Estimates propensity scores for three groups

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 [stats::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

if (FALSE) { # \dontrun{
data(tutoring)
formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age +
     Employment + Income + Transfer + GPA
tpsa <- trips(tutoring, tutoring$treat, formu)
head(tpsa)
} # }