psw.spec.test: Propensity score model specification test
In PSW: Propensity Score Weighting Methods for Dichotomous Treatments

Description Usage Arguments Details Value See Also Examples

psw.spec.test is used to test the sufficiency of propensity score model in balancing covariates between groups.

1	psw.spec.test(data, form.ps, weight, V.name, trans.type, K = 4)

`data`	data frame to be used.
`form.ps`	propensity score model.
`weight`	weighting method to be used. Available methods are `"ATE"`, `"ATT"`, `"ATC"`, `"MW"`, and `"TRAPEZOIDAL"`. Note that specification test is not available for `weight = "OVERLAP"`.
`V.name`	a vector of covariates on which the specification test is performed.
`trans.type`	a vector of the same length as `V.name` that specifies the transformation type for each element in `V.name`. Available transformations are `"identity"`, `"log"`, `"logit"`, `"sqrt"`, and `"Fisher"`. No transformation is perfomred with `"identity"`.
`K`	value of K in ω(e_i) = min(1, K min(e_i, 1-e_i)) for `"TRAPEZOIDAL"` weight. `K=4` by default.

In the data set, treatment indicator should be numerically specified such that a value of 1 indicates the treated and a value of 0 indicates the control. The null hypothesis is that the propensity score model is correctly specified; the alternative is that the propensity score model is misspecified. Therefore, this test is a goodness-of-fit test of propensity score model, with the test statistic being a metric of covariate balance. #' Rejection of the specification test implies current propensity score model is inadquate for balancing covariates between groups.

A list of model specification test results.

`weight`	weighting method.
`ps.model`	object returned by fitting the propensity score model using `glm` with `"binomial"` family.
`ps.hat`	estimated propensity score.
`W`	estimated propensity score weight.
`V.name`	covariates in the specification test.
`g.B1.hat`	a vector of transformed weighted average for covariates in the treated group when `spec.test=T`.
`g.B0.hat`	a vector of transformed weighted average for covariates in the control group when `spec.test=T`.
`B.hat`	difference between `eta.B1.hat` and `eta.B0.hat` when `spec.test=T`.
`var.B.hat`	covariance matrix for `B.hat` when `spec.test=T`.
`test.stat`	test statistic for the specification test, which follows the χ^2_{df} distribution under the null, available when `spec.test=T`.
`df`	degree of freedom for the specification test, `df=rank(var.B.hat)`, available when `spec.test=T`.
`pvalue`	pvalue of the specification test when `spec.test=T`.

psw, psw.balance

# Load the test data set
data(test_data);
# Propensity score model
form.ps <- "Z ~ X1 + X2 + X3 + X4";
# A vector of covariates
V.name <- c( "X1", "X2", "X3", "X4" );
# A vector of transformation types for covariates in V.name.
trans.type <- c( "identity", "identity", "logit", "logit" );
tmp <- psw.spec.test( data = test_data, form.ps = form.ps,
weight = "MW", V.name = V.name, trans.type = trans.type );