Package 'PSAboot' reference manual

Title:	Bootstrapping for Propensity Score Analysis
Description:	It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using 'rpart' and 'ctree' functions), two matching methods (using 'Matching' and 'MatchIt' packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.
Authors:	Jason Bryer [aut, cre]
Maintainer:	Jason Bryer <[email protected]>
License:	GPL
Version:	1.3.8
Built:	2024-11-16 04:51:13 UTC
Source:	https://github.com/jbryer/psaboot

Bootstrapping for Propensity Score Analysis

Description

Bootstrapping procedures for Propensity Score Analysis.

Convert the results of PSAboot summary to a data frame.

Description

Convert the results of PSAboot summary to a data frame.

Usage

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

Arguments

`x`	results of `summary.PSAboot`
`row.names`	row names.
`optional`	unused.
`...`	unused.

Value

a data.frame.

Returns a summary of the balance for all bootstrap samples.

Description

This method provides some crude overall measures of balance.

Usage

balance(psaboot, na.rm = TRUE, pool.fun = mean)
balance(psaboot, na.rm = TRUE, pool.fun = mean)

Arguments

`psaboot`	results from `PSAboot`.
`na.rm`	should NAs be removed. NAs generally occur when there is insufficient sample for a particular covariate or an unused level.
`pool.fun`	a function specifying how the effect sizes across all covariates should be combined. Possible values include `mean` (default), `q25`, `q75`, `median`, `max`, or any function that takes a vector of numeric values.

Value

a list with three elements:

unadjusted: named numeric vector with unadjusted effect size before adjustment for each covariate
complete: a matrix with adjusted effect size for each covariate (columns) for each method (rows).
pooled: a matrix with mean adjusted effect size for all covariates for each method (columns) and each bootstrap sample (rows).
balances: a list with an M x n covariates matrix for each method.

Examples


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)

Returns balance for each covariate from propensity score matching.

Description

This function is function is primarily used by [PSAboot::balance()] and probably does not need to be called directly.

Usage

balance.matching(index.treated, index.control, covs)
balance.matching(index.treated, index.control, covs)

Arguments

`index.treated`	a vector with the index of treated rows in `covs`.
`index.control`	a vector with the index of control rows in `covs`.
`covs`	data frame or matrix of covariates. Factors should already be recoded. See `cv.trans.psa`

Value

a named vector with one element per covariate.

Stratification using classification trees for bootstrapping.

Description

Stratification using classification trees for bootstrapping.

Usage

boot.ctree(Tr, Y, X, X.trans, formu, minStrata = 5, ...)
boot.ctree(Tr, Y, X, X.trans, formu, minStrata = 5, ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`minStrata`	minimum number of treatment or control units within a strata to include that strata.
`...`	other parameters passed from `PSAboot`

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

Matching package implementation for bootstrapping.

Description

Matching package implementation for bootstrapping.

Usage

boot.matching(Tr, Y, X, X.trans, formu, estimand = "ATE", ...)
boot.matching(Tr, Y, X, X.trans, formu, estimand = "ATE", ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`estimand`	character string for estimand, either ATE, ATT, or ATC. See `Match` for more details.
`...`	other parameters passed to `Match`.

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

MatchIt package implementation for bootstrapping.

Description

MatchIt package implementation for bootstrapping.

Usage

boot.matchit(Tr, Y, X, X.trans, formu, ...)
boot.matchit(Tr, Y, X, X.trans, formu, ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`...`	other parameters passed from `PSAboot`

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

Stratification using classification trees for bootstrapping.

Description

Stratification using classification trees for bootstrapping.

Usage

boot.rpart(Tr, Y, X, X.trans, formu, minStrata = 5, ...)
boot.rpart(Tr, Y, X, X.trans, formu, minStrata = 5, ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`minStrata`	minimum number of treatment or control units within a strata to include that strata.
`...`	other parameters passed from `PSAboot`

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

Stratification implementation for bootstrapping.

Description

Stratification implementation for bootstrapping.

