epi.cp {epiR} | R Documentation |
Extract the set of unique patterns from a set of covariates.
epi.cp(dat)
dat |
an i row by j column data frame where the i rows represent individual observations and the m columns represent covariates. |
A covariate pattern is a unique combination of values of predictor variables. For example, if a model contains two dichotomous predictors, there will be four covariate patterns possible: (1,1)
, (1,0)
, (0,1)
, and (0,0)
. This function extracts the n unique covariate patterns from a data set comprised of i observations, labelling them from 1 to n. A vector of length m is also returned, listing the covariate pattern identifier for each observation.
A list containing the following:
cov.pattern |
a data frame with columns: id the unique covariate patterns, n the number of occasions each of the listed covariate pattern appears in the data, and the unique covariate combinations. |
id |
a vector listing the covariate pattern identifier for each observation. |
Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada.
## Generate a set of covariates: set.seed(seed = 1234) obs <- round(runif(n = 100, min = 0, max = 1), digits = 0) v1 <- round(runif(n = 100, min = 0, max = 4), digits = 0) v2 <- round(runif(n = 100, min = 0, max = 4), digits = 0) dat <- as.data.frame(cbind(obs, v1, v2)) dat.glm <- glm(obs ~ v1 + v2, family = binomial, data = dat) dat.mf <- model.frame(dat.glm) ## Covariate pattern: epi.cp(dat.mf[-1]) ## There are 25 covariate patterns in this data set. Subject 100 has ## covariate pattern 21.