| |
|
|
| marginal_effect <- function(out, |
| newdata=NULL, |
| main_var, family = "logit", |
| treat_range, difference = FALSE, |
| seed=1234){ |
| |
| fit <- out$fit |
| |
| coef_mar <- coef(out$fit)[is.na(coef(out$fit)) == FALSE] |
| vcov_mar <- out$vcov |
| |
| |
| y_orig <- model.frame(formula(fit), data = newdata)[ ,1] |
| sample_mean_outcome <- mean(y_orig, na.rm = TRUE) |
| |
| |
| 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) |
| } |
| |
| |
| 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_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) |
| |
| 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)$coef[,1]) |
| } |
| if((b%%100) == 0) cat(paste(b, "...")) |
| } |
| if(family == "poisson"){ |
| glm_boot_final <- glm(formula, family = "poisson", data = data) |
| }else if(family == "negative-binomial"){ |
| glm_boot_final <- glm.nb(formula, data = data) |
| } |
| se <- apply(coef_boot, 1, sd) |
| coef <- glm_boot_final$coefficients |
| |
| output <- list("fit" = glm_boot_final, "coef" = coef, "se" = se) |
| |
| return(output) |
| } |
|
|
| star_out <- function(out, name){ |
| writeLines(capture.output(out), name) |
| } |
|
|
