R: Functions to compute the balance statistics

balance {arm}

R Documentation

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, ..., digits = 2)

## 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.
`x`	an object return by the balance function.
`digits`	minimal number of significant digits, default is 2.
`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.

Note

The function does not work with predictors that contain factor(x), log(x) or all other data transformation. Create new objects for these variables. Attach them into the original dataset before doing the matching procedure.

Author(s)

Jennifer Hill jh1030@columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn

References

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

Examples

# 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)

[Package arm version 1.7-07 Index]