rankest.gpls: Choose optimal number of latent variables for gpls models

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

This evaluates the number of latent variables that leads to the best fit as judged by the AICc, AIC, or BIC metric.

Usage

rankest.gpls(
  formula,
  data,
  eps = 0.001,
  maxit = 1000,
  denom.eps = 1e-20,
  family = NULL,
  link = NULL,
  firth = FALSE,
  contrasts = NULL,
  criterion = c("AICc", "AIC", "BIC", "BICc"),
  plot = TRUE
)

Arguments

formula	model formula
data	a data frame
eps	tolerance
maxit	max iter
denom.eps	tolerance value for denominator to consider a number as zero
family	"gaussian", "poisson", "negative.binomial", "binomial", "multinom", "Gamma", "inverse.gaussian"
link	the link function. see details for available options.
firth	should shrinkage be applied? Defaults to FALSE.
contrasts	model contrasts
criterion	one of AICc, AIC, BIC, or BICc.
plot	Should the results be plotted? Defaults to TRUE.

Value

a numeric value

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.