17
#>
#> $density
#> [1] 1.818182 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [9] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [17] 0.000000 0.000000 0.000000 2.727273 0.000000 0.000000 0.000000 0.000000
#> [25] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [33] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.727273
#> [41] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [49] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [57] 0.000000 0.000000 0.000000 5.681818 0.000000 0.000000 0.000000 0.000000
#> [65] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [73] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.181818
#> [81] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [89] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> [97] 0.000000 0.000000 0.000000 3.863636
#>
#> $mids
#> [1] 1.025 1.075 1.125 1.175 1.225 1.275 1.325 1.375 1.425 1.475 1.525 1.575
#> [13] 1.625 1.675 1.725 1.775 1.825 1.875 1.925 1.975 2.025 2.075 2.125 2.175
#> [25] 2.225 2.275 2.325 2.375 2.425 2.475 2.525 2.575 2.625 2.675 2.725 2.775
#> [37] 2.825 2.875 2.925 2.975 3.025 3.075 3.125 3.175 3.225 3.275 3.325 3.375
#> [49] 3.425 3.475 3.525 3.575 3.625 3.675 3.725 3.775 3.825 3.875 3.925 3.975
#> [61] 4.025 4.075 4.125 4.175 4.225 4.275 4.325 4.375 4.425 4.475 4.525 4.575
#> [73] 4.625 4.675 4.725 4.775 4.825 4.875 4.925 4.975 5.025 5.075 5.125 5.175
#> [85] 5.225 5.275 5.325 5.375 5.425 5.475 5.525 5.575 5.625 5.675 5.725 5.775
#> [97] 5.825 5.875 5.925 5.975
#>
#> $xname
#> [1] "data[, X]"
#>
#> $equidist
#> [1] TRUE
#>
#> attr(,"class")
#> [1] "histogram"
#>
#> $count.tr
#> $count.tr$`20-30`
#> [1] 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
#> [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
#> [51] 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [76] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
#>
#> $count.tr$`30-40`
#> [1] 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0
#> [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0
#> [51] 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [76] 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12
#>
#> $count.tr$`40-67`
#> [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [38] 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [75] 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
#>
#>
#> $tests
#> $tests$treat.type
#> [1] "discrete"
#>
#> $tests$X.Lkurtosis
#> [1] "0.044"
#>
#>
#> $estimator
#> [1] "linear"
#>
#> $model.linear
#>
#> Call: glm(formula = formula, data = data, weights = WEIGHTS)
#>
#> Coefficients:
#> (Intercept) belonging D.Group.2 DX.Group.2
#> 0.65588 0.54469 -0.23110 0.04636
#> D.Group.3 DX.Group.3 modernity egality
#> 1.18174 -0.54620 -0.01999 0.15379
#> shame income_current Dummy.Covariate.1 Dummy.Covariate.2
#> 0.13078 0.04156 0.57169 0.26068
#> Dummy.Covariate.3 Dummy.Covariate.4 Dummy.Covariate.5 Dummy.Covariate.6
#> 0.53110 -0.02034 0.30066 0.25942
#>
#> Degrees of Freedom: 87 Total (i.e. Null); 72 Residual
#> Null Deviance: 213.5
#> Residual Deviance: 103 AIC: 297.6
#>
#> $use.fe
#> [1] 0
#>
#> $figure
#>
#> attr(,"class")
#> [1] "interflex"6.3.2 Generation (7 levels)
df <- data[, c("Tax Moral" = "tax_moral_N", cov_gen7)]
interflex(estimator = "raw",
Y = "tax_moral_N", D = "gen7", X = "belonging", Z = cov_a[1:8], data = df,
base="20-25", treat.type="discrete",
weights = NULL, Ylabel = "Tax Moral",
Dlabel = "Generation", Xlabel="Belonging",
main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE,
file = "out/interflex_raw_gen7.png")
#> Baseline group: treat = 20-25
End of Analysis
---
title: "Tax Moral in the Haredi Community - V2"
author: "Racheli Brecher"
date: "`r Sys.Date()`"
output: 
  html_document:
    code_download: true
    code_folding: hide
    toc: true # table of content true
    toc_depth: 3  # upto three depths of headings (specified by #, ## and ###)
    number_sections: true  ## if you want number sections at each table header
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  fig.width = 10,
  fig.height =7,
  echo = TRUE, 
  warning = FALSE,
  message = FALSE,
  collapse = TRUE,
  comment = "#>"
) 
```

