smvcir: Main SMVCIR function

In danno11/SMVCIR: Sliced Mean Variance Covariance Inverse Regression

Description

Build a Sliced Mean Variance Covariance Inverse Regression model.

Usage

smvcir(group, data, pdt = 100, level = 0.05, test = FALSE,
  scree_plot = FALSE)

Arguments

group	A character string specifying the name of the class variable of interest in your dataset.
data	A data frame (including your group variable).
pdt	Percentage of SMVCIR discrimination desired from dimension
level	Level of dimensionality test
test	Types of tests, can use either, neither, or both.
scree_plot	If TRUE, a scree plot of cumulative percentage of variation explained by discriminant dimensions is produced.
empsimss	Number of draws to use in performing each dimensionality test.

Examples

library(caret)
train<-createDataPartition(pima$diabetes, p = .8, list = FALSE)
pim.smv<-smvcir("diabetes", data = pima[train,], test = T) ###Build smvcir model on training set
summary(pim.smv)

danno11/SMVCIR documentation built on May 14, 2019, 6:06 p.m.