unmarked: crossVal – R documentation

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crossVal

Cross-validation methods for fitted unmarked models and fit lists

Description

Test 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).

Usage

## S4 method for signature 'unmarkedFit'
crossVal(
  object, method=c("Kfold","holdout","leaveOneOut"),
  folds=10, holdoutPct=0.25, statistic=RMSE_MAE, parallel=FALSE, ...)
## S4 method for signature 'unmarkedFitList'
crossVal(
  object, method=c("Kfold","holdout","leaveOneOut"),
  folds=10, holdoutPct=0.25, statistic=RMSE_MAE, parallel=FALSE, 
  sort = c("none", "increasing", "decreasing"), ...)

Arguments

`object`	A fitted model inheriting class `unmarkedFit` or a list of fitted models with class `unmarkedFitList`
`method`	Cross validation method to use as string. Valid options are `"Kfold"`, `"holdout"`, or `"leaveOneOut"`
`folds`	Number of folds to use for k-fold cross validation
`holdoutPct`	Proportion of dataset (value between 0-1) to use as the "holdout" or "test" set, for the holdout method
`statistic`	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.
`parallel`	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.
`sort`	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.

Value

unmarkedCrossVal or unmarkedCrossValList object containing calculated statistic values for each fold.

Author(s)

Ken Kellner contact@kenkellner.com

Examples

## Not run: 
#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'))


## End(Not run)

unmarked

Models for Data from Unmarked Animals

v1.1.0

GPL (>= 3)

Authors

Richard Chandler [aut], Ken Kellner [aut], Ian Fiske [aut], David Miller [aut], Andy Royle [cre, aut], Jeff Hostetler [aut], Rebecca Hutchinson [aut], Adam Smith [aut], Marc Kery [ctb], Mike Meredith [ctb], Auriel Fournier [ctb], Ariel Muldoon [ctb], Chris Baker [ctb]

Initial release

2021-05-05