Type: Package
Title: Multilevel Propensity Score Analysis
Version: 1.3.0
Date: 2025-04-03
Description: Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://jbryer.github.io/multilevelPSA/
BugReports: https://github.com/jbryer/multilevelPSA/issues/
Depends: ggplot2, R (≥ 3.0), xtable
Imports: grid, MASS, party, plyr, PSAgraphics, psych, reshape
Suggests: knitr, MatchIt, mice, rmarkdown, testthat
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-04-04 11:53:16 UTC; jbryer
Author: Jason Bryer ORCID iD [aut, cre]
Maintainer: Jason Bryer <jason@bryer.org>
Repository: CRAN
Date/Publication: 2025-04-04 13:10:02 UTC

Multilevel Propensity Score Analysis

Description

This packages provides functions to estimate and visualize multilevel propensity score analysis.

Details

This package extends the principles put forth by the PSAgraphics (Helmreich, Pruzek, & Xiong, 2010) for multilevel, or clustered, data.

Propensity score analyses are typically done in two phases. In phase I, a statistical model predicting treatment using the available individual covariates is estimated. This package currently currently provides functions to perform propensity score estimates using logistic regression (see [mlpsa.logistic()]) and conditional inference trees (see [mlpsa.ctree()]). The latter method provides explicit stratifications as defined by each leaf node. The former however, results in a numerical value ranging from zero to one (i.e. the fitted values). A common approach is to then create stratifications using quintiles. However, other approaches such as Loess regression are also provided.

Phase II of typical propensity score analyses concerns with the comparison of an outcome between the treatment and comparison groups. The [mlpsa()] method will perform this analysis in a multilevel, or clustered, fashion. That is, the results of the [mlpsa()] procedure produce summary results at level one (i.e. each strata within each cluster), level two (i.e. overall results for each cluster), and overall (i.e. overall results across all clusters).

This package also provides a number of visualizations that provide a critical part in presenting, understanding, and interpreting the results. See [plot.mlpsa()] for details.

Author(s)

Maintainer: Jason Bryer jason@bryer.org (ORCID)

References

[https://cran.r-project.org/package=PSAgraphics](https://www.oecd.org/en/about/programmes/pisa.html) [https://www.jstatsoft.org/article/view/v029i06](https://www.jstatsoft.org/article/view/v029i06)

See Also

PSAgraphics

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.

Description

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.

Usage

## S3 method for class 'plots'
align(x, ...)

Arguments

x

grid.layout

...

graphic elements to combine.

Returns the overall effects as a data frame.

Description

Returns the overall effects as a data frame.

Usage

## S3 method for class 'covariate.balance'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

results of [covariate.balance()].

row.names

unused.

optional

unused.

...

unused

Value

a data frame with overall covariate effects before and after adjustment.

Estimate covariate effect sizes before and after propensity score adjustment.

Description

Estimate covariate effect sizes before and after propensity score adjustment.

Usage

covariate.balance(covariates, treatment, level2, strata, abs = TRUE)

Arguments

covariates

frame or matrix of covariates.

treatment

vector of treatment indicators.

level2

vector indicating level 2 membership.

strata

strata indicators.

abs

if TRUE absolute values of effect sizes will be plotted.

Calculate covariate effect size differences before and after stratification.

Description

This function is modified from the [cv.bal.psa()] function in the PSAgrpahics package.

Usage

covariateBalance(
  covariates,
  treatment,
  propensity,
  strata = NULL,
  int = NULL,
  tree = FALSE,
  minsize = 2,
  universal.psd = TRUE,
  trM = 0,
  absolute.es = TRUE,
  trt.value = NULL,
  use.trt.var = FALSE,
  verbose = FALSE,
  xlim = NULL,
  plot.strata = TRUE,
  na.rm = TRUE,
  ...
)

Arguments

covariates

dataframe of interest

treatment

binary vector of 0s and 1s (necessarily? what if character, or 1, 2?)

propensity

PS scores from some method or other.

strata

either a vector of strata number for each row of covariate, or one number n in which case it is attempted to group rows by ps scores into n strata of size approximately 1/n. This does not seem to work well in the case of few specific propensity values, as from a tree.

int

either a number m used to divide [0,1] into m equal length subintervals, or a vector of cut points between 0 an 1 defining the subintervals (perhaps as suggested by loess.psa). In either case these subintervals define strata, so strata can be of any size.

