Methods_SSI_CV: SSI_CV methods

In MarcooLopez/SFSI_data: Sparse Family and Selection Index

Description

• 'summary': Returns accuracy, MSE, df, achieved by each value of the vector lambda. These values are averages across folds and partitions. Also returns summary for the SSI with maximum accuracy and with minimum MSE.

• 'plot': Creates a plot of either accuracy or MSE versus the (average) number of predictors and versus lambda (in negative logarithm) averaged across folds and partitions.

Usage

## S3 method for class 'SSI_CV'
summary(object, ...)

## S3 method for class 'SSI_CV'
plot(..., py=c("accuracy","MSE"), title=NULL, showFolds=FALSE)

Arguments

object	An object of the class 'SSI_CV'
...	Arguments to be passed: Not needed for summary function object: One or more objects of the class 'SSI_CV' (for function plot)
py	Indicates whether to plot correlation (between observed and predicted) or MSE in y-axis
title	Title of the plot
showFolds	TRUE or FALSE to whether add results for individuals folds

Author(s)

Marco Lopez-Cruz ([e-mail]) and Gustavo de los Campos

Examples

  require(SFSI)
  data(wheatHTP)
  X = scale(X[1:300,])        # Subset and scale markers
  G = tcrossprod(X)/ncol(X)   # Genomic relationship matrix
  y = scale(Y[1:300,"YLD"])   # Subset response variable
 
  fm1 = SSI_CV(y,K=G,trn.CV=101:length(y),nFolds=10,nCV=1)
  
  yHat = fitted(fm1)              # Predicted values for each SSI
  corTST = summary(fm1)$accuracy  # Testing set accuracy (cor(y,yHat))
  summary(fm1)$optCOR             # SSI with maximum accuracy
  summary(fm1)$optMSE             # SSI with minimum MSE
  plot(fm1,title=expression('corr('*y[obs]*','*y[pred]*') vs sparsity'))   
  plot(fm1,py="MSE",title='Mean Square Error vs sparsity')     
  plot(fm1,showFolds=TRUE)     

MarcooLopez/SFSI_data documentation built on April 15, 2021, 10:53 a.m.