```{r}
library(MASS)
library(AER)
library(broom)
library(knitr)
library(stargazer)
library(sjPlot)
library(ggplot2)
library(ggpubr)
library(gt)
library(olsrr)
library(arm)
library(coefplot)
library(effects)
library(vtable)
library(flextable)
library(DT)
library(tidyverse)
library(reshape2)
library(corrplot)
library(interflex)

source("wl_graph.R")
source("wl_utils.R")
source("wl_reg_report.R")
```

This document summarizes the quantitative analysis for Brecher research 'Tax Moral in the Haredi Community'. The research is uses data collected from a quastionaire distributed in Haredi population - see: https://docs.google.com/forms/d/1KdVid-ExT86wjs-nuCrakBeccwNgaDCSu3nhPjIHYqY/edit?ts=648b12b5 . 

The analysis was developed using R version 4.1.3 (2022-03-10) and RStudio 2022.07.2+576 release for Windows.

To view the code - click on the 'Code' buttons on the right side of the page. 

# Research data

Note:
All ordered categorical variables with 5 levels or more are treated as as numeric due to the 'small number of samples' limitation.

```{r}
  raw_data=read.csv("tax_data_150223.csv",sep=";")
    
  names(raw_data)[1] <- "id" #fix name of id column - move between pc and mac
  
   # Dependent Variables
  raw_data$tax_moral_a <- factor(raw_data$tax_morale_a, ordered = TRUE)
  raw_data$tax_moral_b <- factor(raw_data$tax_morale_b, ordered = TRUE)
  raw_data$tax_moral_N <- raw_data$tax_morale
  raw_data$tax_moral <- factor(raw_data$tax_morale, ordered = TRUE)
  
  # Nominal Covariates
  raw_data$tax_returns <- factor(raw_data$tax_returns)
  levels(raw_data$tax_returns) <- c("No", "Low", "Yes", "High")
  raw_data$charity <- factor(raw_data$charity)
  levels(raw_data$charity) <- c("Below 10%", "Around 10%", "Above 10%")
  raw_data$tax_social <- factor(raw_data$tax_social)
  levels(raw_data$tax_social) <- c("No", "Low", "Yes")
  raw_data$gender <- factor(raw_data$isMale)
  levels(raw_data$gender) <- c("Female", "Male")
  
  # Create Generation factor field (3 levels and 5 levels)

  raw_data$gen3_N <- as.numeric(cut(raw_data$age, breaks = c(20, 30, 40, 67)))
  raw_data$gen3 <- factor(cut(raw_data$age, breaks = c(20, 30, 40, 67)),
                    labels = c("20-30", "30-40", "40-67"))
  raw_data$gen7 <- factor(cut(raw_data$age, breaks = c(20, 25, 30, 35, 40, 45, 50, 67)),
                    labels = c("20-25", "25-30", "30-35", "35-40", "40-45", "45-50", "50-67"))
    

  
  dependents <- c("Tax Moral" = "tax_moral",                    #Ordinal (1-6)
                  "Tax Moral (Numeric)" = "tax_moral_N",        #Numeric (1-6)
                  "Tax Moral A (1:4:1)" = "tax_moral_a",        #Ordinal (1-3)
                  "Tax Moral B (2:2:2)" = "tax_moral_b")        #Ordinal (1-3)
  
  cov_full <- c("Strictness" = "strictness",                    #Numeric (1-7)
                "Modernity" = "modernity",                      #Numeric (1-7)
                "Belonging" = "belonging",                      #Numeric (1-6)
                "Egality" = "egality",                          #Numeric (1-6)
                "Shame" = "shame",                              #Numeric (1-6)
                "Tax Returns" = "tax_returns",                  #Nominal (1-4)
                "Social Safety" = "tax_social",                 #*Nominal (1-4)
                "Income Level" = "income_current",              #Numeric (1-5)
                "Gender" = "gender",                            #Binary  (0/1)
                "Age" = "age",                                  #Numeric (0-120)
                "Generation(3)" = "gen3",                       #Ordinal (1-3)
                "Generation(3)N" = "gen3_N",                    #Numeric (1-3)
                "Generation(7)" = "gen7"                        #Ordinal (1-7)
  )
  
  cov_full_1 <- wl_setdiff(cov_full, c("gen3", "gen3_N", "gen7"))
  
