build_formula: Formula Builder

In Sage-Bionetworks/sageseqr: Identify differentially expressed genes counts data

Formula Builder

Description

If a linear mixed model is used, all categorical variables must be modeled as a random effect. This function identifies the variable class to scale numeric variables and model categorical variables as a random effect. Additionally, variables are scaled to account for multiple variables that might have an order of magnitude difference.

Usage

build_formula(
  md,
  primary_variable,
  model_variables = NULL,
  is_num = NULL,
  num_var = NULL,
  random_effect = NULL,
  exclude_variables = NULL,
  add_model = NULL
)

Arguments

md	A data frame with sample identifiers in a column and relevant experimental covariates.
primary_variable	Vector of variables that will be collapsed into a single fixed effect interaction term.
model_variables	Optional. Vector of variables to include in the linear (mixed) model. If not supplied, the model will include all variables in md.
is_num	Is there a numerical covariate to use as an interaction with the primary variable(s). default= NULL
num_var	A numerical metadata column to use in an inaction with the primary variable(s). default= NULL
random_effect	A vector of variables to consider as random effects instead of fixed effects.
exclude_variables	Vector of variables to exclude from testing.
add_model	Optional. User Speciffied variables to add to the null model apriori to model generation. (Default = NULL)

Sage-Bionetworks/sageseqr documentation built on June 13, 2024, 2:11 p.m.