Two-fold cross-validation

Description

Functions and classes to manage outputs of two-fold cross-validation for one (class randxval) or several (class krandxval) statistics

Usage

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as.krandxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), names =
colnames(RMSEc), call = match.call())
## S3 method for class 'krandxval'
print(x, ...)
as.randxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), call =
match.call())
## S3 method for class 'randxval'
print(x, ...)

Arguments

RMSEc

a vector (class randxval) or a matrix (class krandxval) with the root-mean-square error of calibration (statistics as columns and repetions as rows)

RMSEv

a vector (class randxval) or a matrix (class krandxval) with the root-mean-square error of validation (statistics as columns and repetions as rows)

quantiles

a vector indicating the lower and upper quantiles to compute

names

a vector of names for the statistics

call

the matching call

x

an object of class randxval or krandxval

...

other arguments to be passed to methods

Value

an object of class randxval or krandxval

Author(s)

St\'ephane Dray (stephane.dray@univ-lyon1.fr)

References

Stone M. (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111-147

See Also

testdim.multiblock

Examples

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## an example corresponding to 10 statistics and 100 repetitions
cv <- as.krandxval(RMSEc = matrix(rnorm(1000), nrow = 100), RMSEv =
matrix(rnorm(1000, mean = 1), nrow = 100))
cv
if(adegraphicsLoaded())
plot(cv) 

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