fixed bug in posterior predictive simulation function

.Rhistory

@@ -204,3 +204,80 @@ pred<- get_predictive_Y(mcmc, # GMC mcmc object
 nobs = dim(X)[1], # number of observations in predictive data set; we use n = dim(X)[1]
 nsets = dim(X)[1] + 1, # number of predictive data sets to create
 seed = 10)
+#' Title
+#'
+#' @param Y
+#' @param factor.cols
+#'
+#' @return
+#'
+#' @examples
+format_data<-function(Y,factor.cols){
+cat<- NULL
+cat_col = 1
+cat_col_names = NULL
+bin_col_names = NULL
+for(i in factor.cols){
+if(nlevels(Y[,i]) >2){
+one.hot.cat<- model.matrix(~. -1,data =model.frame(~.,data = Y[,i], na.action = na.pass))
+cat<- cbind(cat,one.hot.cat)
+cat_col_names = c(cat_col_names, rep(names(Y)[i], nlevels(Y[,i])))
+}
+else{
+one.hot.bin<- model.matrix(~. , data = model.frame(~.,data = Y[,i], na.action = na.pass))[,2]
+cat<-cbind(cat,one.hot.bin)
+bin_col_names = c(bin_col_names, rep(names(Y)[i],1))
+}
+}
+one.hot.cols<- ncol(cat) # number of one hot encoded categories
+Y_mod<- cbind(cat,Y[,-factor.cols])
+col_mem = c(cat_col_names,bin_col_names,colnames(Y_mod)[(one.hot.cols+1):ncol(Y_mod)])# data frame used for sampler
+return(list( cat_col_names = cat_col_names,
+bin_col_names = bin_col_names, col_mem = col_mem))
+}
+library(GMCImpute)
+GMCImpute::format_data
+library(GMCImpute)
+?GMC.mcmc
+library(GMCImpute)
+?GMC.mcmc
+library(GMCImpute)
+knitr::opts_chunk$set(echo = TRUE)
+library(ggplot2)
+library(gridExtra)
+library(purrr)
+library(devtools)
+remotes::install_github('jfeldman396/GMCImpute', force = T)
+library(GMCImpute)
+set.seed(47)
+num= 500
+X1<-rnorm(num)
+X2<- rpois(num,5*abs(X1))
+X3<- as.factor(rbernoulli(num,pnorm(-.5+scale(X2))))
+X<- data.frame(X1,X2,X3)
+R = t(sapply(1:num, function(x)rbernoulli(2, p = pnorm(-.5 + .5*abs(X1[x]))))) # missingness mechanism
+X_noMis = X
+X[which(R[,1] == T),2] = NA
+X[which(R[,2] == T),3] = NA
+hyperparams = list(delta = 10,
+k.star = 2,
+plugin.threshold = 100,
+a_alpha = 1,
+b_alpha = 3,
+nu_mix = 4,
+kappa_0 = .001,
+nu = 3,
+a1 = 2,
+a2 = 3,
+a.sigma = 1,
+b.sigma = .3,
+D_0 = diag(1,2))
+mcmc<-GMC.mcmc(Data = X, nImp = 5,hyperparams = hyperparams, burn = 1500,nsamp = 2000, seed = 47)
+library(GMCImpute)
+knitr::opts_chunk$set(echo = TRUE)
+library(ggplot2)
+library(gridExtra)
+library(purrr)
+mcmc<-GMC.mcmc(Data = X, nImp = 5,hyperparams = hyperparams, burn = 1500,nsamp = 2000, seed = 47)
+?GMC.mcmc
+setwd("~/Desktop/Research/MI Paper")

R/Helpers.R

@@ -42,6 +42,7 @@ format_data<-function(Y,factor.cols){
 
   Y_mod<- cbind(cat,Y[,-factor.cols])
   col_mem = c(cat_col_names,bin_col_names,colnames(Y_mod)[(one.hot.cols+1):ncol(Y_mod)])# data frame used for sampler
+  #Y_mod_comp = Y_mod %>% tidyr::drop_na() #complete observations
   return(list( cat_col_names = cat_col_names,
                bin_col_names = bin_col_names, col_mem = col_mem))
 }

R/Software.R

@@ -744,9 +744,11 @@ get_predictive_Y<-function(mcmc,
   if(length(unique(z))>1){
     cs<- sample(unique_z, n, replace = T, prob =  pi_h[unique_z])
     etas = NULL
-    etas = do.call(rbind, t(sapply(unique(cs),function(x) return(rbind(etas,
+    #need to fix this
+    etas = do.call(rbind, data.frame(t(sapply(unique(cs),function(x) return(rbind(etas,
                                                                        mvrnorm(sum(cs == x),mu[[x]],
-                                                                               Delta[[x]]))))))
+                                                                               Delta[[x]])))))))
+
   }else{
     etas = mvrnorm(n,mu[[unique_z]], Delta[[unique_z]])
   }