massGAM: Mass-univariate GAM Analysis

In egenn/rtemis: Machine Learning and Visualization

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

Details

NA in the input will be kept as NA in the results, maintaining n of cases.

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.