  # remove factors from VIF calculation
  cov_vif <- wl_setdiff(cov_full_1, c("tax_returns", "charity", "tax_social"))
  
  # Handle colinearity between Strictness and Modernity
  cov_a <- wl_setdiff(cov_full_1, c("strictness"))
  cov_b <- wl_setdiff(cov_full_1, c("modernity"))
  
  data <- raw_data[, c(dependents, cov_full)]  
  data <- data[complete.cases(data), ]
```
  
## Descriptive tables
  
```{r}
  d <- wl_descriptive(data, vars=c(dependents, cov_full_1),
                      labs=c(names(dependents), names(cov_full_1)), display=FALSE)
  save_as_html(d, path = "out/descriptive.html")
  d
```

## Correlation Matrix

```{r}
  ndata <- data[, c(dependents, cov_full_1)] 
  ndata <- ndata[, sapply(ndata, is.numeric)] #get only the numeric data
  cdata <- cor(ndata)
  rownames(cdata) <- colnames(cdata) <- c(names(dependents[2]), 
                                          names(cov_full[1:5]),
                                          names(cov_full[8]), 
                                          names(cov_full[10]))
  png("out/corrplot.png", width = 1600, height = 1600, res = 300)
  corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", tl.cex = 0.8, diag=FALSE)
  dev.off()
    
  corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE)
  
  # Create a grayscale color palette
  grayscale_palette <- 
    colorRampPalette(c("#FFFFFF", "#FAFAFA", "#F5F5F5", "#EEEEEE", "#E0E0E0", 
                       "#D9D9D9", "#CFCFCF", "#BFBFBF", "#9E9E9E", "#757575"))(10)
  
  png("out/corrplot_bw.png", width = 1600, height = 1600, res = 300)
  corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE, 
                     col = grayscale_palette, tl.col = "black")
  dev.off()
  
  corrplot::corrplot(cdata, type="upper", addCoef.col = 'black', tl.pos="lt", diag=FALSE, 
                     col = grayscale_palette, tl.col = "black")
```

## Descriptive Plots

### Tax Moral & Strictness

```{r, fig.height=3}
wl_plot_xy (data, cov_full[1], dependents[2])
ggsave("out/tax_moral_and_strictness.png", height=3)
```

### Tax Moral & Modernity

``` {r, fig.height=3}
wl_plot_xy (data, cov_full[2], dependents[2])
ggsave("out/tax_moral_and_modernity.png", height=3)
```

### Tax Moral & Belonging

``` {r, fig.height=3}      
wl_plot_xy (data, cov_full[3], dependents[2])
ggsave("out/tax_moral_and_belongings.png", height=3)
```

### Tax Moral & Egality
     
``` {r, fig.height=3}
wl_plot_xy (data, cov_full[4], dependents[2])
ggsave("out/tax_moral_and_egality.png", height=3)
```

### Tax Moral & Shame
     
``` {r, fig.height=3}
wl_plot_xy (data, cov_full[5], dependents[2])
ggsave("out/tax_moral_and_shame.png", height=3)
```

## VIF (Multi-Collinearity) Test

The below VIF test table indicates multi-colinearity between strictness and modernity. 
  
```{r}  
  mvif <- ols_regression(data, "tax_moral_N", cov_vif)
  vif_table (mvif, labs=names(cov_vif))
```

The **cor** function calculates the correlation coefficient between two variables. The correlation coefficient measures the strength and direction of the linear relationship between two variables. A value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. As can be seen from the bewlow results we have ~70% correlation in the same direction between the two variables. 

