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# Help functions
marginal_effect <- function(out,
newdata=NULL,
main_var, family = "logit",
treat_range, difference = FALSE,
seed=1234){
fit <- out$fit
# Coef and VCOV
coef_mar <- coef(out$fit)[is.na(coef(out$fit)) == FALSE]
vcov_mar <- out$vcov
# Sample Mean of Outcomes
y_orig <- model.frame(formula(fit), data = newdata)[ ,1]
sample_mean_outcome <- mean(y_orig, na.rm = TRUE)
# Prepare model.frame and treat_range
newdata_use_b <- model.frame(formula(fit), data = newdata)
if(missing(treat_range)){
treat_range <- quantile(newdata_use_b[, main_var],
c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
na.rm = TRUE)
}
# Create new_treat and new_control automatically
newdata_use_l <- list()
for(i in 1:length(treat_range)){
newdata_use_l_b <- newdata_use_b
newdata_use_l_b[ , main_var] <- treat_range[i]
newdata_use_l_exp <- model.matrix(formula(fit), data = newdata_use_l_b)
newdata_use_l_exp <- newdata_use_l_exp[, names(coef_mar)]
newdata_use_l[[i]] <- newdata_use_l_exp
}
set.seed(seed)
sim_coef <- mvrnorm(n = 1000, mu=coef_mar, Sigma=vcov_mar)
# Linear Part
linear_out <- lapply(newdata_use_l,
FUN = function(x) as.matrix(sim_coef) %*% as.matrix(t(x)))
if(family %in% c("nb", "poisson")){
out <- lapply(linear_out, FUN = function(x) apply(exp(x), 1, mean))
}else if(family %in% c("ols")){
out <- lapply(linear_out, FUN = function(x) apply(x, 1, mean))
}else if(family %in% c("logit")){
out <- lapply(linear_out, FUN = function(x) apply((exp(x)/(1 + exp(x))), 1, mean))
}else{
warning("family should be one of 'nb', 'poisson', 'logit', and 'ols'")
}
if(difference == FALSE){
out_main <- lapply(out, FUN = function(x) c(quantile(x, c(0.025)), mean(x), quantile(x, c(0.975))))
names(out_main) <- treat_range
out_main_percent <- lapply(out,
FUN = function(x)
c(quantile(x, c(0.025)), mean(x), quantile(x, c(0.975)))/sample_mean_outcome)
names(out_main_percent) <- treat_range
}else if(difference == TRUE){
out_b <- out[[2]] - out[[1]]
out_main <- c(quantile(out_b, c(0.025)), mean(out_b), quantile(out_b, c(0.975)),
quantile(out_b, c(0.05)), quantile(out_b, c(0.95)))
out_main_percent <- out_main/sample_mean_outcome
}
output <- list("out" = out,
"out_main" = out_main,
"out_main_percent" = out_main_percent,
"sample_mean" = sample_mean_outcome,
"treat_range" = treat_range)
}
bin.summary <- function(formula, print_var, id, data, digits = 3, type = "logit"){
var <- all.vars(formula)
data_use <- model.frame( ~ ., data = data[, c(var, id)])
if(type == "logit") fit <- glm(formula, data = data_use, family = "binomial")
if(type == "probit") fit <- glm(formula, data = data_use, family = binomial(link="probit"))
tab_p <- coeftest(fit, vcov = vcovCL(fit, cluster = data_use[, id]))
if(missing(print_var)) print_var <- seq(1:min(20, nrow(tab_p)))
mat <- tab_p[print_var, ]
sig <- rep("", length(mat[,4]))
sig[mat[ , 4] < 0.001] <- "***"
sig[mat[ , 4] >= 0.001 & mat[ , 4] < 0.01] <- "**"
sig[mat[ , 4] >= 0.01 & mat[ , 4] < 0.05] <- "*"
sig[mat[ , 4] >= 0.05 & mat[ , 4] < 0.1] <- "."
mat <- as.data.frame(mat)
mat <- round(mat, digits = digits)
mat$Sig <- sig
sample_size <- length(fit$residuals)
cat("Coefficients:\n")
print(mat[, c(1, 2, 4, 5)], row.names=TRUE)
cat(paste("(Sample Size:", sample_size, ")\n", sep = ""))
output <- list("fit" = fit, "vcov" = vcovCL(fit, cluster = data_use[, id]),
"sample" = sample_size)
return(output)
}
lm.summary <- function(formula, print_var, id, data, digits = 3){
var <- all.vars(formula)
data_use <- model.frame( ~ ., data = data[, c(var, id)])
fit <- lm(formula, data = data_use)
tab_p <- coeftest(fit, vcov = vcovCL(fit, cluster = data_use[, id]))
if(missing(print_var)) print_var <- seq(1:min(20, nrow(tab_p)))
mat <- tab_p[print_var, ]
sig <- rep("", length(mat[,4]))
sig[mat[ , 4] < 0.001] <- "***"
sig[mat[ , 4] >= 0.001 & mat[ , 4] < 0.01] <- "**"
sig[mat[ , 4] >= 0.01 & mat[ , 4] < 0.05] <- "*"
sig[mat[ , 4] >= 0.05 & mat[ , 4] < 0.1] <- "."
mat <- as.data.frame(mat)
mat <- round(mat, digits = digits)
mat$Sig <- sig
sample_size <- length(fit$residuals)
cat("Coefficients:\n")
print(mat[, c(1, 2, 4, 5)], row.names=TRUE)
cat(paste("(Sample Size:", sample_size, ")\n", sep = ""))
output <- list("fit" = fit, "vcov" = vcovCL(fit, cluster = data_use[, id]),
"sample" = sample_size)
return(output)
}
glm.boot <- function(formula, data, family, cluster_id, boot = 1000, seed = 1234){
set.seed(seed)
data$cluster_id <- cluster_id
data_u <- data[is.na(data$cluster_id) == FALSE, ]
coef_boot <- c()
for(b in 1:boot){
boot_id <- sample(unique(data_u$cluster_id), size = length(unique(data_u$cluster_id)), replace=TRUE)
# create bootstap sample with sapply
boot_which <- sapply(boot_id, function(x) which(data_u$cluster_id == x))
data_boot <- data_u[unlist(boot_which),]
if(family == "poisson"){
glm_boot <- glm(formula, family = "poisson", data = data_boot)
coef_boot <- cbind(coef_boot, summary(glm_boot)$coef[,1])
}else if(family == "negative-binomial"){
glm_nb_boot <- glm.nb(formula, data = data_boot)
coef_boot <- cbind(coef_boot, summary(glm_nb_boot)$coef[,1])
}
if((b%%100) == 0) cat(paste(b, "..."))
}
if(family == "poisson"){
glm_boot_final <- glm(formula, family = "poisson", data = data)
}else if(family == "negative-binomial"){
glm_boot_final <- glm.nb(formula, data = data)
}
se <- apply(coef_boot, 1, sd) # bootstrap SE
coef <- glm_boot_final$coefficients
output <- list("fit" = glm_boot_final, "coef" = coef, "se" = se)
return(output)
}
star_out <- function(out, name){
writeLines(capture.output(out), name)
}
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