bal.cs.psa {PSAgraphics} | R Documentation |
Balance for Categorical Covariate: Random Strata as part of a PSA
Description
Function provides a measure of the balance achieved between control and treatment groups for a categorical covariate from user defined strata. This statistic is compared to the same measure for randomly permuted strata.
Usage
bal.cs.psa(
categorical,
treatment = NULL,
strata = NULL,
B = 1000,
eps = 0.02,
main = NULL,
...
)
Arguments
categorical |
Categorical covariate that is being balanced within
strata in a PSA. If |
treatment |
Binary variable of same length as |
strata |
Integer variable; a vector of same length as
|
B |
Numeric; number of randomly generated iterations of the balance measure are created for the comparison distribution. |
eps |
Numeric; ensures that weighting is reasonable for small categories. |
main |
Title passed to |
... |
Other graphical parameters passed to |
Details
This function measures the balance achieved across K strata for a
categorical covariate with J categories. If p_{ijk}
is the proportion
of cases in stratum k, category j, and treatment i, then the statistic is
the sum over all K, J of |\sqrt{p_{0jk} + \epsilon} - \sqrt{p_{1jk} +
\epsilon } |
. A permutation distribution is generated by randomly
assigning cases to strata, thus generating B permuted stratifications and
the associated B permutation statistics. The permutation stratifications
are generated under a fixed marginals model to retain comparability with the
original stratification. A histogram of the permutation statistics is
produced with the original statistic referenced as a red dot.
Value
In addition to the histogram, a list with the following components is returned:
balance.orig |
Balance measure of user defined strata. |
rank.orig |
Rank of original balance measure in comparison with the B randomly generated values. |
Author(s)
James E. Helmreich James.Helmreich@Marist.edu
Robert M. Pruzek RMPruzek@yahoo.com
See Also
bal.cws.psa
, bal.ms.psa
, bal.ks.psa
Examples
#Everything random
categorical<-sample(4,1000,replace=TRUE)
treatment<-sample(c(0,1),1000,replace=TRUE)
strata<-sample(5,1000,replace=TRUE)
bal.cs.psa(categorical,treatment,strata)
#Perfect balance on 80%, random on last 20%
categorical<-rep(sample(5,1000,replace=TRUE),2)
treatment<-c(rep(0,1000),rep(1,1000))
strat<-sample(6,1200,replace=TRUE)
strat<-c(strat[1:1000],strat[1:800],strat[1001:1200])
bal.cs.psa(categorical,treatment,strat,B=200)