massGAM: Mass-univariate GAM Analysis
In egenn/rtemis: Machine Learning and Visualization

massGAM

R Documentation

Mass-univariate GAM Analysis

Description

Fits a GAM for each of multiple outcomes using a fixed set of features (many y's, one X).

Usage

massGAM(
  x,
  y,
  covariates = NULL,
  x.name = NULL,
  y.name = NULL,
  k = NULL,
  family = gaussian(),
  weights = NULL,
  method = "REML",
  n.cores = rtCores,
  save.mods = FALSE,
  save.summary = TRUE,
  print.plots = FALSE,
  outdir = NULL,
  save.plots = FALSE,
  new.x.breaks = 9
)

Arguments

`x`	Numeric vector or matrix / data frame of features i.e. independent variables
`y`	Numeric matrix / data frame: Outcomes
`covariates`	Numeric matrix / data.frame of additional covariates
`x.name`	Character: Name of the predictor
`y.name`	Character, vector: Names of the outcomes
`k`	Integer: Basis dimension for smoothing spline
`family`	`family` argument for `mgcv::gam`
`weights`	Vector, numeric: Weights for GAM
`method`	Estimation method for GAM
`n.cores`	Integer. Number of cores to use
`save.mods`	Logical. Should models be saved
`save.summary`	Logical. Should model summary be saved
`print.plots`	Logical Should plots be shown
`outdir`	Path to save output
`save.plots`	Logical. Should plots be saved
`new.x.breaks`	Integer. Number of splits in the range of x to form vector of features for estimation of fitted values