balance.CBMB: Optimal Covariate Balance for Multiple Simulatenously Administered Binary Treatments

Description

Returns the mean and standardized mean associated with each treatment group, before and after weighting.

Usage

## S3 method for class 'CBMB':
balance(object, stabilized, ...)

Arguments

object

A CBMB object.

stabilized

Set to TRUE to use stabilized weights, and FALSE to use unstabilized weights. Default is TRUE.

...

Additional arguments to be passed to balance.

Value

Returns a list of two matrices, "original" (before weighting) and "balanced" (after weighting).

Details

Each of the matrices' rows are the covariates and whose columns are the weighted mean, and standardized mean associated with each treatment group. The standardized mean is the weighted mean divided by the standard deviation of the covariate for the whole population.

Examples

Run this code

###
### Example: Optimal Covariate Balance
###
n<-200
k<-4
set.seed(1040)
X1<-cbind(1,matrix(rnorm(n*k),ncol=k))

betas.1<-betas.2<-betas.3<-c(2,4,4,-4,3)/5


probs.1<-probs.2<-probs.3<-(1+exp(-X1 %*% betas.1))^-1

treat.1<-rbinom(n=length(probs.1),size=1,probs.1)
treat.2<-rbinom(n=length(probs.2),size=1,probs.2)
treat.3<-rbinom(n=length(probs.3),size=1,probs.3)

formulas.multi<-list(c(treat.1~X1, treat.2~X1, treat.3~X1))

multibin1<-CBPS(formulas.multi,method="over",type="MultiBin")
balance(multibin1)