brainGraph_GLM_design takes a data.table of covariates and
returns a design matrix to be used in linear model analysis.
brainGraph_GLM_design(covars, coding = c("dummy", "effects",
"cell.means"), factorize = TRUE, mean.center = FALSE,
binarize = NULL, int = NULL)A data.table of covariates
Character string indicating how factor variables will be coded
(default: 'dummy')
Logical indicating whether to convert character
columns into factor (default: TRUE)
Logical indicating whether to mean center non-factor
variables (default: FALSE)
Character vector specifying the column name(s) of the
covariate(s) to be converted from type factor to numeric
(default: NULL)
Character vector specifying the column name(s) of the
covariate(s) to test for an interaction (default: NULL)
A numeric matrix
There are three different ways to code factors: dummy, effects,
or cell-means (chosen by the argument coding). To understand
the difference, see Chapter 8 of the User Guide.
Importantly, the default behavior (as of v2.1.0) is to convert all character
columns (excluding the Study ID column and any that you list in the
binarize argument) to factor variables. To change this, set
factorize=FALSE. So, if your covariates include multiple character
columns, but you want to convert Scanner to binary instead of a
factor, you may still specify binarize='Scanner' and get the expected
result. binarize will convert the given
factor variable(s) into numeric variable(s), which is performed before
mean-centering.
The argument mean.center will mean-center (i.e., subtract the mean of
the entire dataset from each variable) any non-factor variables (including
any dummy/indicator covariates). This is done after "factorizing" and
"binarizing".
int specifies which variables should interact with one another. This
argument accepts both numeric (e.g., Age) and factor variables (e.g.,
Sex). All interaction combinations will be generated: if you supply 3
variables, all two-way and the single three-way interaction will be
generated. This variable must have at least two elements.