diff --git a/scripts/sim_functions.R b/scripts/sim_functions.R
index 5020c3d2afd75d10278c9813e5061b0295942b7b..59e2c11c52cb30f8de7c0fb42eec5f6b3c92664c 100644
--- a/scripts/sim_functions.R
+++ b/scripts/sim_functions.R
@@ -1,370 +1,467 @@
-####----ABOUT---------------------------------------------------------------####
-####
-#### Evaluating alternative methods for modeling trap
-#### efficiencies of outmigrating juvenile salmonids
-####
-#### Authors: Walden et al.
-####
-#### Content: R-Code for functions used in simulation
-####
-#### This is the file with all functions needed to run the
-#### simulation shown in Section X of the manuscript.
-####
-
-####
-####----1. Function to generate beta-distributed data-----------------------####
-####
-
-## This uses names of variables declared in "scripts/sim_variables.R"
-simulateDataset <- function(nobs, type=c("train", "test", "tune"),
- efficiency=c("Max efficiency 0.1", "Max efficiency 0.25"),
- seeds=NA) {
-
- # Get number of observations depending on dataset type
- thisnobs <- ifelse(type == "test", ntest, nobs)
-
- ## Create vector of parameter values
- Beta <- NA
- Beta0 <- ifelse(efficiency == "Max efficiency 0.1", Beta0_maxpt1, Beta0_maxpt25)
- Beta[1] <- Beta1
- Beta[2] <- Beta2
- Beta[3] <- Beta3
- Beta[4] <- Beta4
- Beta[5] <- Beta5
- Beta[6] <- Beta6
- Beta[7] <- 1 # needed for year random effect
-
- # While-loop checks that generated dataset y values are all less than max
- # trap efficiency
- thiscondition <- TRUE
- while(thiscondition) {
-
- # If seeds aren't provided, then generate seeds, otherwise retrieve
- # seeds.
- if(any(is.na(seeds))) {
- seed1to6 <- round(runif(6, 1, 999999999),0)
- # While loop checks that there is at least one obs per random effect
- # group
- thiscond2 <- TRUE
- while (thiscond2) {
- seedyr <- round(runif(1, 1, 999999999),0)
- set.seed(seedyr)
- year <- sample.int(nyear, size=thisnobs, replace=TRUE)
- thiscond2 <- length(unique(year)) != nyear
- } # end while loop
- } else {
- seed1to6 <- as.numeric(seeds[1:6])
- seedyr <- as.numeric(seeds[7])
- set.seed(seedyr)
- year <- sample.int(nyear, size=thisnobs, replace=TRUE)
- } # end if-else loop
-
- # Create fixed-effects covariate vectors
- set.seed(seed1to6[2])
- Q <- runif(thisnobs, Qmin, Qmax)
- Q <- scale(Q)
- set.seed(seed1to6[3])
- C <- runif(thisnobs, degCmin, degCmax)
- C <- scale(C)
- set.seed(seed1to6[4])
- effort <- runif(thisnobs, effortmin, effortmax)
- effort <- scale(effort)
- set.seed(seed1to6[5])
- precip <- sample(c(0, 1), size=thisnobs, replace=TRUE) # yes-no
-
- # Create random effects, one for each group
- set.seed(seed1to6[1])
- yearRE <- rnorm(nyear, 0, raneffSD)
-
- # Create matrix of covariates
- covariates<-matrix(c(Q, C, effort, precip,
- year), nrow=thisnobs, ncol=5)
-
- # Create design matrix
- designMat<-cbind(covariates[,1], covariates[,2], covariates[,3],
- covariates[,4],
- covariates[,3] * covariates[,3],
- covariates[,1] * covariates[,2],
- yearRE[covariates[,5]] + Beta0)
-
- # Calculate mean mu
- lin.mu <- designMat%*%Beta
- mu <- inverselogit(lin.mu)
-
- # Calculate beta distribution parameters
- a <- mu / phiTrue
- b <- (1 - mu) / phiTrue
