gendata_MLR: Generate Simulated Data with Multinomial Logistic Regression...

In longhaiSK/HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors

Generate Simulated Data with Multinomial Logistic Regression Model

Description

This function generates the response variables y given optional supplied X using a multinomial logistic regression model.

Usage

gendata_MLR(n, p, NC = 3, nu = 2, w = 1, X = NULL, betas = NULL)

Arguments

n	Number of observations.
p	Number of features.
NC	Number of classes for response variables.
nu, w	If betas is not supplied (default), the regression coefficients are generated with t prior with df = nu, scale = sqrt(w); will be ignored if betas is supplied.
X	The design matrix; will be generated from standard normal distribution if not supplied.
betas	User supplied regression coefficients.

Value

A list contains input matrix X, response variables y, and regression coefficients deltas.

See Also

gendata_FAM

Examples

set.seed(12345)
dat <- gendata_MLR(n = 100, p = 10)
ggplot2::qplot(dat$y, bins = 6)
corrplot::corrplot(cor(dat$X))

longhaiSK/HTLR documentation built on Oct. 24, 2022, 5:33 p.m.