Plot the Result of an SVD Cross-Validation

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

# S3 method for 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.

See also

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

Author

Patrick O. Perry

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")