-
- # Realized y
- set.seed(seed1to6[6])
- y <- rbeta(thisnobs, a, b)
-
- # Check realized y to exit while loop
- thiscondition <- ifelse(efficiency == "Max efficiency 0.1",
- length(which(y > 0.1)) > 0,
- length(which(y > 0.25)) > 0)
- } # Exit while loop
-
- # Collate output
- seedcol <- as.numeric(c(seed1to6, seedyr, rep(NA, thisnobs-7)))
- results <- cbind(y, covariates, a, b, seedcol)
- return(results)
-} # End function
-
-####
-####----2. Function to create, compile, and run Nimble model----------------####
-####
-
-run_MCMC_allcode <- function(model, data, monitors, inits, niter, nburnin) {
-
- myModel <- readBUGSmodel(
- model,
- data=data,
- buildDerivs=FALSE
- )
-
- CmyModel <- compileNimble(myModel)
- myMCMC <- buildMCMC(CmyModel, monitors=monitors)
- CmyMCMC <- compileNimble(myMCMC)
-
- results <- runMCMC(
- CmyMCMC,
- inits=inits,
- niter=niter,
- nburnin=nburnin,
- thin=10
- )
-
- return(results)
-}
-
-####
-####----3. Function to parallelize Nimble MCMC chains-----------------------####
-####
-
-do <- function(cores, pop, fun, ...){
- require(doParallel)
- require(foreach)
- require(nimble)
- cl <- makeCluster(cores)
- registerDoParallel(cl)
- results <- foreach(pop,
- .packages=c("nimble")) %dopar% fun(...)
- stopCluster(cl)
- return(results)
-}
-
-####
-####----4. Calculate f-statistic: proportion of mcmc chain with same -------####
-#### sign as the mean
-####
-
-fstat <- function(x) {
- if(mean(x) < 0) {
- temp <- mean(x < 0, na.rm=TRUE)
- return(temp)
- }
- if(mean(x) > 0) {
- temp <- mean(x > 0, na.rm=TRUE)
- return(temp)
- }
- if(mean(x) == 0) {
- return(0)
- }
-}
-
-####
-####----5. Calculate log-likelihood for beta-distributed variable-----------####
-#### (Weinhold et al. 2020)
-####
-
-betaLogLik <- function(y, mu, phi) {
- lgamma(phi) - lgamma(mu*phi) - lgamma((1 - mu)*phi) + (mu*phi - 1)*log(y) +
- ((1 - mu)*phi - 1)*log(1 - y)
-}
-
-####
-####----6. Loss function for beta-distributed variable used in MLE----------####
-#### (Weinhold et al. 2020)
-####
-
-betaLoss <- function(par, y, mu) {
- -sum(lgamma(par) - lgamma(mu*par) - lgamma((1 - mu)*par) +
- (mu*par - 1)*log(y) + ((1 - mu)*par - 1)*log(1 - y))
-}
-
-####
-####----7. Get predictions from ranger random forest object-----------------####
-####---- when pdp or iml packages used
-
-pred.fun.ranger <- function(object, newdata) {
- results <- predict(object, newdata)$predictions
- return(results)
-}
-
-####
-####----8. Get root mean squared error (RMSE) using beta distribution-------####
-#### Standardized residuals defined by Ferrari and Cribari-Neto 2004 ####
-
-rmse <- function(y_test, y_predicted, phi) {
- std.residuals <- (y_test - y_predicted) /
- sqrt( (y_predicted * (1 - y_predicted)) / (1 + phi))
- out <- sqrt(mean(std.residuals^2))
- return(out)
-}
-
-####
-####----9. Parallel bootstrapping of boosted beta regression----------------####
-####
-
-run_bootstrap <- function(nboot, mstopmu, mstopphi, maindata, mainfmu,
- mainfphi, testdata, i, nyear) {
- inverselogit <- function(alpha) {
- exp(alpha) / ( exp(alpha) + 1 )
- }
- logit <- function(p) {
- log(p/(1-p))
- }
- results <- matrix(data=NA, nrow=300, ncol=1)
