StratifiedBalancing (version 0.1.0)

stratacont:

Stratacont()

Description

This is the primary stratification function for continuous outcome variables.It locates naturally occurring strata in the data, weights them and returns an improved regression coefficient for the treatment variable which is adjusted for both confounding and interaction terms. Returns a matrix containig naturally occurring strata.

Usage

stratacont(Treatment,Outcome,Matrix)

Arguments

Treatment

Column number of variable to be used as treatment.

Outcome

Column number of variable to be used as outcome.

Matrix

Name of matrix or data.frame where data is stored.

Details

This is the primary stratification function for continuous outcome variables.It locates naturally occurring strata in the data, weights them and returns an improved regression coefficient for the treatment variable which is adjusted for both confounding and interaction terms.

Examples

## We will first begin by simulating data in 11 covariates and a continuous outcome
## with significant interaction terms and correlations amongst covariates (to simulate a
## non-randomized experiment with a strongly non-linear underlying model).
## First, we will create a matrix with the input variables. The inout variables will all be
## categorical variables.
m=matrix(nrow=1000,ncol=12)
for ( i in 1:10){
m[,i]=round(runif(1000,min=1,max=3))
}
m[,11]=rbinom(1000,1,0.5)

## Next, we will simulate the output variable and include interaction terms
for(i in 1:nrow(m)){
a=(2*m[i,11] + 0.5*m[i,1] - 4*m[i,2] + 2.3*m[i,3] +
0.8*m[i,4] -0.7*m[i,5] - 4*m[i,6] + 3.6*m[i,7] +
1.2*m[i,8] - 11*m[i,9] - 2.1*m[i,10] + 2.3*m[i,3]*m[i,4]
-3.5*m[i,5]*m[i,6]*m[i,7] + 8*m[i,1]*m[i,2]*m[i,9]
+ 2.1*m[i,2]*m[i,6]*m[i,8] + 5*m[i,4]*m[i,7]*m[i,9]
+ 8*m[i,3]*m[i,10]*m[i,6] + 11*m[i,7]*m[i,8]*m[i,5] + 8*m[i,3]*m[i,9]*m[i,2])
m[i,12]=rnorm(1,a,1)
}
## We are interested in determining the coefficient of covariate 11 which is 2.
## Tmost straightforward
## way of doing this is to use simple linear regression as follows
m=as.data.frame(m)
k=lm(m[,12]~.,data=m[,(1:11)])
## The value of the coefficient of variable 11 found by the regression can be retrieved using
k$coeff[12]

## We can now use the stratacont() function to find a more accurate estimation of the coefficient
g=stratacont(11,12,m)