In trangnguyen74/mediationClarity: Simple and more robust estimation of marginal natural (in)direct effects

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This is code to make the version of the results figure that is included in the final article. This version differs from the one in the package vignette only in that it does not include the estimators from the old (ie preprint v3) section on leveraging covariates to improve precision. During the review of this paper we decided to cut that section from the final manuscript.

library(mediationClarity)
library(ggplot2)

# grab the results with the confidence intervals based on boot.num = 999
estimates <- readRDS(here::here("vignettes", "results", "estimates.rds"))

est.names <- c("wtd", 
               "Y2pred", "Y2predR",
               "psYpred", "psYpredMR",
               "Ypred", "YpredMR",
               "MsimYpred", "MsimYpredMR",
               "NDEpred", "NDEpredR")

estimates <- lapply(est.names, function(z) {
    tmp <- estimates[[z]]$estimates
    tmp <- tmp[c("NDE0", "NIE1"), c("estimate", "2.5%", "97.5%")]
    colnames(tmp) <- c("estimate", "lb", "ub")
    est <- as.data.frame(tmp, row.names = FALSE)
    est$effect <- rownames(tmp)
    est$estimator <- z
    est
})

estimates <- do.call(rbind, estimates)

estimates$estimator <- factor(estimates$estimator, levels = est.names)

estimates$properties <- 
    ifelse(estimates$estimator %in% c("wtd", 
                                      "Y2pred", "psYpred", "Ypred", "MsimYpred", 
                                      "NDEpred"),
           "non-robust",
           ifelse(estimates$estimator %in% c("psYpredMR", "YpredMR", "MsimYpredMR"),
                  "more robust",
                  "robust"))
estimates$properties = factor(estimates$properties, 
                              levels = c("non-robust", "more robust", "robust"))

pdf(here::here("vignettes", "results", "fig9-article.pdf"), width = 7, height = 4)
ggplot(data = estimates, 
       aes(x = estimator, 
           # flip the sign of y, as we will present effects as reductions
           y = -estimate, ymin = -ub, ymax = -lb, 
           color = properties, shape = properties)) +
    geom_hline(yintercept = 0, color = "gray") +
    geom_linerange() +
    geom_point(aes(size = properties)) +
    scale_color_manual(values = c("gray50", "blue", "purple")) +
    scale_shape_manual(values = c(17, 18, 19)) +
    scale_size_manual(values = c(3, 4.5, 3.7)) +
    scale_x_discrete(limits = rev, position = "top") +
    coord_flip() +
    labs(x = "", y = "estimate and 95% confidence interval") +
    facet_wrap(~ effect) +
    theme_bw() +
    theme(legend.position = "left")
dev.off()

trangnguyen74/mediationClarity documentation built on May 4, 2023, 6:22 a.m.