# brainGraph_GLM_design: Create a design matrix for linear model analysis In brainGraph: Graph Theory Analysis of Brain MRI Data

## Description

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

## Usage

 ```1 2``` ```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.

A numeric matrix

## Author(s)

Christopher G. Watson, [email protected]

Other GLM functions: `GLMfit`, `brainGraph_GLM`, `mtpc`