design: Create full and reduced design matrices for the cpg.assoc function.

Description

Designed to be used by cpg.assoc and cpg.perm. Creates a full and reduced design matrices.

Usage

design(covariates, indep, chip.id, random)

Value

Returns a list containing the full and reduced design matrices.

full: The full design matrix.
reduced: The reduced design matrix.

Arguments

covariates: A data frame consisting of the covariates of interest. covariates can also be a matrix if it is a model matrix minus the intercept column. It can also be a vector if there is only one covariate of interest. If no covariates must be specified as NULL.
indep: A vector containing the main variable of interest. cpg.assoc will evaluate the association between indep and the beta values.
chip.id: An optional vector containing chip or batch identities. If specified, chip.id will be included as a factor in the model.
random: Is the model going to be a mixed effects. If so, chip.id will not be included in the design matrices.

Author

Barfield, R.; Kilaru,V.; Conneely, K.
Maintainer: R. Barfield: <barfieldrichard8@gmail.com>

Examples

Run this code

data(samplecpg,samplepheno,package="CpGassoc")
#Example where there are covariates:
covar<-data.frame(samplepheno$weight,samplepheno$Distance)
test<-design(covar,samplepheno$SBP,samplepheno$chip,FALSE)
dim(test$full)
dim(test$reduced)
test$reduced[1:5,1:5]
test$full[1:5,1:5]
#When no covariates or chip.id:
test2<-design(NULL,samplepheno$SBP,NULL,FALSE)
dim(test2$full)
dim(test2$reduced)