epi.cp {epiR} | R Documentation |
Extract the set of unique patterns from a set of covariates (explanatory variables).
epi.cp(dat)
dat |
an i row by j column data frame where the i rows represent individual observations and the m columns represent a set of m covariates. The function allows for one or more covariates for each observation. |
This function extracts the k unique covariate patterns in a data set comprised of i observations, labelling them from 1 to k. The frequency of occurrence of each covariate pattern is listed. A vector of length i is also returned, listing the 1:k covariate pattern identifier for each observation.
A list containing the following:
cov.pattern |
a data frame with columns: |
id |
a vector of length i listing the 1:k covariate pattern identifier for each observation. |
Thanks to Johann Popp and Mathew Jay for providing code and suggestions to enhance the utility of this function.
Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada.
## EXAMPLE 1: ## 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.df01 <- data.frame(obs, v1, v2) dat.glm01 <- glm(obs ~ v1 + v2, family = binomial, data = dat.df01) dat.mf01 <- model.frame(dat.glm01) ## Covariate pattern. Drop the first column of dat.mf01 (since column 1 is the ## outcome variable: epi.cp(dat.mf01[,2:3]) ## There are 25 covariate patterns in this data set. Subject 100 has ## covariate pattern 21.