tree

logical, if unique ps scores are few, as from a recursively partitioned tree, then TRUE will force each ps value to define a stratum.

minsize

smallest allowable stratum-treatment size. If violated, strata is removed.

universal.psd

If 'TRUE', forces standard deviations used to be unadjusted for stratification.

trM

trimming proportion for mean calculations.

absolute.es

logical, if 'TRUE' routine uses absolute values of all effect sizes.

trt.value

allows user to specify which value is active treatment, if desired.

use.trt.var

logical, if true then Rubin-Stuart method using only treatment variance with be used in effect size calculations.

verbose

logical, controls output that is visibly returned.

xlim

limits for the x-axis.

plot.strata

logical indicating whether to print strata.

na.rm

should missing values be removed.

...

currently unused.

Details

Note: effect sizes are calculated as treatment 1 - treatment 0, or treatment B - treatment A.

Author(s)

Robert M. Pruzek RMPruzek@yahoo.com

James E. Helmreich James.Helmreich@Marist.edu

KuangNan Xiong harryxkn@yahoo.com

Jason Bryer jason@bryer.org

Transformation of Factors to Individual Levels

Description

The function cv.trans.psa takes a covariate data frame and replaces each categorical covariate of n >=3 levels with n new binary covariate columns, one for each level. Transforms covariate dataframe for use with the function cv.bal.psa.

Usage

cv.trans.psa(covariates, fcol = NULL)

Arguments

covariates

A dataframe of covariates, presumably some factors.

fcol

An optional vector containing the factor columns in the covariate data frame. In NULL (default) routine to identify factors internally.

Details

NOTE: This function originated in the PSAgraphics package. It has been adapted here for the multilevelPSA package.

Author(s)

James E. Helmreich James.Helmreich@Marist.edu

Robert M. Pruzek RMPruzek@yahoo.com

KuangNan Xiong harryxkn@yahoo.com

Jason Bryer jason@bryer.org

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

Description

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

Usage

difftable.plot(x, fill.colors = NULL, legendlab = NULL, ...)

Arguments

x

the results of [mlpsa()].

fill.colors

the colors to use for each level two.

legendlab

the label to use for the legend, or NULL to exclude.

...

currently unused.

Value

a ggplot2 figure

Returns a data frame with two columns corresponding to the level 2 variable and the fitted value from the logistic regression.

Description

Returns a data frame with two columns corresponding to the level 2 variable and the fitted value from the logistic regression.

Usage

getPropensityScores(lr.results, nStrata = 5)

Arguments

lr.results

the results of [mlpsa.logistic()]

nStrata

number of strata within each level.

Value

a data frame

See Also

mlpsa.logistic

Returns a data frame with two columns corresponding to the level 2 variable and the leaves from the conditional inference trees.

Description

Returns a data frame with two columns corresponding to the level 2 variable and the leaves from the conditional inference trees.

Usage

getStrata(party.results, data, level2)

Arguments

party.results

the results of [mlpsa.ctree()]

data

the data frame to merge results to

level2

the name of the level 2 variable.

Value

a data frame

See Also

mlpsa.ctree

Returns true if the object is of type mlpsa

Description

Returns true if the object is of type mlpsa

Usage

is.mlpsa(x)

Arguments

x

the object to test

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

Description

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

Usage

loess.plot(
  x,
  response,
  treatment,
  responseTitle = "",
  treatmentTitle = "Treatment",
  percentPoints.treat = 0.1,
  percentPoints.control = 0.01,
  points.treat.alpha = 0.1,
  points.control.alpha = 0.1,
  plot.strata,
  plot.strata.alpha = 0.2,
  ...
)

Arguments

x

vector of propensity scores.

response

the response variable.

treatment

the treatment variable as a logical type.

responseTitle

the label to use for the y-axis (i.e. the name of the response variable)

treatmentTitle

the label to use for the treatment legend.

percentPoints.treat

the percentage of treatment points to randomly plot.

percentPoints.control

the percentage of control points to randomly plot.

points.treat.alpha

the transparency level for treatment points.

points.control.alpha

the transparency level for control points.

plot.strata

an integer value greater than 2 indicating the number of vertical lines to plot corresponding to quantiles.

plot.strata.alpha

the alpha level for the vertical lines.

...

other parameters passed to [ggplot2::geom_smooth()] and [ggplot2::stat_smooth()].

