plot.cvsvd: Plot the Result of an SVD Cross-Validation

In bcv: Cross-Validation for the SVD (Bi-Cross-Validation)

Plot the Result of an SVD Cross-Validation

Description

Plot the result of cv.svd.gabriel or cv.svd.wold, optionally with error bars.

Usage

## S3 method for class 'cvsvd'
plot(
  x,
  errorbars = TRUE,
  add = FALSE,
  xlab = "Rank",
  ylab = "Mean Sq. Prediction Error",
  col = "blue",
  col.errorbars = "gray50",
  ...
)

Arguments

x	the result of a cv.svd.gabriel or link{cv.svd.wold} computation.
errorbars	indicates whether or not to add error bars.
add	indicates whether or not to add to the current plot.
xlab	the label for the x axis.
ylab	the label for the y axis.
col	the color to use for showing prediction error.
col.errorbars	the color to use for the error bars.
...	additional arguments for plot.

Details

Plot the result of cv.svd.gabriel or cv.svd.wold. This plots a the estimated prediction error as a function of rank, optionally with error bars.

If add is TRUE, the current plot is not cleared.

Author(s)

Patrick O. Perry

See Also

cv.svd.gabriel, cv.svd.wold, print.cvsvd summary.cvsvd

Examples

  # generate a rank-2 matrix plus noise
  n <- 50; p <- 20; k <- 2
  u <- matrix( rnorm( n*k ), n, k )
  v <- matrix( rnorm( p*k ), p, k )
  e <- matrix( rnorm( n*p ), n, p )
  x <- u %*% t(v) + e
  
  # perform 5-fold Wold-style cross-validtion
  cvw <- cv.svd.wold( x, 5, maxrank=10 )
  
  # perform (2,2)-fold Gabriel-style cross-validation
  cvg <- cv.svd.gabriel( x, 2, 2, maxrank=10 )
  
  # plot the results
  par( mfrow=c(2,1) )
  plot( cvw, main="Wold-style CV")
  plot( cvg, main="Gabriel-style CV")

bcv documentation built on May 31, 2023, 5:32 p.m.