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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-package 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.