Value

a ggplot2 figure

See Also

plot.mlpsa

Examples

## Not run: 
require(multilevelPSA)
require(party)
data(pisana)
data(pisa.psa.cols)
cnt = 'USA' #Can change this to USA, MEX, or CAN
pisana2 = pisana[pisana$CNT == cnt,]
pisana2$treat <- as.integer(pisana2$PUBPRIV) %% 2
lr.results <- glm(treat ~ ., data=pisana2[,c('treat',pisa.psa.cols)], family='binomial')
st = data.frame(ps=fitted(lr.results), 
				math=apply(pisana2[,paste('PV', 1:5, 'MATH', sep='')], 1, mean), 
				pubpriv=pisana2$treat)
				st$treat = as.logical(st$pubpriv)
loess.plot(st$ps, response=st$math, treatment=st$treat, percentPoints.control = 0.4, 
           percentPoints.treat=0.4)

## End(Not run)

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

Description

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

Usage

lsos(..., n = 10)

Arguments

...

not used.

n

the number of objects to return.

Value

a list of objects loaded sorted by size.

Returns a heat map graphic representing missingness of variables grouped by the given grouping vector.

Description

NOTE: This is an experimental function and the results may vary depending on the nature of the dataset.

Usage

missing.plot(
  x,
  grouping,
  grid = FALSE,
  widths = c(ggplot2::unit(3, "null"), ggplot2::unit(1, "inches")),
  heights = c(ggplot2::unit(1, "inches"), ggplot2::unit(3, "null")),
  color = "red",
  ...
)

Arguments

x

a data frame containing the variables to visualize missingness

grouping

a vector of length nrow(vars) corresponding to how missing will be grouped by

grid

whether to draw a grid between tiles

widths

the ratio of the widths of the heatmap and histogram.

heights

the ratio of the heights of the heatmap and histogram.

color

the color used for indicating missingness.

...

currently unused.

Value

a ggplot2 expression

See Also

plot.mlpsa

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

Description

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

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.

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)

Plots the results of a multilevel propensity score model.

Description

The plot created uses the ggplot2 framework. As such, additional modifications can be made. This plot is an extension of the circ.psa function in the PSAgraphics package for multilevel models.

Usage

mlpsa.circ.plot(
  x,
  xlab = names(multilevelPSA$level2.summary)[4],
  ylab = names(multilevelPSA$level2.summary)[5],
  legendlab = "Level 2",
  title = NULL,
  overall.col = "blue",
  overall.ci.col = "green",
  level1.plot = FALSE,
  level1.point.size = NULL,
  level1.rug.plot = NULL,
  level1.projection.lines = FALSE,
  level2.plot = TRUE,
  level2.point.size = NULL,
  level2.rug.plot = "tr",
  level2.projection.lines = TRUE,
  level2.label = FALSE,
  unweighted.means = FALSE,
  weighted.means = FALSE,
  fill.colors = NULL,
  plot.adjusted.means = TRUE,
  ...
)

Arguments

x

the results of [mlpsa()].

xlab

label for the x-axis.

ylab

label for the y-axis.

legendlab

the label for the legend, or NULL to exclude.

title

title for the figure.

overall.col

the color used for the overall results.

overall.ci.col

the color used for the confidence intervals.

level1.plot

logical value indicating whether level 1 points should be plotted.

level1.point.size

the size of level 1 points

level1.rug.plot

the placement for plotting a level 2 rug. Possible values are bl (for left and bottom), tr (for top and right), or NULL (to exclude).

level1.projection.lines

logical value indicating whether level 1 project lines (parallel to the unit line) are drawn.

level2.plot

logical value indicating whether level 2 points should be plotted.

level2.point.size

the size of level 2 points

level2.rug.plot

the placement for plotting a level 2 rug. Possible values are bl (for left and bottom), tr (for top and right), or NULL (to exclude).

level2.projection.lines

logical value indicating whether level 2 project lines (parallel to the unit line) are drawn.

level2.label

logical value indicating whether level 2 points should be labeled.

unweighted.means

logical value indicating whether horizontal and vertical lines are drawn representing the unweighted (i.e. unadjusted from phase I of PSA) means for each level 2, or cluster.

weighted.means

logical value indicating whether horizontal and vertical lines are drawn representing the weighted means for each level 2, or cluster.

fill.colors

if specified, the colors to use for level 2 points.

plot.adjusted.means

whether to plot verticial and horizontal lines for the adjsuted means.

...

currently unused.

