CV: Bayesian CV

In yanbowisc/SIMP: Bayesian Simultaneous Partial Envelope Model

Bayesian CV

Description

Bayesian CV

Usage

CV(SIMP.fit.full, Y, X1C, X1D, X2, dx, dy, K_CV, n.iter, correction = FALSE)

Arguments

SIMP.fit.full	The output of SIMP() fitted on the full dataset. This input is used as correction, and the posterior samples from all chains will be combined.
Y	Response matrix. Must have the same number of rows as X.
X1C	Design matrix of the continuous part of the predictors of interest. Must have the same number of rows as Y.
X1D	Design matrix of the discrete part of the predictors of interest. Must have the same number of rows as Y.
X2	Design matrix of the nuisance predictors. Must have the same number of rows as Y.
dx	Partial predictor envelope dimension. Must be an integer between 0 and ncol(X1C).
dy	Partial response envelope dimension. Must be an integer between 0 and ncol(Y).
K_CV	Fold for CV
n.iter	Number of Markov chain iterations to run in each chains, for the fitting in CV. *Includes burn-in*.
correction	Whether correction should be applied.

yanbowisc/SIMP documentation built on Oct. 30, 2022, 1:33 a.m.