crossVal.RdTest predictive accuracy of fitted models using several cross-validation approaches. The dataset is divided by site only into folds or testing and training datasets (i.e., encounter histories within sites are never split up).
# S4 method for class 'unmarkedFit'
crossVal(
object, method=c("Kfold","holdout","leaveOneOut"),
folds=10, holdoutPct=0.25, statistic=RMSE_MAE, parallel=FALSE, ncores, ...)
# S4 method for class 'unmarkedFitList'
crossVal(
object, method=c("Kfold","holdout","leaveOneOut"),
folds=10, holdoutPct=0.25, statistic=RMSE_MAE, parallel=FALSE, ncores,
sort = c("none", "increasing", "decreasing"), ...)A fitted model inheriting class unmarkedFit or a list of
fitted models with class unmarkedFitList
Cross validation method to use as string. Valid options are
"Kfold", "holdout", or "leaveOneOut"
Number of folds to use for k-fold cross validation
Proportion of dataset (value between 0-1) to use as the "holdout" or "test" set, for the holdout method
Function that calculates statistics for each fold. The
function must take an unmarkedFit object as the first argument and
return a named numeric vector with statistic value(s). The default function
RMSE_MAE returns root-mean-square error and mean absolute error.
See unmarked:::RMSE_MAE for an example of correct statistic
function structure.
If TRUE, run folds in parallel. This may speed up
cross-validation if the unmarked model takes a long time to fit or you have
a large number of sites and are using leave-one-out cross-validation.
Number of parallel cores to use.
If doing cross-validation on a fitList, you can optionally
sort the resulting table(s) of statistic values for each model.
Other arguments passed to the statistic function.
unmarkedCrossVal or unmarkedCrossValList object containing
calculated statistic values for each fold.
if (FALSE) { # \dontrun{
#Get data
data(frogs)
pferUMF <- unmarkedFrameOccu(pfer.bin)
siteCovs(pferUMF) <- data.frame(sitevar1 = rnorm(numSites(pferUMF)))
obsCovs(pferUMF) <- data.frame(obsvar1 = rnorm(numSites(pferUMF) * obsNum(pferUMF)))
#Fit occupancy model
fm <- occu(~ obsvar1 ~ 1, pferUMF)
#k-fold cross validation with 10 folds
(kfold = crossVal(fm, method="Kfold", folds=10))
#holdout method with 25
(holdout = crossVal(fm,method='holdout', holdoutPct=0.25))
#Leave-one-out method
(leave = crossVal(fm, method='leaveOneOut'))
#Fit a second model and combine into a fitList
fm2 <- occu(~1 ~1, pferUMF)
fl <- fitList(fm2,fm)
#Cross-validation for all fits in fitList using holdout method
(cvlist <- crossVal(fl, method='holdout'))
} # }