See Also

plot.mlpsa

Examples

## Not run: 
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)
mlpsa.circ.plot(results.psa.math, legendlab=FALSE)

## End(Not run)

Estimates propensity scores using the recursive partitioning in a conditional inference framework.

Description

This function will estimate propensity scores using the conditional inference framework as outlined in the party package. Specifically, a separate tree will be estimated for each level 2 (or cluster). A key advantage of this framework over other methods for estimating propensity scores is that this method will work on data sets containing missing values.

Usage

mlpsa.ctree(vars, formula, level2, ...)

Arguments

vars

a data frame containing the covariates to use for estimating the propensity scores.

formula

the model for estimating the propensity scores. For example, treat ~ .

level2

the name of the column in vars specifying the level 2 (or cluster).

...

currently unused.

Value

a list of BinaryTree-class classes for each level 2

References

Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006). Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651–674.

See Also

[getStrata()]

[tree.plot()]

Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.

Description

Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.

Usage

mlpsa.difference.plot(
  x,
  xlab,
  ylab = NULL,
  title = NULL,
  overall.col = "blue",
  overall.ci.col = "green",
  level2.point.size = NULL,
  level1.points = TRUE,
  errorbars = TRUE,
  errorbars.adjusted.ci = TRUE,
  level2.rug.plot = TRUE,
  jitter = TRUE,
  reorder = TRUE,
  labelLevel2 = TRUE,
  sd = NULL,
  xlim,
  ...
)

Arguments

x

the results of [mlpsa()].

xlab

label for the x-axis, or NULL to exclude.

ylab

label for the y-axis, or NULL to exclude.

title

title of the figure, or NULL to exclude.

overall.col

the color of the overall results line.

overall.ci.col

the color of the overall confidence interval.

level2.point.size

the point size of level 2 points.

level1.points

logical value indicating whether level 1 strata should be plotted.

errorbars

logical value indicating whether error bars should be plotted for for each level 1.

errorbars.adjusted.ci

whether the Bonferroni adjusted error bars should be plotted (these will be dashed lines).

level2.rug.plot

logical value indicating whether a rug plot should be plotted for level 2.

jitter

logical value indicating whether level 1 points should be jittered.

reorder

logical value indicating whether the level two clusters should be reordered from largest difference to smallest.

labelLevel2

logical value indicating whether the difference for each level 2 should be labeled.

sd

If specified, effect sizes will be plotted instead of difference in the native unit.

xlim

the limits of the x-axis.

...

currently unused.

See Also

plot.mlpsa

Examples

## Not run: 
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$CNT, minN=5)
mlpsa.difference.plot(results.psa.math, sd=mean(student.party$mathscore, na.rm=TRUE))

## End(Not run)

Plots distribution for either the treatment or comparison group.

Description

Plots distribution for either the treatment or comparison group.

Usage

mlpsa.distribution.plot(
  x,
  treat,
  fill.colours = NULL,
  flip = TRUE,
  label = treat,
  level2.label = NULL,
  legendlab = NULL,
  axis.text.size = 8,
  fill.colors = NULL,
  ...
)

Arguments

x

the results of [mlpsa()].

treat

the group to plot. This must be one of the two levels of the treatment variable.

fill.colours

if specified, the colors to use for level 2 points.

flip

if TRUE, the level 2 clusters will be on the y-axis and the outcome variable on the x-axis. Otherwise reversed.

label

the label to use for the axis.

level2.label

the axis label for the level 2 indicators.

legendlab

the label for the legend, or NULL to exclude a legend.

axis.text.size

the size of the axis text

fill.colors

if specified, the colors to use for level 2 points.

...

currently unused.

See Also

plot.mlpsa

Estimates propensity scores using logistic regression.

Description

This method will estimate a separate logistic regression model for each level 2 (or cluster).

Usage

mlpsa.logistic(vars, formula, level2, stepAIC = FALSE, ...)

Arguments

vars

data frame containing the variables to estimate the logistic regression

formula

the logistic regression formula to use

level2

the name of the column containing the level 2 specification

stepAIC

if true, the [MASS::stepAIC()] from the 'MASS' package will be used within each level.

...

currently unused.

Value

a list of glm classes for each level 2 or stepwise-selected model if stepAIC is true.

See Also

getPropensityScores

Mapping of variables in 'pisana' with full descriptions.

Description

This data frame provides three variables, 'Variable' corresponding to the column names in 'pisana', 'ShortDesc' providing a short description of the variable as a valid R object name, and 'Desc' providing a longer description of the variable.

