rrda.plot: Plot the results of cross-validation for Bhat obtained from...

In rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data

Plot the results of cross-validation for Bhat obtained from the rrda.cv function.

Description

This function visualizes the results of cross-validation for the estimated Bhat matrix obtained from the rrda.cv function. It creates a plot of the Mean Squared Error (MSE) for each combination of rank and lambda regularization parameter, allowing for the selection of specific ranks and lambda ranges to be plotted. Error bars representing the standard error of the MSE can be displayed for the best rank.

Usage

rrda.plot(
  cv_result,
  nrank = NULL,
  min_l = NULL,
  max_l = NULL,
  show_error_bar = FALSE,
  title = NULL
)

Arguments

cv_result	A result list from the function rrda.cv, containing a matrix of MSE values for each rank and lambda, and a vector of lambda values.
nrank	A numeric vector specifying the ranks of Bhat to be plotted. Default is NULL, which plots all ranks.
min_l	Minimum lambda value to be plotted. Default is NULL, which uses the minimum lambda value in cv_result.
max_l	Maximum lambda value to be plotted. Default is NULL, which uses the maximum lambda value in cv_result.
show_error_bar	Logical value indicating if the error bar is shown on the line that gives the best MSE value.
title	Title of the figure

Value

A plot of MSE cross-validation results.

Examples

set.seed(10)
simdata<-rdasim1(n = 10,p = 30,q = 30,k = 3) # data generation
X <- simdata$X
Y <- simdata$Y

cv_result<- rrda.cv(Y = Y, X = X, maxrank = 5, nfold = 5) # cv
rrda.summary(cv_result = cv_result)
rrda.plot(cv_result = cv_result)

rrda documentation built on June 8, 2025, 12:09 p.m.