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setGeneric("crossVal", function(object,
method=c("Kfold","holdout","leaveOneOut"), folds=10, holdoutPct=0.25,
statistic=RMSE_MAE, ...) standardGeneric("crossVal"))
setClass("unmarkedCrossVal",
representation(stats = "data.frame",
summary = "data.frame",
method = "character",
folds = "numeric",
holdoutPct = "numeric"),
validity=function(object){
errors <- character(0)
hp <- object@holdoutPct
if(hp<0|hp>1){
errors <- c(errors,"holdoutPct must be between 0 and 1")
}
}
)
#Constructor of crossVal objects
setMethod("crossVal", "unmarkedFit",
function(object, method=c("Kfold","holdout","leaveOneOut"),
folds=10, holdoutPct=0.25,
statistic=RMSE_MAE, parallel=FALSE, ncores, ...){
method <- match.arg(method, c('Kfold','holdout','leaveOneOut'))
if(method=="Kfold" & !is.integer(folds) & folds < 0){
stop("folds must be a positive integer")
}
if(method=="holdout" & (holdoutPct>1 | holdoutPct<0)){
stop("holdoutPct must be a proportion between 0 and 1")
}
partitions <- switch(method,
Kfold = partitionKfold(object, folds=folds),
holdout = partitionHoldout(object, holdoutPct=holdoutPct),
leaveOneOut = partitionLeaveOneOut(object)
)
n_reps <- length(partitions)
check_stat <- statistic(object, ...)
if(!is.numeric(check_stat)||is.null(names(check_stat))){
stop("Function provided to statistic argument must return a named numeric vector")
}
do_crossval <- function(i, object, partitions, statistic, ...){
newfit <- unmarked::update(object, data=partitions[[i]]$trainData)
newfit@data <- partitions[[i]]$testData
if(!is.null(attributes(newfit)$knownOcc)){
newfit@knownOcc <- rep(FALSE,numSites(newfit@data))
}
statistic(newfit, ...)
}
if(parallel){
if(missing(ncores)) ncores <- parallel::detectCores()-1
cl <- parallel::makeCluster(ncores)
on.exit(parallel::stopCluster(cl))
stat_raw <- pblapply(1:n_reps, do_crossval, object,
partitions, statistic, ..., cl = cl)
} else {
stat_raw <- pblapply(1:n_reps, do_crossval, object,
partitions, statistic, ...)
}
stats <- as.data.frame(do.call("rbind", stat_raw))
summary <- data.frame(Estimate=sapply(stats, mean, na.rm=TRUE),
SD=sapply(stats, sd, na.rm=TRUE))
out <- new("unmarkedCrossVal", stats=stats, summary=summary, method=method,
folds=folds, holdoutPct=holdoutPct)
out
})
#Kfold partition function
partitionKfold <- function(object, folds){
site_inds <- 1:numSites(object@data)
shuf_site_inds <- sample(site_inds,numSites(object@data))
fold_inds <- cut(site_inds, breaks=folds, labels=FALSE)
fold_list <- vector(length=folds,"list")
for (i in 1:folds){
trainInds <- shuf_site_inds[fold_inds!=i]
testInds <- shuf_site_inds[fold_inds==i]
fold_list[[i]]$trainData <- object@data[trainInds,]
fold_list[[i]]$testData <- object@data[testInds,]
}
fold_list
}
#Holdout partition function
partitionHoldout <- function(object, holdoutPct){
site_inds <- 1:numSites(object@data)
shuf_site_inds <- sample(site_inds,numSites(object@data))
splitInd <- round(numSites(object@data)*(1-holdoutPct))
trainInds <- shuf_site_inds[1:splitInd]
testInds <- shuf_site_inds[(splitInd+1):length(shuf_site_inds)]
fold_list <- vector(length=1,"list")
fold_list[[1]]$trainData <- object@data[trainInds,]
fold_list[[1]]$testData <- object@data[testInds,]
fold_list
}
#leave-one-out
partitionLeaveOneOut <- function(object){
fold_list <- vector(length=numSites(object@data),"list")
for (i in seq_along(fold_list)){
fold_list[[i]]$trainData <- object@data[-i,]
fold_list[[i]]$testData <- object@data[i,]
}
fold_list
}
setMethod("show", "unmarkedCrossVal", function(object)
{
st <- object@stats
if(object@method=='Kfold'){
cat(paste('Method: k-fold (',object@folds,' folds)\n\n',sep=''))
} else if(object@method=='holdout'){
cat(paste('Method: holdout (',round(object@holdoutPct*100),
'% in test set)\n\n',sep=''))
} else if(object@method=='leaveOneOut'){
cat('Method: leave-one-out\n\n')
}
for (i in 1:length(st)){
cat(paste0(names(st)[i],':\n'))
print(data.frame(object@summary[i,]), row.names=FALSE, digits=4)
if(i != length(st)) cat('\n')
}
})
setClass("unmarkedCrossValList",
representation(stats_list="list",
method = "character",
folds="numeric",
holdoutPct="numeric",
sort="character")
)
#CrossVal list constructor
setMethod("crossVal", "unmarkedFitList",
function(object, method=c("Kfold","holdout","leaveOneOut"),
folds=10, holdoutPct=0.25,
statistic=RMSE_MAE, parallel=FALSE, ncores,
sort = c("none", "increasing", "decreasing"), ...){
method <- match.arg(method, c('Kfold','holdout','leaveOneOut'))
sort <- match.arg(sort, c('none','increasing','decreasing'))
if(missing(ncores)) ncores <- parallel::detectCores()-1
stats <- lapply(object@fits, crossVal, method, folds,
holdoutPct, statistic, parallel, ncores, ...)
out <- new("unmarkedCrossValList", stats_list=stats, method=method,
folds=folds, holdoutPct=holdoutPct, sort=sort)
})
setMethod("show", "unmarkedCrossValList", function(object){
sl <- object@stats_list
mod_names <- names(sl)
nfits <- length(sl)
nstats <- length(sl[[1]]@stats)
stat_names <- names(sl[[1]]@stats)
if(object@method=='Kfold'){
cat(paste('Method: k-fold (',object@folds,' folds)\n\n',sep=''))
} else if(object@method=='holdout'){
cat(paste('Method: holdout (',round(object@holdoutPct*100),'% in test set)\n\n',sep=''))
} else if(object@method=='leaveOneOut'){
cat('Method: leave-one-out\n\n')
}
for (i in 1:nstats){
cat(paste0(stat_names[i],':\n'))
stat_sum = lapply(sl, function(x) x@summary[i,])
stat_sum = do.call("rbind", stat_sum)
sort_ind <- switch(object@sort,
none = 1:nrow(stat_sum),
increasing = order(stat_sum$Estimate),
decreasing = order(stat_sum$Estimate, decreasing=TRUE))
stat_sum <- stat_sum[sort_ind, ]
print(stat_sum, digits=4)
if(i != nstats) cat('\n')
}
})
#Function to calculate RMSE and MAE
#Default function for statistic argument
#Returns a named list
RMSE_MAE <- function(object){
res <- residuals(object)
if(is.list(res)) res <- unlist(res)
mae <- mean(abs(res), na.rm=T)
rmse <- sqrt(mean(res^2, na.rm=T))
c(`Root mean square error`=rmse, `Mean absolute error`=mae)
}
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