- thiscondition <- FALSE
- while(thiscondition == FALSE){
- bootingfunc <- function() {
- tryCatch(
- expr={
- thiscond <- TRUE
- while (thiscond) {
- temp <- sample(1:i, i, replace=TRUE) # get index of obs to keep
- bootdat <- maindata[temp, ] # get dataset
- thiscond <- length(unique(bootdat$year)) != nyear
- }
- results <- FDboostLSS(list(mu=mainfmu, phi=y ~ 1),
- timeformula=NULL,
- families=BetaLSS(),
- data=bootdat,
- control=boost_control(mstop=c(mu=mstopmu,
- phi=mstopphi),
- nu=0.1))
- return(results)
- }, error=function(error_message){
- out <- error_message
- return(out)
- }
- )
- }
- modelBoot <- bootingfunc()
- if(any(class(modelBoot) == "FDboostLSS")) {
- thiscondition <- TRUE
- } else {
- temp <- paste(as.character(modelBoot))
- temp <- str_sub(word(temp,-1),end=-2)
- mstopmu <- as.numeric(temp) - 1
- }
- }
-
- # Keep mstop values
- results[20,] <- mstop(modelBoot)[[1]]
- results[21,] <- mstop(modelBoot)[[2]]
-
- # #B1 on link scale (logit)
- results[1,] <- coef(modelBoot, raw=TRUE, which=2, parameter="mu")[[1]]
- # #B2 on link scale (logit)
- results[2,] <- coef(modelBoot, raw=TRUE, which=3, parameter="mu")[[1]]
- # #B3 on link scale (logit)
- results[3,] <- coef(modelBoot, raw=TRUE, which=4, parameter="mu")[[1]]
- # #B4 on link scale (logit)
- results[4,] <- coef(modelBoot, raw=TRUE, which=5, parameter="mu")[[1]]
- # #B5 on link scale (logit)
- x <- coef(modelBoot)$mu$smterms$`bbs(effort)`$x
- y <- coef(modelBoot)$mu$smterms$`bbs(effort)`$value[,1]
- thisdat <- data.frame("x"=x, "y"=y)
- thismod <- lm(y ~ I(x^2), data=thisdat)
- results[5,] <- as.numeric(thismod$coefficients[2])
- # #B6 on link scale (logit)
- results[6,] <- coef(modelBoot, raw=TRUE, which=7, parameter="mu")[[1]]
- # #Phi intercept
- results[11,] <- 1 / exp(modelBoot$phi$offset +
- as.vector(coef(modelBoot)$phi$intercept))
- #B0 on link scale (logit)
- results[10,] <- modelBoot$mu$offset + as.vector(coef(modelBoot)$mu$intercept)
- # yr.sd
- results[7,] <- sd(as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`))
-
- # prediction on test dataset using bootstrapped model
- testset <- testdata
- oldw <- getOption("warn")
- options(warn=-1)
- temp <- predict(modelBoot, newdata=testset, type="response")
- options(warn=oldw)
- testset$predy <- temp$mu
- testset$predphi <- 1 / temp$phi
- temp2 <- as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`)
- for(m in 1:nrow(testset)){
- testset$raneff[m] <- temp2[testset$year[m]]
- testset$predy2[m] <- inverselogit(logit(testset$predy[m]) - testset$raneff[m])
- testset$yhat[m] <-rbeta(1, testset$predy2[m] / testset$predphi,
- (1 - testset$predy2[m]) / testset$predphi)
- }
-
- results[100:199,] <- testset$yhat
- results[200:299,] <- testset$predphi
- return(results)
-}
-
-
-####
-####----9.5 Function to parallelize Bootstrapping---------------------------####
-####
-
-doboot <- function(numcores, pop, fun, ...){
- require(doParallel)
- require(foreach)
- require(gamboostLSS)
- require(FDboost)
- require(stringr)
- cl <- makeCluster(numcores)
- registerDoParallel(cl)
- results <- foreach(pop, .combine=cbind,
- .packages=c("gamboostLSS",
- "FDboost",
- "stringr")) %dopar% fun(...)
