# Multilevel Propensity Score Analysis

## Documentation for package ‘multilevelPSA’ version 1.3.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.
- Package NEWS

## Help Pages

multilevelPSA-package | Multilevel Propensity Score Analysis
align.plots | Adapted from ggExtra package which is no longer available. This is related to an experimental mlpsa plot that will combine the circular plot along with the two individual distributions.
as.data.frame.covariate.balance | Returns the overall effects as a data frame.
covariate.balance | Estimate covariate effect sizes before and after propensity score adjustment.
covariateBalance | Calculate covariate effect size differences before and after stratification.
cv.trans.psa | Transformation of Factors to Individual Levels
difftable.plot | This function produces a ggplot2 figure containing the mean differences for each level two, or cluster.
getPropensityScores | Returns a data frame with two columns corresponding to the level 2 variable and the fitted value from the logistic regression.
getStrata | Returns a data frame with two columns corresponding to the level 2 variable and the leaves from the conditional inference trees.
is.mlpsa | Returns true if the object is of type 'mlpsa'
loess.plot | Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.
lsos | Nicer list of objects in memory. Particularly useful for analysis of large data. https://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session
missing.plot | Returns a heat map graphic representing missingness of variables grouped by the given grouping vector.
mlpsa | This function will perform phase II of the multilevel propensity score analysis.
mlpsa.circ.plot | Plots the results of a multilevel propensity score model.
mlpsa.ctree | Estimates propensity scores using the recursive partitioning in a conditional inference framework.
mlpsa.difference.plot | Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.
mlpsa.distribution.plot | Plots distribution for either the treatment or comparison group.
mlpsa.logistic | Estimates propensity scores using logistic regression.
multilevelPSA | Multilevel Propensity Score Analysis
pisa.colnames | Mapping of variables in 'pisana' with full descriptions.
pisa.countries | Data frame mapping PISA countries to their three letter abbreviation.
pisa.psa.cols | Character vector representing the list of covariates used for estimating propensity scores.
pisana | North American (i.e. Canada, Mexico, and United States) student results of the 2009 Programme of International Student Assessment.
plot.covariate.balance | Multiple covariate balance assessment plot.
plot.mlpsa | Plots the results of a multilevel propensity score model.
plot.psrange | Plots densities and ranges for the propensity scores.
print.covariate.balance | Prints the overall effects before and after propensity score adjustment.
print.mlpsa | Prints basic information about a 'mlpsa' class.
print.psrange | Prints information about a psrange result.
print.xmlpsa | Prints the results of [mlpsa()] and [xtable.mlpsa()].
psrange | Estimates models with increasing number of comparison subjects starting from 1:1 to using all available comparison group subjects.
summary.mlpsa | Provides a summary of a 'mlpsa' class.
summary.psrange | Prints the summary results of psrange.
tree.plot | Heat map representing variables used in a conditional inference tree across level 2 variables.
xtable.mlpsa | Prints the results of [mlpsa()] as a LaTeX table.