mlpsa: This function will perform phase II of the multilevel...

In jbryer/multilevelPSA: Multilevel Propensity Score Analysis

Description

TODO: Need more details

Usage

mlpsa(response, treatment = NULL, strata = NULL, level2 = NULL,
  minN = 5, reverse = FALSE, ci.level = 0.05)

Arguments

response	vector containing the response values
treatment	vector containing the treatment conditions
strata	vector containing the strata for each response
level2	vector containing the level 2 specifications
minN	the minimum number of subjects per strata for that strata to be included in the analysis.
reverse	reverse the order of treatment and control for the difference calculation.
ci.level	the confidence level to use for confidence intervals. Defaults to a 95% confidence level.

Details

The ci.adjust provides a Bonferroni-Sidak adjusted confidence intervals based on the number of levels/clusters.

Value

a mlpsa class

See Also

mlpsa.ctree mlpsa.logistic

Examples

## Not run: 
require(multilevelPSA)
require(party)
data(pisana)
data(pisa.colnames)
data(pisa.psa.cols)
mlctree = mlpsa.ctree(pisana[,c('CNT','PUBPRIV',pisa.psa.cols)], formula=PUBPRIV ~ ., level2='CNT')
student.party = getStrata(mlctree, pisana, level2='CNT')
student.party$mathscore = apply(student.party[,paste0('PV', 1:5, 'MATH')], 1, sum) / 5
results.psa.math = mlpsa(response=student.party$mathscore, 
       treatment=student.party$PUBPRIV, 
       strata=student.party$strata, 
       level2=student.party$CNT, minN=5)
results.psa.math
summary(results.psa.math)

## End(Not run)

jbryer/multilevelPSA documentation built on April 10, 2020, 1:20 a.m.