- stopCluster(cl)
- return(results)
-}
-
-####
-####----10. Inverse-logit function------------------------------------------####
-
-inverselogit <- function(alpha) {
- exp(alpha) / ( exp(alpha) + 1 )
-}
-
-####
-####----11. logit function------------------------------------------####
-
-logit <- function(p) {
- log(p/(1-p))
-}
-
-
-####
-####----12. Parallelize cvrisk() function-----------------------------------####
-
-myApply <- function(dataset, FDboostLSS, ...){
- myFun <- function(...) {
- library("mboost")
- FDboostLSS(...)
- }
- parLapply(cl=cl, dataset, myFun, ...)
-}
-
-
-####
-####----13. Customized predictor function (source: iml package at ----------####
-#### https://github.com/giuseppec/iml/blob/main/R/Predictor.R
-
-# This adds "regression" as the task for the iml:Predictor function. Otherwise
-# the task is "unknown".
-
-inferTaskFromModel.ranger <- function(model) {
- return("regression")
+####----ABOUT---------------------------------------------------------------####
+####
+#### Evaluating alternative methods for modeling trap
+#### efficiencies of outmigrating juvenile salmonids
+####
+#### Authors: Walden et al.
+####
+#### Content: R-Code for functions used in simulation
+####
+#### This is the file with all functions needed to run the
+#### simulation shown in Section X of the manuscript.
+####
+
+####
+####----1. Function to generate beta-distributed data-----------------------####
+####
+
+## This uses names of variables declared in "scripts/sim_variables.R"
+simulateDataset <- function(nobs, type=c("train", "test", "tune"),
+ efficiency=c("Max efficiency 0.1", "Max efficiency 0.25"),
+ seeds=NA) {
+
+ # Get number of observations depending on dataset type
+ thisnobs <- ifelse(type == "test", ntest, nobs)
+
+ ## Create vector of parameter values
+ Beta <- NA
+ Beta0 <- ifelse(efficiency == "Max efficiency 0.1", Beta0_maxpt1, Beta0_maxpt25)
+ Beta[1] <- Beta1
+ Beta[2] <- Beta2
+ Beta[3] <- Beta3
+ Beta[4] <- Beta4
+ Beta[5] <- Beta5
+ Beta[6] <- Beta6
+ Beta[7] <- 1 # needed for year random effect
+
+ # While-loop checks that generated dataset y values are all less than max
+ # trap efficiency
+ thiscondition <- TRUE
+ while(thiscondition) {
+
+ # If seeds aren't provided, then generate seeds, otherwise retrieve
+ # seeds.
