balance: Functions to compute the balance statistics

Description

This function computes the balance statistics before and after matching.

Usage

balance(rawdata, matched, pscore.fit, factor=TRUE)

## S3 method for class 'balance':
print(x, \dots, digits = getOption("digits"))

## S3 method for class 'balance':
plot(x, longcovnames = NULL, 
    main = "Standardized Difference in Means",
    v.axis=TRUE, cex.main = 1, 
    cex.vars = 0.8, cex.pts = 0.8, 
    mar=c(0,3,5.1,2), plot=TRUE, ...)

Arguments

rawdata

data before using matching function, see the example below.

matched

matched data using matching function, see the example below.

pscore.fit

glm.fit object to get propensity scores.

factor

default is TRUE which will display the factorized categorical variables. In a situation where no equal levels of factorized categorical variables is observed, use factor=FALSE to proceed.

an object return by the balance function.

digits

minimal number of significant digits

longcovnames

long covariate names. If not provided, plot will use covariate variable name by default

main

The main title (on top) using font and size (character expansion) par("font.main") and color par("col.main"); default title is Standardized Difference in Means.

v.axis

default is TRUE, which shows the top axis--axis(3).

cex.main

font size of main title

cex.vars

font size of variabel names

cex.pts

point size of the estimates

mar

A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(0,3,5.1,2).

plot

default is TRUE, which will plot the plot.

...

other plot options may be passed to this function

Details

This function plots the balance statistics before and after matching. The open circle dots represent the unmatched balance statistics. The solid dots represent the matched balance statistics. The closer the value of the estimates to the zero, the better the treated and control groups are balanced after matching.

References

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006. (Chater 10)

Examples

Run this code

# matching first
old.par <- par(no.readonly = TRUE)
data(lalonde)
attach(lalonde)
fit <- glm(treat ~ re74 + re75 + age + factor(educ) + 
            black + hisp + married + nodegr + u74 + u75, 
            family=binomial(link="logit"))
pscores <- predict(fit, type="link")
matches <- matching(z=lalonde$treat, score=pscores)
matched <- lalonde[matches$matched,]

# balance check
b.stats <- balance(lalonde, matched, fit)
print(b.stats)
plot(b.stats)
par(old.par)