```{r}
  #Strictness and Modernity correlation
  cor(data$strictness, data$modernity)
```

# Regression models

The dependent variable, Tax Moral, is an ordered categorical variable with 6 levels. To analyze the effects of the independent variables and control for additional covariates we use Proportional odds logistic regression (POLR). We also use as a reference Ordinary Least Squares (OLS) regression. 
In both types of regressions we calculate two models: one with the independent variable Strictness and the other with Modernity (we can not use both due to multi-collinearity).  

```{r}
  # Main Regression Models
  m1 <- polr_regression(data, "tax_moral", cov_a)
  m2 <- polr_regression(data, "tax_moral", cov_b)
  
  m3 <- ols_regression(data, "tax_moral_N", cov_a)
  m4 <- ols_regression(data, "tax_moral_N", cov_b)
  
  main_models <- list(m1,m2,m3,m4)
  
  # Regression Table
  cov_labels_ab <- c(names(cov_a[1]),
                  names(cov_b[1]),
                  names(cov_a[2]),
                  names(cov_a[3]), 
                  names(cov_a[4]),
                  paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[2],"]"),
                  paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[3],"]"),
                  paste0(names(cov_a[5])," [", levels(data[,cov_a[5]])[4],"]"),
                  paste0(names(cov_a[6])," [", levels(data[,cov_a[7]])[2],"]"),
                  paste0(names(cov_a[6])," [", levels(data[,cov_a[7]])[3],"]"),
                  names(cov_a[7]),
                  paste0(names(cov_a[8])," [", levels(data[,cov_a[9]])[2],"]"),
                  names(cov_a[9])
                  )
  wl_stargazer (main_models, "tax_moral_reg", cov_labels = cov_labels_ab, coef_exp = TRUE) 
```
```{r echo = FALSE}
  wl_stargazer (main_models, "tax_moral_reg", cov_labels = cov_labels_ab, coef_exp = TRUE) 
```

## Interpretations

### Model 1 Interpretation

```{r echo=FALSE}
  wl_reg_interpret(m1, df=data, y_name=dependents[1], x_list=cov_a, reg_type="POLR")
```

### Model 2 Interpretation

```{r echo=FALSE}
  wl_reg_interpret(m2, df=data, y_name=dependents[1], x_list=cov_b, reg_type="POLR")
```

### Model 3 Interpretation

```{r echo=FALSE}
  wl_reg_interpret(m3, df=data, y_name=dependents[2], x_list=cov_a, reg_type="OLS")
```

### Model 4 INterpretation

```{r echo=FALSE}
  wl_reg_interpret(m4, df=data, y_name=dependents[2], x_list=cov_b, reg_type="OLS")
```

## Coefficiant plots

The standardized coefficient plots enable to compare the magnitude of the effects. Belonging has the highest positive statistically significant magnitude and age has the highest negative statistically significant magnitude. 

Note: The intercepts at the top of the tables represent the probability boundaries between Tax Moral levels. 

### Model 1 - POLR, Strictness

```{r}
  cov_labels_a <- cov_labels_ab[c(1,3:length(cov_labels_ab))]
  cov_labels_b <- cov_labels_ab[2:length(cov_labels_ab)]
  cov_labels_i = c("Intercept 5|6","Intercept 4|5","Intercept 3|4","Intercept 2|3","Intercept 1|2")
  
  png("out/model1.png", width = 1200, height = 800)
  arm::coefplot(m1, main="",
                varnames = c(cov_labels_a, rev(cov_labels_i)), 
                mar = c(5, 12, 4, 2) + 0.1,
                cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
  dev.off()
  arm::coefplot(m1, main="",
                varnames = c(cov_labels_a, rev(cov_labels_i)), 
                mar = c(5, 12, 4, 2) + 0.1,
                cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
```

### Model 2 - POLR, Modernity

