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