plot.cvsvd {bcv} R Documentation

## 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,
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

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")



[Package bcv version 1.0.2 Index]