+ if(any(is.na(seeds))) {
+ seed1to6 <- round(runif(6, 1, 999999999),0)
+ # While loop checks that there is at least one obs per random effect
+ # group
+ thiscond2 <- TRUE
+ while (thiscond2) {
+ seedyr <- round(runif(1, 1, 999999999),0)
+ set.seed(seedyr)
+ year <- sample.int(nyear, size=thisnobs, replace=TRUE)
+ thiscond2 <- length(unique(year)) != nyear
+ } # end while loop
+ } else {
+ seed1to6 <- as.numeric(seeds[1:6])
+ seedyr <- as.numeric(seeds[7])
+ set.seed(seedyr)
+ year <- sample.int(nyear, size=thisnobs, replace=TRUE)
+ } # end if-else loop
+
+ # Create fixed-effects covariate vectors
+ set.seed(seed1to6[2])
+ Q <- runif(thisnobs, Qmin, Qmax)
+ Q <- scale(Q)
+ set.seed(seed1to6[3])
+ C <- runif(thisnobs, degCmin, degCmax)
+ C <- scale(C)
+ set.seed(seed1to6[4])
+ effort <- runif(thisnobs, effortmin, effortmax)
+ effort <- scale(effort)
+ set.seed(seed1to6[5])
+ precip <- sample(c(0, 1), size=thisnobs, replace=TRUE) # yes-no
+
+ # Create random effects, one for each group
+ set.seed(seed1to6[1])
+ yearRE <- rnorm(nyear, 0, raneffSD)
+
+ # Create matrix of covariates
+ covariates<-matrix(c(Q, C, effort, precip,
+ year), nrow=thisnobs, ncol=5)
+
+ # Create design matrix
+ designMat<-cbind(covariates[,1], covariates[,2], covariates[,3],
+ covariates[,4],
+ covariates[,3] * covariates[,3],
+ covariates[,1] * covariates[,2],
+ yearRE[covariates[,5]] + Beta0)
+
+ # Calculate mean mu
+ lin.mu <- designMat%*%Beta
+ mu <- inverselogit(lin.mu)
+
+ # Calculate beta distribution parameters
+ a <- mu / phiTrue
+ b <- (1 - mu) / phiTrue
+
+ # Realized y
+ set.seed(seed1to6[6])
+ y <- rbeta(thisnobs, a, b)
+
+ # Check realized y to exit while loop
+ thiscondition <- ifelse(efficiency == "Max efficiency 0.1",
+ length(which(y > 0.1)) > 0,
+ length(which(y > 0.25)) > 0)
+ } # Exit while loop
+
+ # Collate output
+ seedcol <- as.numeric(c(seed1to6, seedyr, rep(NA, thisnobs-7)))
+ results <- cbind(y, covariates, a, b, seedcol)
+ return(results)
+} # End function
+
+####
+####----2. Function to create, compile, and run Nimble model----------------####
+####
+
+run_MCMC_allcode <- function(model, data, monitors, inits, niter, nburnin) {
+
+ myModel <- readBUGSmodel(
+ model,
+ data=data,
+ buildDerivs=FALSE
+ )
+
+ CmyModel <- compileNimble(myModel)
+ myMCMC <- buildMCMC(CmyModel, monitors=monitors)
+ CmyMCMC <- compileNimble(myMCMC)
+
+ results <- runMCMC(
+ CmyMCMC,
+ inits=inits,
+ niter=niter,
+ nburnin=nburnin,
+ thin=10
+ )
+
+ return(results)
+}
+
+####
+####----3. Function to parallelize Nimble MCMC chains-----------------------####
+####
+
+do <- function(cores, pop, fun, ...){
+ require(doParallel)
+ require(foreach)
+ require(nimble)
+ cl <- makeCluster(cores)
+ registerDoParallel(cl)
+ results <- foreach(pop,
+ .packages=c("nimble")) %dopar% fun(...)
