R/coef_comparison.R

In cc458/postregplots: Making plots after regression models

#' A function to plot first difference for the same variable across Logit model with cluster sd .
#' For multipel models
#' This function creates a ridge density plot for variables in Logit model using simulations
#' @param ModelResults list. Specify model name of the logit model.
#' @param n.sim numeric. Specify the number of simulations
#' @param varname character vector. A vector contains the name of variables
#' @param data data.frame. The name of the data frame in logit model
#' @param val1 numeric. Specify the min value of percentile
#' @param val2 numeric. Specify the max value of percentile
#' @param clusterid character. The cluster id
#' @import ggplot2 arm multiwayvcov lmtest gridExtra viridis ggridges dplyr
#' ggplot2 tidyr stringr purrr
#' @export

coef_comparison = function(ModelResults,n.sim = 1000, varname, data,
                           val1, val2, clusterid){
       require(arm)
       library(multiwayvcov)
       library(lmtest)
       library(gridExtra)
       library(viridis)
       library(ggridges)
       library(dplyr)
       library(ggplot2)
       library(tidyr)
       library(stringr)
       library(purrr)

       cluster <- data[,clusterid]

       vcov_cluster = lapply(ModelResults, function(x) FUN = cluster.vcov(x, cluster))
       modSumm = mapply(function(x, y) coeftest(x,y), x = ModelResults, y = vcov_cluster)
       noModels=length(modSumm)
       model_names = paste('Model',1:noModels)

       set.seed(12345)
       sim = Map(function(x, y) mvrnorm(n= n.sim, coef(x), y), ModelResults, vcov_cluster)

       fd <- list()

       for (i in 1:length(ModelResults)){
              X1 <- model.matrix(ModelResults[[i]])
              X2 <- model.matrix(ModelResults[[i]])
              X = model.matrix(ModelResults[[i]])
              value = as.numeric(quantile(X[, varname], c(val1, val2)))
              X1[, varname] = value[1]
              X2[, varname] = value[2]
              fd[[model_names[i]]] =  apply(apply(X2, 1, function (x) plogis(sim[[i]] %*% x)) -
                                               apply(X1, 1, function (x) plogis(sim[[i]] %*% x)), 1, mean)
       }

       df_plot <- map(fd, data.frame) %>%
              map2_df(., names(.), ~mutate(.x, id = .y))

       names(df_plot)[1] <- "value"


       p =    ggplot(df_plot, aes(x = value, y = id, height=..density.., fill = id)) +
              geom_density_ridges(col = "grey70", scale = .8, show.legend = F) +
              scale_fill_viridis(discrete = TRUE) +
              geom_vline(xintercept = 0, colour = gray(1/2), lty = 2) +
              theme_ridges(font_size = 13, grid = F, center_axis_labels = T) +
              theme(axis.title.y = element_blank())
       return(p)

}