design {CpGassoc} | R Documentation |
Designed to be used by cpg.assoc
and cpg.perm
. Creates a full and reduced design matrices.
design(covariates, indep, chip.id, random)
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 |
indep |
A vector containing the main variable of interest. |
chip.id |
An optional vector containing chip or batch identities. If specified, |
random |
Is the model going to be a mixed effects. If so, |
Returns a list containing the full and reduced design matrices.
full |
The full design matrix. |
reduced |
The reduced design matrix. |
The design
function is designed to be used exclusively by the cpg.assoc
and cpg.perm
functions.
Barfield, R.; Kilaru,V.; Conneely, K.
Maintainer: R. Barfield: <rbarfield01@fas.harvard.edu>
cpg.assoc
cpg.perm
cpg.work
plot.cpg
scatterplot
cpg.combine
manhattan
plot.cpg.perm
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)