brainGraph_GLM_design: Create a design matrix for linear model analysis
Description
This function takes a data.table of covariates and returns a
design matrix to be used in linear model analysis.Usage
brainGraph_GLM_design(covars, coding = c("dummy", "effects", "cell.means"),
mean.center = FALSE, binarize = NULL, int = NULL)Arguments
covars
A data.table of covariates
coding
Character string indicating how factor variables will be coded
(default: 'dummy')
mean.center
Logical indicating whether to mean center non-factor
variables (default: FALSE)
binarize
Character string specifying the column name(s) of the
covariate(s) to be converted from type factor to numeric
(default: NULL)
int
Character string specifying the column name(s) of the
covariate(s) to test for an interaction (default: NULL)
Details
There are three different ways to code factors: dummy, effects,
or cell-means (chosen by the argument coding). To understand
the difference, see Chapter 7 of the User Guide. The argument mean.center allows you to mean-center any non-factor
variables (including dummy/indicator covariates). The argument
binarize will turn given factor variables into dummy/indicator
variables. The int argument specifies which variables should interact with the
Group factor variable. This argument accepts either numeric variables
(e.g., Age) and other factor variables (e.g., Sex) if you are
running a two-way ANOVA. See Chapter 7 of the User Guide for examples.