| PS.MA {madr} | R Documentation |
Calculate model probabilities for the propensity score model using a pseudo-MC3 algorithm
Description
This function uses a pseudo-MC3 algorithm to search the propensity score model space.
Usage
PS.MA(X, U, W = NULL, M = 1000, alpha = NULL, master.index = NULL,
master.dict = list())
Arguments
X |
vector of the treatment (0/1) |
U |
matrix of covariates to be considered for inclusion/exclusion |
W |
matrix of covariates that will be included in all models (optional) |
M |
the number of MCMC iteration |
alpha |
vector of inclusion indicators (which columns of U) to start MCMC algorithm (optional) |
master.index |
indexes which columns of U should be considered for inclusion in the propensity score model (optional) |
master.dict |
list containing information from previous propensity score model fits (optional) |
Value
A list. The list contains the following named components:
dict |
a list that contains the BIC and estimated propensity scores from propensity score models |
alpha |
the last model visited by the algorithm |
out.table |
a matrix that contains the BIC from each propensity score model |