Format

a data frame with 50 rows of 3 variables.

Data frame mapping PISA countries to their three letter abbreviation.

Description

This data frame has two columns, CNT3 for the three letter abbreviation of each country and Country that provides the full country name in English.

Format

data frame with 65 rows of 2 variables.

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

Description

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

Format

a character vector with covariate names for estimating propensity scores.

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

Description

Student results from the 2009 Programme of International Student Assessment (PISA) as provided by the Organization for Economic Co-operation and Development (OECD). See [https://www.oecd.org/en/about/programmes/pisa.html/](https://www.oecd.org/en/about/programmes/pisa.html/) for more information including the code book.

Format

a data frame with 66,548 obvservations of 65 variables.

Details

Note that missing values have been imputed using the [mice](https://cran.r-project.org/package=mice) package. Details on the specific procedure are in the 'pisa.impute' function in the [pisa](https://github.com/jbryer/pisa) package.

Source

Organization for Economic Co-operation and Development

References

Organization for Economic Co-operation and Development (2009). Programme for International Student Assessment (PISA). [https://www.oecd.org/en/about/programmes/pisa.html](https://www.oecd.org/en/about/programmes/pisa.html)

Multiple covariate balance assessment plot.

Description

A graphic based upon [cv.bal.psa()] function in the 'PSAgraphics' package. This graphic plots the effect sizes for multiple covariates before and after propensity score adjustement.

Usage

## S3 method for class 'covariate.balance'
plot(
  x,
  plot.strata = FALSE,
  order = c("unadjusted", "adjusted"),
  strata.size = 3,
  strata.legend.guide = "none",
  point.size = 3,
  point.alpha = 1,
  line.color = "black",
  line.alpha = 0.2,
  legend.position = c(0.8, 0.2),
  ...
)

Arguments

x

results of [covariate.balance()].

plot.strata

whether individual strata should be plotted.

order

how to order the y-axis. Possible values are adjusted, unadjusted, or NULL (don't reorder).

strata.size

text size for strata if plotted.

strata.legend.guide

guide for legend placement for strata.

point.size

size of the overall effect size points.

point.alpha

transparency level of the overall effect size points.

line.color

the color of the line connecting the overall effect size points.

line.alpha

transparency level of the line connecting the overall effect size points.

legend.position

where to position the legend.

...

currently unused.

Value

a ggplot2 with an attribute, effects, that is the data frame used to create the plot.

Plots the results of a multilevel propensity score model.

Description

The plot created uses the ggplot2 framework. As such, additional modifications can be made. This plot is an extension of the circ.psa function in the PSAgraphics package for multilevel models.

Usage

## S3 method for class 'mlpsa'
plot(x, ratio = c(1, 2), plotExtra = NULL, ...)

Arguments

x

the results of [mlspa()].

ratio

the ratio of the size of the distribution plots (left and bottom) to the circular plot.

plotExtra

a plot to place in the lower left corner.

...

parameters passed to [mlpsa.circ.plot()] and [mlpsa.distribution.plot()]

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)
plot(results.psa.math)

## End(Not run)

Plots densities and ranges for the propensity scores.

Description

Plots densities and ranges for the propensity scores.

Usage

## S3 method for class 'psrange'
plot(
  x,
  xlab = NULL,
  ylab = NULL,
  labels = c("Comparison", "Treatment"),
  text.ratio.size = 4,
  text.ncontrol.size = 3,
  point.size = 1,
  point.alpha = 0.6,
  line.width = 6,
  density.alpha = 0.2,
  rect.color = "green",
  rect.alpha = 0.2,
  ...
)

Arguments

x

the result of psrange.

xlab

label for x-axis.

ylab

label for y-axis.

labels

labels for the comparison and treatment legend.

text.ratio.size

size of the text for the ratio.

text.ncontrol.size

size of the text for the number of control units.

point.size

size of the points for the minimum and maximum ranges for each model.

point.alpha

the alpha (transparency) level for the points.

line.width

the width of the line between the median of the minimum and maximum ranges.

density.alpha

the alpha (transparency) level of the density curves.

rect.color

the color of the rectangle surrounding the range of minimum and maximum ranges.

rect.alpha

the alpha (transparency) level of the rectangle.

...

currently unused.

Value

a ggplot2 object

Prints the overall effects before and after propensity score adjustment.

