brainGraph_GLM_design: Create a design matrix for linear model analysis

Description Usage Arguments Details Value Author(s) See Also

View source: R/brainGraph_GLM.R

Description

brainGraph_GLM_design takes a data.table of covariates and returns a design matrix to be used in linear model analysis.

Usage

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brainGraph_GLM_design(covars, coding = c("dummy", "effects", "cell.means"),
  factorize = TRUE, 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')

factorize

Logical indicating whether to convert character columns into factor (default: TRUE)

mean.center

Logical indicating whether to mean center non-factor variables (default: FALSE)

binarize

Character vector specifying the column name(s) of the covariate(s) to be converted from type factor to numeric (default: NULL)

int

Character vector 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 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.

Value

A numeric matrix

Author(s)

Christopher G. Watson, [email protected]

See Also

Other GLM functions: GLMfit, brainGraph_GLM, mtpc


brainGraph documentation built on May 29, 2018, 9:03 a.m.