Two-fold cross-validation
randxval.Rd
Functions and classes to manage outputs of two-fold
cross-validation for one (class randxval
) or several (class
krandxval
) statistics
Usage
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 (classkrandxval
) with the root-mean-square error of calibration (statistics as columns and repetions as rows)- RMSEv
a vector (class
randxval
) or a matrix (classkrandxval
) 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
orkrandxval
- ...
other arguments to be passed to methods
References
Stone M. (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111-147
Author
Stéphane Dray (stephane.dray@univ-lyon1.fr)
Examples
## 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
#> Two-fold cross-validation
#> Call: as.krandxval(RMSEc = matrix(rnorm(1000), nrow = 100), RMSEv = matrix(rnorm(1000,
#> mean = 1), nrow = 100))
#>
#> Results for 10 statistics
#>
#> Root mean square error of calibration:
#> N.rep Mean 25% 75%
#> 1 100 -0.02696284 -0.7361177 0.5888360
#> 2 100 0.09032550 -0.5002461 0.5452448
#> 3 100 0.17082273 -0.4494000 0.7430513
#> 4 100 -0.01924157 -0.7276047 0.6248826
#> 5 100 -0.07635087 -0.6092265 0.5419279
#> 6 100 0.02084293 -0.5889042 0.7812515
#> 7 100 0.06222205 -0.5889446 0.9141202
#> 8 100 0.01643932 -0.5888750 0.6724972
#> 9 100 -0.08409289 -0.9066959 0.6649879
#> 10 100 0.08044706 -0.5313701 0.5827132
#>
#> Root mean square error of validation:
#> N.rep Mean 25% 75%
#> 1 100 0.8577505 0.2094997 1.513781
#> 2 100 1.0524984 0.3188293 1.784864
#> 3 100 1.0712937 0.3178318 1.874005
#> 4 100 1.1070485 0.5393782 1.648372
#> 5 100 1.1066287 0.2792434 1.876980
#> 6 100 1.0255428 0.3284189 1.610546
#> 7 100 1.0064095 0.3955722 1.700900
#> 8 100 1.0392005 0.2898142 1.629777
#> 9 100 0.9962083 0.1280009 1.767080
#> 10 100 1.0346484 0.3224993 1.738972
if(adegraphicsLoaded())
plot(cv)