```{r}
  png("out/model2.png", width = 1200, height = 800)
  arm::coefplot(m2, main="",
                varnames = c(cov_labels_a, rev(cov_labels_i)), 
                mar = c(5, 12, 4, 2) + 0.1,
                cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
  dev.off()
  arm::coefplot(m2, main="",
                varnames = c(cov_labels_a, rev(cov_labels_i)), 
                mar = c(5, 12, 4, 2) + 0.1,
                cex.pts = 1.2, pch.pts = 15, cex.var = 1.)
```

# Effect Analysis

Make sure to include the data in the model so Effect will work

```{r}
m1$call$data <- data
```

## Belonging effect on Tax Moral probabilities

```{r}
  png("out/age_effect.png", width = 1200, height = 1200)
  plot(Effect(focal.predictors = c("belonging"), m1), rug = FALSE, cex=3.0)
  dev.off()
  plot(Effect(focal.predictors = c("belonging"), m1), rug = FALSE, cex=3.0)
```

## Age effect on Tax Moral probabilities

```{r}
  png("out/age_effect.png", width = 1200, height = 1200)
  plot(Effect(focal.predictors = c("age"), m1), rug = FALSE, cex=3.0)
  dev.off()
  plot(Effect(focal.predictors = c("age"), m1), rug = FALSE, cex=3.0)
```

## Egality effect on Tax Moral probabilities

```{r}
  png("out/egality_effect.png", width = 1200, height = 1200)
  plot(Effect(focal.predictors = c("egality"), m1), rug = FALSE, cex=3.0)
  dev.off()
  plot(Effect(focal.predictors = c("egality"), m1), rug = FALSE, cex=3.0)
```

## Gender effect on Tax Moral probabilities

```{r}
  png("out/gender_effect.png", width = 1200, height = 1200)
  plot(Effect(focal.predictors = c("gender"), m1), rug = FALSE, cex=3.0)
  dev.off()
  plot(Effect(focal.predictors = c("gender"), m1), rug = FALSE, cex=3.0)
```

## Income effect om on Tax Moral probabilities

```{r}  
  png("out/income_effect.png", width = 1200, height = 1200)
  plot(Effect(focal.predictors = c("income_current"), m1), rug = FALSE, cex=3.0)
  dev.off()
  plot(Effect(focal.predictors = c("income_current"), m1), rug = FALSE, cex=3.0)
```

# Interactions

As belonging seems to be the highest magnitude effect, we test it's interactions with Age, Gender and Income.

```{r}
  cov_int_a <- c(cov_a, "belonging*age")
  mi_age <- polr_regression(data, "tax_moral", cov_int_a)
  cov_int_a <- c(cov_a, "belonging*egality")
  mi_egality<- polr_regression(data, "tax_moral", cov_int_a)
  cov_int_a <- c(cov_a, "egality*age")
  mi_age_egality<- polr_regression(data, "tax_moral", cov_int_a)
  cov_int_a <- c(cov_a, "belonging*gender")
  mi_gender <- polr_regression(data, "tax_moral", cov_int_a)
  cov_int_a <- c(cov_a, "belonging*income_current")
  mi_income <- polr_regression(data, "tax_moral", cov_int_a)
  
  int_models <- list(mi_age, mi_egality, mi_age_egality)
  names(int_models) <- c("Belonging*Age", "Belonging*Egality", "Egality*Age")
  
  cov_labels_ia <- c(cov_labels_a, names(int_models))
  
  wl_stargazer (int_models, "interaction_reg", cov_labels=cov_labels_ia, coef_exp=TRUE)
```
```{r echo = FALSE}
  wl_stargazer (int_models, "interaction_reg", cov_labels=cov_labels_ia, coef_exp=TRUE)
```

## Interaction Plots

### Age & Belonging Interaction Plot

```{r} 
  sjPlot::plot_model(mi_age, type = "int")
  ggsave("out/age_interaction.png")
```

### egality & Belonging Interaction Plot

```{r} 
  sjPlot::plot_model(mi_egality, type = "int")
  ggsave("out/egality_interaction.png")
```

### egality & age Interaction Plot

```{r} 
  sjPlot::plot_model(mi_age_egality, type = "int")
  ggsave("out/egality_interaction.png")
```

### Gender & Belonging Interaction Plot

```{r} 
  sjPlot::plot_model(mi_gender, type = "int")
  ggsave("out/gender_interaction.png")
```

### Income & Belonging Interaction Plot

```{r} 
  sjPlot::plot_model(mi_income, type = "int")
  ggsave("out/income_interaction.png")
```

# Robustness

We use two different coding schemes to test the robustness of our results. The first scheme (1:4:1) emphasize the edges of Tax Moral scale, re-coding level 1 as 1, levels 2-5 as 2, and level 6 as 3. The second scheme (2:2:2) reduces the granularity of Tax Moral levels, re-coding levels 1-2 as 1, levels 3-4 as 2, and levels 5-6 as 3. 

