trips: Estimates propensity scores for three groups

In jbryer/TriMatch: Propensity Score Matching of Non-Binary Treatments

Description

The propensity score is

e(X)=P({ W }=1|X)

This function will estimate the propensity scores for each pair of groups (e.g. two treatments and one control).

Usage

trips(thedata, treat, formu = ~., groups = unique(treat), nstrata = 5,
  method = "logistic", ...)

Arguments

thedata	the data frame.
treat	vector or factor indicating the treatment/control assignment for thedata. Length must be equal to nrow(thedata).
formu	the logistic regression formula. Note that the dependent variable should not be specified and will be modified.
groups	a vector of exactly length three corresponding the values in treat for each control/treatment.
nstrata	the number of strata marks to plot on the edge.
method	the method to use to estimate the propensity scores. Current options are logistic or randomForest.
...	other parameters passed to glm.

Details

{ PS }_{ 1 }=e({ X }_{ { T }_{ 1 }C })=Pr(z=1|{ X }_{ { T }_{ 1 }C })

{ PS }_{ 2 }=e({ X }_{ { T }_{ 2 }C })=Pr(z=1|{ X }_{ { T }_{ 2 }C })

{ PS }_{ 3 }=e({ X }_{ { T }_{ 2 }{ T }_{ 1 } })=Pr(z=1|{ X }_{ { T }_{ 2 }{ T }_{ 1 } })

Examples

## Not run: 
data(tutoring)
formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age +
     Employment + Income + Transfer + GPA
tpsa <- trips(tutoring, tutoring$treat, formu)
head(tpsa)

## End(Not run)

jbryer/TriMatch documentation built on May 18, 2019, 7:01 p.m.