Simplified Output for Matchit package

Have been attending a seminar on propensity score matching this week, when I was asked if I could write a function to reduce the complexity of the matchit package output. I produced two functions.

First I wrote a function to produce a histogram of the matched mean differences which gives a nice summary of how well the matching procedure has achieved balance (note you need to install the package ggplot2 to use this function):

meandiffplot <- function (x) {
	mdiff<-data.frame(x$sum.matched)
	names(mdiff)[4]<-c("m_mean_diff")
	diffplot<-ggplot(mdiff, aes(m_mean_diff) )
	diffplot<- diffplot+ geom_histogram (fill="grey")
	diffplot<- diffplot+ geom_density (colour="red")
	diffplot 
	}

Next I produced a function the reports only those matched mean differences with a standardized difference of over .1 (sorted by absolute size).

meandifftable<- function (x){
	post<-data.frame(x$sum.matched[4])
	matchID <- as.vector (row.names (post) )
	names(post)[1]<-c("m_mean_diff")
	post$absolute<- abs(post[1])
	total2<-post[order (-post$absolute, na.last=NA) ,]
	meandiffover1<- subset(total2[1], total2[1]> .1 | total2[1]< -.1)
	meandiffover1
}

If you use the matchit package I think these functions are really useful.

 

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About Philip Parker

I am a post doc in developmental and educational psychology at a Germany university. I did my PhD at the university of Sydney in stress and well-being. Most days I am hunched over a computer yelling at statistical software or responding to journal editors who seem to always want twice the amount of content but with half the words. For fun I like to read up on the latest developments in R and programming various functions.
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