Usage

boot.strata(Tr, Y, X, X.trans, formu, nstrata = 5, ...)
boot.strata(Tr, Y, X, X.trans, formu, nstrata = 5, ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`nstrata`	number of strata to divide the propensity scores.
`...`	other parameters passed from `PSAboot`

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

Propensity score weighting implementation for bootstrapping.

Description

Propensity score weighting implementation for bootstrapping.

Usage

boot.weighting(Tr, Y, X, X.trans, formu, estimand = "ATE", ...)
boot.weighting(Tr, Y, X, X.trans, formu, estimand = "ATE", ...)

Arguments

`Tr`	vector indicating treatment assignment.
`Y`	vector of outcome.
`X`	matrix or data frame of covariates.
`X.trans`	a data frame of `X` with factors recoded. See `cv.trans.psa`
`formu`	the formula to use to estimate propensity scores. Note that the dependent varaible (i.e. treatment varaible) name will be updated using the `Tr` vector.
`estimand`	which treatment effect to estimate. Values can be ATE, ATT, ATC, or ATM.
`...`	other parameters passed from `PSAboot`

Value

a list with three elements:

summary: a named numeric vector (with at minimum estimate, ci.min, and ci.max but other values allowed)
balance: a named numeric vector with one element per covariate listed in X.trans representing a balance statistic (usually standardized effect size after adjustment)
details: an arbitrary object that contains the full results of the analysis

Boxplot of PSA bootstrap results.

Description

Boxplot of PSA bootstrap results.

Usage

## S3 method for class 'PSAboot'
boxplot(
  x,
  bootstrap.mean.color = "blue",
  bootstrap.ci.color = "green",
  bootstrap.ci.width = 0.5,
  bootstrap.ci.size = 3,
  overall.mean.color = "red",
  tufte = FALSE,
  coord.flip = TRUE,
  ...
)
## S3 method for class 'PSAboot'
boxplot(
  x,
  bootstrap.mean.color = "blue",
  bootstrap.ci.color = "green",
  bootstrap.ci.width = 0.5,
  bootstrap.ci.size = 3,
  overall.mean.color = "red",
  tufte = FALSE,
  coord.flip = TRUE,
  ...
)

Arguments

`x`	result of `PSAboot`.
`bootstrap.mean.color`	the color of the point for the bootstrap mean, or NA to omit.
`bootstrap.ci.color`	the color of the confidence intervals of the bootstrap samples, or NA to omit.
`bootstrap.ci.width`	the width of the confidence interval lines at the end.
`bootstrap.ci.size`	the size of the confidence interval lines.
`overall.mean.color`	the color of the point for the overall (before bootstrapping) mean, or NA to omit.
`tufte`	use Tufte's boxplot style. Requires the `ggthemes` package.
`coord.flip`	Whether to flip the coordinates.
`...`	unused

Value

a ggplot2 expression.

Boxplot of the balance statistics for bootstrapped samples.

Description

Boxplot of the balance statistics for bootstrapped samples.

Usage

## S3 method for class 'PSAboot.balance'
boxplot(
  x,
  unadjusted.color = "red",
  pooled.color = "blue",
  point.size = 3,
  point.alpha = 0.5,
  ...
)
## S3 method for class 'PSAboot.balance'
boxplot(
  x,
  unadjusted.color = "red",
  pooled.color = "blue",
  point.size = 3,
  point.alpha = 0.5,
  ...
)

Arguments

`x`	results of `balance`
`unadjusted.color`	the color used for the unadjusted effect size.
`pooled.color`	the color used for the mean bootstrap effect size.
`point.size`	the size of the points.
`point.alpha`	the transparency level for the points.
`...`	other parameters passed to `facet_wrap`

Value

a ggplot2 expression.

Examples


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)
boxplot(bm.usa.bal, nrow = 1)

library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)
boxplot(bm.usa.bal, nrow = 1)

Calculates propensity score weights.

Description

Calculates propensity score weights.