```{r}
 # Robustness Models with different tax_moral coding
  m1_ra <- polr_regression(data, "tax_moral_a", cov_a)
  m2_ra <- polr_regression(data, "tax_moral_a", cov_b)
  
  m3_rb <- polr_regression(data, "tax_moral_b", cov_a)
  m4_rb <- polr_regression(data, "tax_moral_b", cov_b)
  
  coding_models <- list(m1_ra, m2_ra, m3_rb, m4_rb)
  names(coding_models) <- c("1:4:1 Coding", "1:4:1 Coding", 
                                "2:2:2 Coding", "2:2:2 Coding")
  wl_stargazer (coding_models, "coding", cov_labels = cov_labels_ab, coef_exp = TRUE)
```
```{r echo = FALSE}  
  wl_stargazer (coding_models, "coding", cov_labels = cov_labels_ab, coef_exp = TRUE) 
```

## Interpretation  

### '1:4:1 Coding' - model 1

```{r echo=FALSE}
 cat(wl_reg_interpret(m1_ra, df=data, y_name=dependents[3], x_list=cov_a, reg_type="POLR"))
```


### '1:4:1 Coding' - model 2

```{r echo=FALSE}
 cat(wl_reg_interpret(m2_ra, df=data, y_name=dependents[3], x_list=cov_b, reg_type="POLR"))
```

### '2:2:2 Coding' - model 3

```{r echo=FALSE}
 cat(wl_reg_interpret(m3_rb, df=data, y_name=dependents[4], x_list=cov_a, reg_type="POLR"))
```


### '2:2:2 Coding' - model 4

```{r echo=FALSE}
 cat(wl_reg_interpret(m4_rb, df=data, y_name=dependents[4], x_list=cov_b, reg_type="POLR"))
```

# Belonging and Generation Analysis

## Descriptive

### Generation (3 Levels)

```{r, fig.height=6}
# Calculate mean and standard error
grouped_data <- data %>%
  group_by(gen3) %>%
  summarise(
    mean_tax_moral = mean(tax_moral_N),
    count = n(),
    se = sd(tax_moral_N) / sqrt(n())  # Standard error
  )

# Sort the grouped data by gen3
grouped_data <- grouped_data %>%
  arrange(gen3)

# Create the bar chart with error bars and display the SE value
ggplot(grouped_data, aes(x = gen3, y = mean_tax_moral)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  geom_errorbar(aes(ymin = mean_tax_moral - se, ymax = mean_tax_moral + se), 
                width = 0.2) +  # Add error bars
  labs(title = "Average Tax Moral by Gen3",
       x = "Gen3",
       y = "Mean Tax Moral") +
  geom_text(aes(label = paste0("n=", count, ", SE=", round(se, 2))),
            vjust = -0.5,
            hjust = 0.5)

ggsave("out/gen3_barchart.png")
```

### Generation (7 levels)

```{r, fig.height=6}
grouped_data <- data %>%
  group_by(gen7) %>%
  summarise(mean_tax_moral = mean(tax_moral_N), count = n())

# Sort the grouped data by gen7
grouped_data <- grouped_data %>%
  arrange(gen7)

ggplot(grouped_data, aes(x = gen7, y = mean_tax_moral)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Tax Moral by Gen7",
       x = "Gen7",
       y = "Mean Tax Moral") +
  geom_text(aes(label = paste0("n=", count)),
            vjust = -0.5,
            hjust = 0.5)
```

## Interaction (Regression)

### Interaction (Regression)