+ stopCluster(cl)
+ return(results)
+}
+
+####
+####----4. Calculate f-statistic: proportion of mcmc chain with same -------####
+#### sign as the mean
+####
+
+fstat <- function(x) {
+ if(mean(x) < 0) {
+ temp <- mean(x < 0, na.rm=TRUE)
+ return(temp)
+ }
+ if(mean(x) > 0) {
+ temp <- mean(x > 0, na.rm=TRUE)
+ return(temp)
+ }
+ if(mean(x) == 0) {
+ return(0)
+ }
+}
+
+####
+####----5. Calculate log-likelihood for beta-distributed variable-----------####
+#### (Weinhold et al. 2020)
+####
+
+betaLogLik <- function(y, mu, phi) {
+ lgamma(phi) - lgamma(mu*phi) - lgamma((1 - mu)*phi) + (mu*phi - 1)*log(y) +
+ ((1 - mu)*phi - 1)*log(1 - y)
+}
+
+####
+####----6. Loss function for beta-distributed variable used in MLE----------####
+#### (Weinhold et al. 2020)
+####
+
+betaLoss <- function(par, y, mu) {
+ -sum(lgamma(par) - lgamma(mu*par) - lgamma((1 - mu)*par) +
+ (mu*par - 1)*log(y) + ((1 - mu)*par - 1)*log(1 - y))
+}
+
+####
+####----7. Get predictions from ranger random forest object-----------------####
+####---- when pdp or iml packages used
+
+pred.fun.ranger <- function(object, newdata) {
+ results <- predict(object, newdata)$predictions
+ return(results)
+}
+
+####
+####----8. Get root mean squared error (RMSE) using beta distribution-------####
+#### Standardized residuals defined by Ferrari and Cribari-Neto 2004 ####
+
+rmse <- function(y_test, y_predicted, phi) {
+ std.residuals <- (y_test - y_predicted) /
+ sqrt( (y_predicted * (1 - y_predicted)) / (1 + phi))
+ out <- sqrt(mean(std.residuals^2))
+ return(out)
+}
+
+####
+####----9. Parallel bootstrapping of boosted beta regression----------------####
+####
+
+run_bootstrap <- function(nboot, mstopmu, mstopphi, maindata, mainfmu,
+ mainfphi, testdata, i, nyear) {
+ inverselogit <- function(alpha) {
+ exp(alpha) / ( exp(alpha) + 1 )
+ }
+ logit <- function(p) {
+ log(p/(1-p))
+ }
+ results <- matrix(data=NA, nrow=300, ncol=1)
+ thiscondition <- FALSE
+ while(thiscondition == FALSE){
+ bootingfunc <- function() {
+ tryCatch(
+ expr={
+ thiscond <- TRUE
+ while (thiscond) {
+ temp <- sample(1:i, i, replace=TRUE) # get index of obs to keep
+ bootdat <- maindata[temp, ] # get dataset
+ thiscond <- length(unique(bootdat$year)) != nyear
+ }
+ results <- FDboostLSS(list(mu=mainfmu, phi=y ~ 1),
+ timeformula=NULL, families=BetaLSS(), data=bootdat,
+ control=boost_control(mstop=c(mu=mstopmu, phi=mstopphi), nu=0.1))
+ return(results)
+ }, error=function(error_message){
+ out <- error_message
+ return(out)
+ }
+ )
+ }
+ modelBoot <- bootingfunc()
+ if(any(class(modelBoot) == "FDboostLSS")) {
+ thiscondition <- TRUE
+ } else {
+ temp <- paste(as.character(modelBoot))
+ temp <- str_sub(word(temp,-1),end=-2)
+ mstopmu <- as.numeric(temp) - 1
+ }
+ }
+
+ # Keep mstop values
+ results[20,] <- mstop(modelBoot)[[1]]
+ results[21,] <- mstop(modelBoot)[[2]]
+
+ # #B1 on link scale (logit)
+ results[1,] <- coef(modelBoot, raw=TRUE, which=2, parameter="mu")[[1]]
+ # #B2 on link scale (logit)
+ results[2,] <- coef(modelBoot, raw=TRUE, which=3, parameter="mu")[[1]]
+ # #B3 on link scale (logit)
+ results[3,] <- coef(modelBoot, raw=TRUE, which=4, parameter="mu")[[1]]