Usage

calculate_ps_weights(treatment, ps, estimand = "ATE")
calculate_ps_weights(treatment, ps, estimand = "ATE")

Arguments

`treatment`	a logical vector for treatment status.
`ps`	numeric vector of propensity scores
`estimand`	character string indicating which estimand to be used. Possible values are ATE (average treatment effect), ATT (average treatment effect for the treated), ATC (average treatement effect for the controls), ATM (Average Treatment Effect Among the Evenly Matchable), ATO (Average Treatment Effect Among the Overlap Populatio)

Returns a vector with the default methods used by `PSAboot`.

Description

The current default methods are:

Stratification: boot.strata
ctree: boot.ctree
rpart: boot.rpart
Matching: boot.matching
MatchIt: boot.matchit

Usage

getPSAbootMethods()
getPSAbootMethods()

Details

The default methods can be changed by setting the PSAboot.methods option using options('PSAboot.methods'=c(...)) where ... is a named list of functions.

Value

a vector of methods for use by PSAboot

Histogram of PSA bootstrap results

Description

Histogram of PSA bootstrap results

Usage

## S3 method for class 'PSAboot'
hist(x, ...)
## S3 method for class 'PSAboot'
hist(x, ...)

Arguments

`x`	result of `PSAboot`.
`...`	other parameters passed to `geom_histogram`

Value

a ggplot2 expression.

Matrix Plot of Bootstrapped Propensity Score Analysis

Description

Matrix Plot of Bootstrapped Propensity Score Analysis

Usage

matrixplot(bm)
matrixplot(bm)

Arguments

`bm`	result from `PSAboot`.

Value

Nothing returned. Creates a plot using the [graphics::pairs()] function.

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.

Programme of International Student Assessment (PISA) results from the Luxembourg in 2009.

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/pisa/ for more information including the code book.

Format

a data frame with 4,622 rows and 65 columns.

CNT: Country
SCHOOLID: SchoolID
ST01Q01: Grade
ST04Q01: Sex
ST05Q01: Attend
ST06Q01: Age
ST07Q01: Repeat
ST08Q01: At home mother
ST08Q02: At home father
ST08Q03: At home brothers
ST08Q04: At home sisters
ST08Q05: At home grandparents
ST08Q06: At home others
ST10Q01: Mother highest schooling
ST12Q01: Mother current job status
ST14Q01: Father highest schooling
ST16Q01: Father current job status
ST19Q01: Language at home
ST20Q01: Desk
ST20Q02: Own room
ST20Q03: Study place
ST20Q04: Computer
ST20Q05: Software
ST20Q06: Internet
ST20Q07: Literature
ST20Q08: Poetry
ST20Q09: Art
ST20Q10: Textbooks
ST20Q12: Dictionary
ST20Q13: Dishwasher
ST20Q14: DVD
ST21Q01: How many cellphones
ST21Q02: How many TVs
ST21Q03: How many computers
ST21Q04: How many cars
ST21Q05: How many rooms bath or shower
ST22Q01: How many books
ST23Q01: Reading enjoyment time
ST31Q01: Enrich in test language
ST31Q02: Enrich in mathematics
ST31Q03: Enrich in science
ST31Q05: Remedial in test language
ST31Q06: Remedial in mathematics
ST31Q07: Remedial in science
ST32Q01: Out of school lessons in test language
ST32Q02: Out of school lessons maths
ST32Q03: Out of school lessons in science
PUBPRIV: Public or private school
STRATIO: Student to teacher ratio in school

Details

Note that missing values have been imputed using the mice package. Details on the specific procedure are in the pisa.impute function in the pisa package.

References

Organisation for Economic Co-operation and Development (2009). Programme for International Student Assessment (PISA).

Programme of International Student Assessment (PISA) results from the United States in 2009.

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 www.oecd.org/pisa/ for more information including the code book.

Format

a data frame with 5,233 rows and 65 columns.

