[-causalModel {causalGel} | R Documentation |
Different subsetting methods for S4 class objects of the package. The subset method returns an new object with observations selected by the second argument. See example.
signature(x = "causalModel", i = "missing", j = "numericORlogical")
Subsets observations.
signature(x = "causalModel", i = "numeric", j = "missing")
Selects balancing covatriates.
signature(x = "causalModel", i = "numeric", j = "numericORlogical")
Selects balancing covariates and observations.
signature(x = "rcausalModel", i = "missing", j = "numericORlogical")
Subsets observations for restricted models.
signature(x = "rcausalModel", i = "numeric", j = "missing")
Selects balancing covatriates for restricted models.
signature(x = "rcausalModel", i = "numeric", j = "numericORlogical")
Selects balancing covariates and observations for restricted models.
signature(x = "causalGelfit", i = "missing", j = "numericORlogical")
Subsets observations and refit the model.
signature(x = "causalGelfit", i = "numeric", j = "missing")
Selects balancing covatriates and refit the model.
signature(x = "causalGelfit", i = "numeric", j = "numericORlogical")
Selects balancing covariates and observations and refit the model.
data(nsw) balm <- ~age+ed+black+hisp:married+nodeg+re75+I(re75^2) g <- re78~treat model <- causalModel(g, balm, nsw) model[1:5, 1:500] fit <- gelFit(model, gelType="EL") fit[1:5,1:500]