+ # #B4 on link scale (logit)
+ results[4,] <- coef(modelBoot, raw=TRUE, which=5, parameter="mu")[[1]]
+ # #B5 on link scale (logit)
+ x <- coef(modelBoot)$mu$smterms$`bbs(effort)`$x
+ y <- coef(modelBoot)$mu$smterms$`bbs(effort)`$value[,1]
+ thisdat <- data.frame("x"=x, "y"=y)
+ thismod <- lm(y ~ I(x^2), data=thisdat)
+ results[5,] <- as.numeric(thismod$coefficients[2])
+ # #B6 on link scale (logit)
+ results[6,] <- coef(modelBoot, raw=TRUE, which=7, parameter="mu")[[1]]
+ # #Phi intercept
+ results[11,] <- 1 / exp(modelBoot$phi$offset +
+ as.vector(coef(modelBoot)$phi$intercept))
+ #B0 on link scale (logit)
+ results[10,] <- modelBoot$mu$offset + as.vector(coef(modelBoot)$mu$intercept)
+ # yr.sd
+ results[7,] <- sd(as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`))
+
+ # prediction on test dataset using bootstrapped model
+ testset <- testdata
+ oldw <- getOption("warn")
+ options(warn=-1)
+ temp <- predict(modelBoot, newdata=testset, type="response")
+ options(warn=oldw)
+ testset$predy <- temp$mu
+ testset$predphi <- 1 / temp$phi
+ temp2 <- as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`)
+ for(m in 1:nrow(testset)){
+ testset$raneff[m] <- temp2[testset$year[m]]
+ testset$predy2[m] <- inverselogit(logit(testset$predy[m]) - testset$raneff[m])
+ testset$yhat[m] <-rbeta(1, testset$predy2[m] / testset$predphi,
+ (1 - testset$predy2[m]) / testset$predphi)
+ }
+
+ results[100:199,] <- testset$yhat
+ results[200:299,] <- testset$predphi
+ return(results)
+}
+
+####
+####----9.1. Bootstrapping with out-of-sample prediction--------------------####
+####
+
+run_bootstrap_oos <- function(nboot, mstopmu, mstopphi, maindata, mainfmu,
+ mainfphi, testdata, i, nyear) {
+ inverselogit <- function(alpha) {
+ exp(alpha) / ( exp(alpha) + 1 )
+ }
+ logit <- function(p) {
+ log(p/(1-p))
+ }
+ results <- matrix(data=NA, nrow=300, ncol=1)
+ thiscondition <- FALSE
+ while(thiscondition == FALSE){
+ bootingfunc <- function() {
+ tryCatch(
+ expr={
+ thiscond <- TRUE
+ while (thiscond) {
+ keepindex <- sample(1:i, i, replace=TRUE) # get index of obs to keep
+ bootdat <- maindata[keepindex, ] # get dataset
+ thiscond <- length(unique(bootdat$year)) != nyear
+ }
+ results <- FDboostLSS(list(mu=mainfmu, phi=y ~ 1),
+ timeformula=NULL,
+ families=BetaLSS(),
+ data=bootdat,
+ control=boost_control(mstop=c(mu=mstopmu,
+ phi=mstopphi),
+ nu=0.1))
+ return(list(results,keepindex))
+ }, error=function(error_message){
+ out <- error_message
+ return(out)
+ }
+ )
+ }
+ modelBoot <- bootingfunc()
+ if(any(class(modelBoot) == "error")) {
+ temp <- paste(as.character(modelBoot))
+ temp <- str_sub(word(temp,-1),end=-2)
+ mstopmu <- as.numeric(temp) - 1
+ } else {
+ keepindex <- modelBoot[[2]]
+ modelBoot <- modelBoot[[1]]
+ thiscondition <- TRUE
+ }
+ }
+
+ # Keep mstop values
+ results[20,] <- mstop(modelBoot)[[1]]
+ results[21,] <- mstop(modelBoot)[[2]]
+
+ # #B1 on link scale (logit)
+ results[1,] <- coef(modelBoot, raw=TRUE, which=2, parameter="mu")[[1]]
+ # #B2 on link scale (logit)
+ results[2,] <- coef(modelBoot, raw=TRUE, which=3, parameter="mu")[[1]]
+ # #B3 on link scale (logit)