CNT: Country
SCHOOLID: SchoolID
ST01Q01: Grade
ST04Q01: Sex
ST05Q01: Attend
ST06Q01: Age
ST07Q01: Repeat
ST08Q01: At home mother
ST08Q02: At home father
ST08Q03: At home brothers
ST08Q04: At home sisters
ST08Q05: At home grandparents
ST08Q06: At home others
ST10Q01: Mother highest schooling
ST12Q01: Mother current job status
ST14Q01: Father highest schooling
ST16Q01: Father current job status
ST19Q01: Language at home
ST20Q01: Desk
ST20Q02: Own room
ST20Q03: Study place
ST20Q04: Computer
ST20Q05: Software
ST20Q06: Internet
ST20Q07: Literature
ST20Q08: Poetry
ST20Q09: Art
ST20Q10: Textbooks
ST20Q12: Dictionary
ST20Q13: Dishwasher
ST20Q14: DVD
ST21Q01: How many cellphones
ST21Q02: How many TVs
ST21Q03: How many computers
ST21Q04: How many cars
ST21Q05: How many rooms bath or shower
ST22Q01: How many books
ST23Q01: Reading enjoyment time
ST31Q01: Enrich in test language
ST31Q02: Enrich in mathematics
ST31Q03: Enrich in science
ST31Q05: Remedial in test language
ST31Q06: Remedial in mathematics
ST31Q07: Remedial in science
ST32Q01: Out of school lessons in test language
ST32Q02: Out of school lessons maths
ST32Q03: Out of school lessons in science
PUBPRIV: Public or private school
STRATIO: Student to teacher ratio in school

Details

Note that missing values have been imputed using the mice package. Details on the specific procedure are in the pisa.impute function in the pisa package.

References

Organisation for Economic Co-operation and Development (2009). Programme for International Student Assessment (PISA).

Plot the results of PSAboot

Description

Plot the results of PSAboot

Usage

## S3 method for class 'PSAboot'
plot(
  x,
  sort = "all",
  ci.sig.color = "red",
  plot.overall = FALSE,
  plot.bootstrap = TRUE,
  ...
)
## S3 method for class 'PSAboot'
plot(
  x,
  sort = "all",
  ci.sig.color = "red",
  plot.overall = FALSE,
  plot.bootstrap = TRUE,
  ...
)

Arguments

`x`	result of `PSAboot`.
`sort`	how the sort the rows by mean difference. Options are to sort using the mean difference from matching, stratification, both individually, or no sorting.
`ci.sig.color`	the color used for confidence intervals that do not span zero.
`plot.overall`	whether to plot vertical lines for the overall (non-bootstrapped) estimate and confidence interval.
`plot.bootstrap`	whether to plot vertical lines for the bootstrap pooled estimate and confidence interval.
`...`	currently unused.

Value

a ggplot2 expression.

Plot method for balance.

Description

Plot method for balance.

Usage

## S3 method for class 'PSAboot.balance'
plot(
  x,
  unadjusted.color = "red",
  complete.color = "blue",
  pooled.color = "black",
  ...
)
## S3 method for class 'PSAboot.balance'
plot(
  x,
  unadjusted.color = "red",
  complete.color = "blue",
  pooled.color = "black",
  ...
)

Arguments

`x`	results from `balance`
`unadjusted.color`	color of the vertical line representing the mean unadjusted effect size for all covariates.
`complete.color`	color of the vertical line representing the mean adjusted effect size for all covariates using the complete dataset.
`pooled.color`	color of the vertical line representing the mean adjusted effect size for all covariates across all bootstrapped samples.
`...`	currently unused.

Value

a ggplot2 expression.

Examples


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)
plot(bm.usa.bal)

library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)
bm.usa.bal <- balance(bm.usa)
plot(bm.usa.bal)

Print results of PSAboot

Description

Print results of PSAboot

Usage

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

Arguments

`x`	result of `PSAboot`.
`...`	currently unused.

Value

Nothing returned. S3 generic function that calls the [PSAboot::summary()] function.

Print method for balance.