```{r}
cov_gen3 <- c(cov_a[1:8],"Generation(3)" = "gen3")
cov_gen3_N <- c(cov_a[1:8],"Generation(3)N" = "gen3_N")
cov_gen7 <- c(cov_a[1:8],"Generation(7)" = "gen7") 

cov_int_gen3 <- c(cov_a, "belonging*gen3")
mi_gen3 <- ols_regression(data, "tax_moral_N", cov_int_gen3)

mi_gen3_F <- ols_regression(data, "tax_moral_N", cov_gen3)
mi_gen3_N <- ols_regression(data, "tax_moral_N", cov_gen3_N)


int_models <- list(mi_gen3_F, mi_gen3_N, mi_gen3)
names(int_models) <- c("Gen3 (Factor)", "Gen3 (Numeric)", "Belonging*Gen3")

cov_labels <- c(names(cov_full[2]),
                names(cov_full[3]),
                names(cov_full[4]), 
                names(cov_full[5]),
                paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[2],"]"),
                paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[3],"]"),
                paste0(names(cov_full[6])," [", levels(data[,cov_full[6]])[4],"]"),
                paste0(names(cov_full[7])," [", levels(data[,cov_full[7]])[2],"]"),
                paste0(names(cov_full[7])," [", levels(data[,cov_full[7]])[3],"]"),
                names(cov_full[8]),
                paste0(names(cov_full[9])," [", levels(data[,cov_full[9]])[2],"]"),
                names(cov_full[10]),
                paste0(names(cov_full[11])," [", levels(data[,cov_full[11]])[2],"]"),
                paste0(names(cov_full[11])," [", levels(data[,cov_full[11]])[3],"]"),
                names(cov_full[12]),
                paste0(names(int_models[3])," [", levels(data[,cov_full[11]])[2],"]"),
                paste0(names(int_models[3])," [", levels(data[,cov_full[11]])[3],"]")
                )
 wl_stargazer (int_models, "interaction_reg_gen", cov_labels=cov_labels, coef_exp=FALSE)
```
``` {r echo = FALSE}
wl_stargazer (int_models, "interaction_reg_gen", cov_labels=cov_labels, coef_exp=FALSE)
```

### Generation (3 levels)

```{r, fig.height=6}
# Colored
sjPlot::plot_model(mi_gen3, type = "int") 
ggsave("out/gen3_interaction_ols.png")

# Black & White
sjPlot::plot_model(mi_gen3, type = "int", colors="bw")
ggsave("out/gen3_interaction_ols_bw.png")
```

## Robustness (Interflex)

### Generation (3 Levels)

```{r fig.height=6, message=FALSE, warning=FALSE}
df <- data[, c("Tax Moral" = "tax_moral_N", cov_gen3)]

interflex(estimator = "raw",
          Y = "tax_moral_N", D = "gen3", X = "belonging", Z = cov_a[1:8], data = df,
          base="20-30", treat.type="discrete",
          weights = NULL, Ylabel = "Tax Moral",
          Dlabel = "Generation", Xlabel="Belonging",
          main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE, 
          file = "out/interflex_raw_gen3.png")

interflex(estimator = "linear",
          Y = "tax_moral_N", D = "gen3", X = "belonging", Z = cov_a[1:8], data = df,
          base="20-30", treat.type="discrete",
          weights = NULL, Ylabel = "Tax Moral",
          Dlabel = "Generation", Xlabel="Belonging",
          main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE, 
          file = "out/interflex_linear_gen3.png")
```

### Generation (7 levels)

```{r, fig.height=6}
df <- data[, c("Tax Moral" = "tax_moral_N", cov_gen7)]

interflex(estimator = "raw",
          Y = "tax_moral_N", D = "gen7", X = "belonging", Z = cov_a[1:8], data = df,
          base="20-25", treat.type="discrete",
          weights = NULL, Ylabel = "Tax Moral",
          Dlabel = "Generation", Xlabel="Belonging",
          main = "Generation * Belonging Plot", cex.main = 1.2, pool=TRUE, 
          file = "out/interflex_raw_gen7.png")
```

End of Analysis