Description

Prints the overall effects before and after propensity score adjustment.

Usage

## S3 method for class 'covariate.balance'
print(x, ...)

Arguments

x

results of [covariate.balance()].

...

unused.

Prints basic information about a mlpsa class.

Description

Prints basic information about a mlpsa class.

Usage

## S3 method for class 'mlpsa'
print(x, ...)

Arguments

x

the mlpsa class.

...

unused.

Prints information about a psrange result.

Description

Prints information about a psrange result.

Usage

## S3 method for class 'psrange'
print(x, ...)

Arguments

x

psrange to print info about.

...

currently unused

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

Description

Print method for [xtable.mlpsa()].

Usage

## S3 method for class 'xmlpsa'
print(x, tabular.environment = "longtable", floating = FALSE, ...)

Arguments

x

result of [xtable.mlpsa()]

tabular.environment

see [xtable::print.xtable()].

floating

see [xtable::print.xtable()].

...

other parameters passed to [xtable::print.xtable()]

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

Description

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

Usage

psrange(
  df,
  treatvar,
  formula,
  nsteps = 10,
  nboot = 10,
  samples,
  type = c("logistic", "ctree"),
  ...
)

Arguments

df

data frame with variables to pass to glm

treatvar

vector representing treatment placement. Should be coded as 0s (for control) and 1s (for treatment).

formula

formula for logistic regression model

nsteps

number of steps to estimate from 1:1 to using all control records.

nboot

number of models to execute for each step.

samples

the sample sizes to draw from control group for each step.

type

either logistic for Logistic regression (using glm function) or ctree for Conditional Inference Trees (using the ctree function).

...

other parameters passed to glm.

Value

a class of psrange that contains a summary data frame, a details data frame, and a list of each individual result from glm.

Provides a summary of a mlpsa class.

Description

Provides a summary of a mlpsa class.

Usage

## S3 method for class 'mlpsa'
summary(object, overall.label = "Overall", ...)

Arguments

object

the mlpsa object.

overall.label

the label to place in the strata column for the overall results.

...

unused.

Prints the summary results of psrange.

Description

Prints the summary results of psrange.

Usage

## S3 method for class 'psrange'
summary(object, ...)

Arguments

object

psrange to print summary of.

...

currently unused.

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

Description

This figure provides a summary of the covariates used within each level two cluster along with their relative importance. Covariates are listed on the y-axis and level two clusters along the x-axis. Cells that are shaded indicate that that covariate was present in the conditional. The shade of the color represents the highest level within the tree that covariate appeared. That is, the darkest color, or depth 1, corresponds to the covariate used at the root of the tree, or the first split.

Usage

tree.plot(
  x,
  colNames,
  level2Col,
  colLabels = NULL,
  color.high = "azure",
  color.low = "steelblue",
  color.na = "white",
  ...
)

Arguments

x

the results of [mlpsa.ctree()]

colNames

the columns to include in the graphic

level2Col

the name of the level 2 column.

colLabels

column labels to use. This is a data frame with two columns, the first column should match the values in trees and the second column the description that will be used for labeling the variables.

color.high

color for variables with less relative importance as determined by occurring later in the tree (further from the root split).

color.low

color for variables with greater relative importance as determined by occurring sooner in the tree (closer to the root split).

color.na

color for variables that do not occur in the tree.

...

currently unused.

Value

a ggplot2 expression

See Also

plot.mlpsa

Examples

## Not run: 
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')
tree.plot(mlctree, level2Col=pisana$CNT)

## End(Not run)

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

Description

This function implements the [xtable::xtable()] method for [mlpsa()].

Usage

## S3 method for class 'mlpsa'
xtable(
  x,
  caption,
  label,
  align,
  digits = 2,
  display = NULL,
  auto = FALSE,
  include.note = TRUE,
  ...
)

Arguments

x

results of [mlpsa()]

caption

passed through to [xtable::xtable()].

label

passed through to [xtable::xtable()].

align

Not used.

digits

number of digits to print.

display

passed through to [xtable::xtable()].

auto

passed through to [xtable::xtable()].

include.note

include a table note indicating how many rows were removed due to insufficient data within a strata.

...

other parameters passed to [summary.mlpsa()]

The zero grob draws nothing and has zero size.

Description

The zero grob draws nothing and has zero size.

Usage

zeroGrob()

Author(s)

hadley

References

https://github.com/hadley/ggplot2/blob/master/R/grob-null.r