Description

This is a crude measure of overall balance. Absolute value of the standardized effect sizes are calculated for each covariate. Overall balance statistics are the mean of those effect sizes after adjustment for each method across all bootstrap samples.

Usage

## S3 method for class 'PSAboot.balance'
print(x, na.rm = TRUE, ...)
## S3 method for class 'PSAboot.balance'
print(x, na.rm = TRUE, ...)

Arguments

`x`	results from `balance`.
`na.rm`	whether NA balance statistics should be removed before averaging them.
`...`	currently unused.

Value

No valued returned.

Print method for PSAboot Summary.

Description

Print method for PSAboot Summary.

Usage

## S3 method for class 'PSAbootSummary'
print(x, digits = 3, ...)
## S3 method for class 'PSAbootSummary'
print(x, digits = 3, ...)

Arguments

`x`	result of `summary.PSAboot`
`digits`	desired number of digits after the decimal point.
`...`	unused.

Value

Nothing returned.

Propensity Score Analysis using Stratification

Description

Propensity Score Analysis using Stratification

Usage

psa.strata(Y, Tr, strata, trim = 0, minStrata = 5)
psa.strata(Y, Tr, strata, trim = 0, minStrata = 5)

Arguments

`Y`	response variable.
`Tr`	treatment variable.
`strata`	strata identifier.
`trim`	allows for a trimmed mean as outcome measure, where trim is from 0 to .5 (.5 implying median).
`minStrata`	minimum number of treatment or control units within a strata to include that strata.

Value

a character vector containing summary.strata, ATE, se.wtd, approx.t, df, and CI.95.

Bootstrapping for propensity score analysis

Description

Bootstrapping has become a popular resampling method for estimating sampling distributions. And propensity score analysis (PSA) has become popular for estimating causal effects in observational studies. This function implements bootstrapping specifically for PSA. Like typical bootstrapping methods, this function estimates treatment effects for M random samples. However, unlike typical bootstrap methods, this function allows for separate sample sizes for treatment and control units. That is, under certain circumstances (e.g. when the ratio of treatment-to-control units is large) bootstrapping only the control units may be desirable. Additionally, this function provides a framework to use multiple PSA methods for each bootstrap sample.

Usage

PSAboot(
  Tr,
  Y,
  X,
  M = 100,
  formu = as.formula(paste0("treat ~ ", paste0(names(X), collapse = " + "))),
  control.ratio = 5,
  control.sample.size = min(control.ratio * min(table(Tr)), max(table(Tr))),
  control.replace = TRUE,
  treated.sample.size = min(table(Tr)),
  treated.replace = TRUE,
  methods = getPSAbootMethods(),
  parallel = TRUE,
  seed = NULL,
  ...
)
PSAboot(
  Tr,
  Y,
  X,
  M = 100,
  formu = as.formula(paste0("treat ~ ", paste0(names(X), collapse = " + "))),
  control.ratio = 5,
  control.sample.size = min(control.ratio * min(table(Tr)), max(table(Tr))),
  control.replace = TRUE,
  treated.sample.size = min(table(Tr)),
  treated.replace = TRUE,
  methods = getPSAbootMethods(),
  parallel = TRUE,
  seed = NULL,
  ...
)

Arguments

`Tr`	numeric (0 or 1) or logical vector of treatment indicators.
`Y`	vector of outcome variable
`X`	matrix or data frame of covariates used to estimate the propensity scores.
`M`	number of bootstrap samples to generate.
`formu`	formula used for estimating propensity scores. The default is to use all covariates in `X`.
`control.ratio`	the ratio of control units to sample relative to the treatment units.
`control.sample.size`	the size of each bootstrap sample of control units.
`control.replace`	whether to use replacement when sampling from control units.
`treated.sample.size`	the size of each bootstrap sample of treatment units. The default uses all treatment units for each bootstrap sample.
`treated.replace`	whether to use replacement when sampling from treated units.
`methods`	a named vector of functions for each PSA method to use.
`parallel`	whether to run the bootstrap samples in parallel.
`seed`	random seed. Each iteration, i, will use a seed of `seed + i`.
`...`	other parameters passed to `Match` and `psa.strata`

