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October 4, 2015 10:57
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Imputing missing data with R; MICE package: Full article at http://datascienceplus.com/imputing-missing-data-with-r-mice-package/
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# Using airquality dataset | |
data <- airquality | |
data[4:10,3] <- rep(NA,7) | |
data[1:5,4] <- NA | |
# Removing categorical variables | |
data <- airquality[-c(5,6)] | |
summary(data) | |
#------------------------------------------------------------------------------- | |
# Look for missing > 5% variables | |
pMiss <- function(x){sum(is.na(x))/length(x)*100} | |
# Check each column | |
apply(data,2,pMiss) | |
# Check each row | |
apply(data,1,pMiss) | |
#------------------------------------------------------------------------------- | |
# Missing data pattern | |
library(mice) | |
# Missing data pattern | |
md.pattern(data) | |
library(VIM) | |
# Plot of missing data pattern | |
aggr_plot <- aggr(data, col=c('navyblue','red'), numbers=TRUE, sortVars=TRUE, labels=names(data), cex.axis=.7, gap=3, ylab=c("Histogram of missing data","Pattern")) | |
# Box plot | |
marginplot(data[c(1,2)]) | |
#------------------------------------------------------------------------------- | |
# Impute missing data using mice | |
tempData <- mice(data,m=5,maxit=50,meth='pmm',seed=500) | |
summary(tempData) | |
# Get imputed data (for the Ozone variable) | |
tempData$imp$Ozone | |
# Possible imputation models provided by mice() are | |
methods(mice) | |
# What imputation method did we use? | |
tempData$meth | |
# Get completed datasets (observed and imputed) | |
completedData <- complete(tempData,1) | |
summary(completedData) | |
#------------------------------------------------------------------------------- | |
# Plots | |
# Scatterplot Ozone vs all | |
xyplot(tempData,Ozone ~ Wind+Temp+Solar.R,pch=18,cex=1) | |
# Density plot original vs imputed dataset | |
densityplot(tempData) | |
# Another take on the density: stripplot() | |
stripplot(tempData, pch = 20, cex = 1.2) | |
#------------------------------------------------------------------------------- | |
# Pooling the results and fitting a linear model | |
modelFit1 <- with(tempData,lm(Temp~ Ozone+Solar.R+Wind)) | |
pool(modelFit1) | |
summary(pool(modelFit1)) | |
# Using more imputed datasets | |
tempData2 <- mice(data,m=50,seed=245435) | |
modelFit2 <- with(tempData2,lm(Temp~ Ozone+Solar.R+Wind)) | |
summary(pool(modelFit2)) |
Hello author, may I ask how to extract the most appropriate data set from these 5 interpolation sets as the data for subsequent overall analysis
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Thanks, it was so easy to understand