epiR (version 0.9-82)

epi.cp: Extract unique covariate patterns from a data set

Description

Extract the set of unique patterns from a set of covariates.

Usage

epi.cp(dat)

Arguments

dat

an i row by j column data frame where the i rows represent individual observations and the m columns represent covariates.

Value

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.

Details

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.

References

Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada.

Examples

## 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 <- data.frame(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.