Value

a list with following elements:

overall.summary: Data frame with the results using the complete dataset (i.e. unbootstrapped results).
overall.details: Objects returned from each method for complete dataset.
pooled.summary: Data frame with results of each bootstrap sample.
pooled.details: List of objects returned from each method for each bootstrap sample.
control.sample.size: sample size used for control units.
treated.sample.size: sample size used for treated units.
control.replace: whether control units were sampled with replacement.
treated.replace: whether treated units were sampled with replacement.
Tr: vector of treatment assignment.
Y: vector out outcome.
X: matrix or data frame of covariates.
M: number of bootstrap samples.

Examples


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)


library(PSAboot)
data(pisa.psa.cols)
data(pisausa)
bm.usa <- PSAboot(Tr = as.integer(pisausa$PUBPRIV) - 1,
    Y = pisausa$Math,
    X = pisausa[,pisa.psa.cols],
    control.ratio = 5, M = 100, seed = 2112)

Return the 25th percentile.

Description

Return the 25th percentile.

Usage

q25(x, na.rm = FALSE, ...)
q25(x, na.rm = FALSE, ...)

Arguments

`x`	numeric vector.
`na.rm`	logical; if true, any NA and NaN's are removed from x before the quantiles are computed
`...`	other parameters passed to `quantile`.

Value

the 25th percentile.

Returns the 75th percentile.

Description

Returns the 75th percentile.

Usage

q75(x, na.rm = FALSE, ...)
q75(x, na.rm = FALSE, ...)

Arguments

`x`	numeric vector.
`na.rm`	logical; if true, any NA and NaN's are removed from x before the quantiles are computed
`...`	other parameters passed to `quantile`.

Value

the 75th percentile.

Summary of pooled results from PSAboot

Description

Summary of pooled results from PSAboot

Usage

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

Arguments

`object`	result of `PSAboot`.
`...`	currently unused.

Value

a list with pooled summary statistics.

a list with the results from easch PSA method. For each method a list contains the following elements:

sig.tot.per: Percentage of boostrap samples where the confidence interval does not span zero.
boostrap.mean: Weighted mean difference across all bootstrap samples.
boostrap.ci: Overall confidence interval across all bootstrap samples.
bootstrap.weighted.mean: Overall weighted bootstrap mean.
percent.sig: Contingency table of the number of bootstrap samples that don't span zero.
complete: Results of the summary of the PSA method.

Package 'PSAboot'

Help Index

Bootstrapping for Propensity Score Analysis

Description

Convert the results of PSAboot summary to a data frame.

Description

Usage

Arguments

Value

Returns a summary of the balance for all bootstrap samples.

Description

Usage

Arguments

Value

Examples

Returns balance for each covariate from propensity score matching.

Description

Usage

Arguments

Value

Stratification using classification trees for bootstrapping.

Description

Usage

Arguments

Value

Matching package implementation for bootstrapping.

Description

Usage

Arguments

Value

MatchIt package implementation for bootstrapping.

Description

Usage

Arguments

Value

Stratification using classification trees for bootstrapping.

Description

Usage

Arguments

Value

Stratification implementation for bootstrapping.

Description

Usage

Arguments

Value

Propensity score weighting implementation for bootstrapping.

Description

Usage

Arguments

Value

Boxplot of PSA bootstrap results.

Description

Usage

Arguments

Value

Boxplot of the balance statistics for bootstrapped samples.

Description

Usage

Arguments

Value

Examples

Calculates propensity score weights.

Description

Usage

Arguments

Returns a vector with the default methods used by PSAboot.

Description

Usage

Details

Value

Histogram of PSA bootstrap results

Description

Usage

Arguments

Value

Matrix Plot of Bootstrapped Propensity Score Analysis

Description

Usage

Arguments

Value

Returns a vector with the default methods used by `PSAboot`.