+ results[3,] <- coef(modelBoot, raw=TRUE, which=4, parameter="mu")[[1]]
+ # #B4 on link scale (logit)
+ results[4,] <- coef(modelBoot, raw=TRUE, which=5, parameter="mu")[[1]]
+ # #B5 on link scale (logit)
+ x <- coef(modelBoot)$mu$smterms$`bbs(effort)`$x
+ y <- coef(modelBoot)$mu$smterms$`bbs(effort)`$value[,1]
+ thisdat <- data.frame("x"=x, "y"=y)
+ thismod <- lm(y ~ I(x^2), data=thisdat)
+ results[5,] <- as.numeric(thismod$coefficients[2])
+ # #B6 on link scale (logit)
+ results[6,] <- coef(modelBoot, raw=TRUE, which=7, parameter="mu")[[1]]
+ # #Phi intercept
+ results[11,] <- 1 / exp(modelBoot$phi$offset +
+ as.vector(coef(modelBoot)$phi$intercept))
+ #B0 on link scale (logit)
+ results[10,] <- modelBoot$mu$offset + as.vector(coef(modelBoot)$mu$intercept)
+ # yr.sd
+ results[7,] <- sd(as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`))
+
+ # prediction on test dataset using bootstrapped model
+ dontkeep <- unique(keepindex)
+ testset <- maindata
+ maindata$year <- as.numeric(as.factor(maindata$year))
+ oldw <- getOption("warn")
+ options(warn=-1)
+ temp <- predict(modelBoot, type="response")
+ options(warn=oldw)
+ maindata$predy <- temp$mu
+ maindata$predphi <- 1 / temp$phi
+ temp2 <- as.vector(coef(modelBoot, raw=TRUE)$mu$`brandom(year)`)
+ for(m in 1:nrow(maindata)){
+ maindata$raneff[m] <- temp2[maindata$year[m]]
+ maindata$predy2[m] <- inverselogit(logit(maindata$predy[m]) - maindata$raneff[m])
+ maindata$yhat[m] <-rbeta(1, maindata$predy2[m] / maindata$predphi,
+ (1 - maindata$predy2[m]) / maindata$predphi)
+ }
+
+ maindata$yhat[dontkeep] <- NA
+ st <- nrow(maindata) + 100 - 1
+ results[100:st,] <- maindata$yhat
+ return(results)
+}
+
+####
+####----9.5 Function to parallelize Bootstrapping---------------------------####
+####
+doboot <- function(numcores, pop, fun, ...){
+ require(doParallel)
+ require(foreach)
+ require(gamboostLSS)
+ require(FDboost)
+ require(stringr)
+ cl <- makeCluster(numcores)
+ registerDoParallel(cl)
+ # clusterExport(cl, varlist=c("FUN", "a", "b"))
+ results <- foreach(pop, .combine=cbind,
+ .packages=c("gamboostLSS",
+ "FDboost",
+ "stringr")) %dopar% fun(...)
+ stopCluster(cl)
+ return(results)
+}
+
+####
+####----10. Inverse-logit function------------------------------------------####
+
+inverselogit <- function(alpha) {
+ exp(alpha) / ( exp(alpha) + 1 )
+}
+
+####
+####----11. logit function------------------------------------------####
+
+logit <- function(p) {
+ log(p/(1-p))
+}
+
+
+####
+####----12. Parallelize cvrisk() function-----------------------------------####
+
+myApply <- function(dataset, FDboostLSS, ...){
+ myFun <- function(...) {
+ library("mboost")
+ FDboostLSS(...)
+ }
+ parLapply(cl=cl, dataset, myFun, ...)
+}
+
+
+####
+####----13. Customized predictor function (source: iml package at ----------####
+#### https://github.com/giuseppec/iml/blob/main/R/Predictor.R
+
+# This adds "regression" as the task for the iml:Predictor function. Otherwise
+# the task is "unknown" and it throws an error.
+
+inferTaskFromModel.ranger <- function(model) {
+ return("regression")
}
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