multilevelPSA (version 1.3.0)

Multilevel Propensity Score Analysis

Description

Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) .

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Version

Install

install.packages('multilevelPSA')

Monthly Downloads

220

Version

1.3.0

License

GPL (>= 2)

Maintainer

Jason Bryer

Last Published

April 4th, 2025

Functions in multilevelPSA (1.3.0)

covariateBalance

Calculate covariate effect size differences before and after stratification.

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.

as.data.frame.covariate.balance

Returns the overall effects as a data frame.

loess.plot

Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.

cv.trans.psa

Transformation of Factors to Individual Levels

mlpsa.logistic

Estimates propensity scores using logistic regression.

mlpsa.distribution.plot

Plots distribution for either the treatment or comparison group.

multilevelPSA-package

Multilevel Propensity Score Analysis

pisana

North American (i.e. Canada, Mexico, and United States) student results of the 2009 Programme of International Student Assessment.

pisa.colnames

Mapping of variables in `pisana` with full descriptions.

difftable.plot

This function produces a ggplot2 figure containing the mean differences for each level two, or cluster.

mlpsa

This function will perform phase II of the multilevel propensity score analysis.

plot.covariate.balance

Multiple covariate balance assessment plot.

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.

print.covariate.balance

Prints the overall effects before and after propensity score adjustment.

plot.mlpsa

Plots the results of a multilevel propensity score model.

mlpsa.circ.plot

Plots the results of a multilevel propensity score model.

plot.psrange

Plots densities and ranges for the propensity scores.

print.mlpsa

Prints basic information about a mlpsa class.

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.

pisa.countries

Data frame mapping PISA countries to their three letter abbreviation.

print.psrange

Prints information about a psrange result.

print.xmlpsa

Prints the results of [mlpsa()] and [xtable.mlpsa()].

xtable.mlpsa

Prints the results of [mlpsa()] as a LaTeX table.

pisa.psa.cols

Character vector representing the list of covariates used for estimating propensity scores.

summary.psrange

Prints the summary results of psrange.

psrange

Estimates models with increasing number of comparison subjects starting from 1:1 to using all available comparison group subjects.

zeroGrob

The zero grob draws nothing and has zero size.

tree.plot

Heat map representing variables used in a conditional inference tree across level 2 variables.

summary.mlpsa

Provides a summary of a mlpsa class.

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.

covariate.balance

Estimate covariate effect sizes before and after propensity score adjustment.

is.mlpsa

Returns true if the object is of type mlpsa