cv.trans.psa: Transformation of Factors to Individual Levels

In PSAgraphics: Propensity Score Analysis Graphics

Transformation of Factors to Individual Levels

Description

The function cv.trans.psa takes a covariate data frame and replaces each categorical covariate of n >=3 levels with n new binary covariate columns, one for each level. Transforms covariate dataframe for use with the function cv.bal.psa.

Usage

cv.trans.psa(covariates, fcol = NULL)

Arguments

covariates	A dataframe of covariates, presumably some factors.
fcol	An optional vector containing the factor columns in the covariate dataframe. In NULL (default) routine to identfy factors internally.

Value

Returns a dataframe covariates.transformed containing new columns for each level of more than binary factors. The rest of the covariate dataframe stays unchanged.

Author(s)

James E. Helmreich [e-mail]

Robert M. Pruzek [e-mail]

KuangNan Xiong [e-mail]

See Also

cv.bal.psa, loess.psa, cstrata.psa, cv.trans.psa

Examples

#Note reordering of columns, binary factor and numeric column are unchanged.
f2 <- factor(sample(c(0, 1), 20, replace = TRUE))
f4 <- factor(sample(c("a", "b", "c", "d"), 20, replace = TRUE))
cv <- rnorm(20)
X <- data.frame(f2, f4, cv)
cv.trans.psa(X)
#
f2 <- factor(sample(c('c', 'C'), 20, replace = TRUE))
f4 <- factor(sample(c("b", "A", "d", "CC"), 20, replace = TRUE))
cv <- rnorm(20)
X <- data.frame(f2, f4, cv)
cv.trans.psa(X)

PSAgraphics documentation built on March 31, 2023, 5:30 p.m.