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Prediction_AGF18_githubtidy_NatCommschecks_v2_Final.R
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#R version [1] "C:/Users/kquigley/Documents/R/R-3.6.0/library"
#Tools, Global Options, General --> [64-bit] C:\Users\kquigley\Documents\R\R-3.6.0
#Install packages ----
library(rlang) #0.4.10
library(caret) #6.0-86
library(gbm) #2.1.5
library(rsample) #_0.0.6
library(dplyr) #_0.8.0.1
library(plotmo) #_3.5.7
library(pdp) #_0.7.0
library(ggpubr) #_0.1.8
#sessionInfo() #check versions are the same
#Check your Random number generator ----
#make sure random and rounding number generator. These defaults changed after R (v.3.6.1)
RNGkind()
sessionInfo()
#RNGkind("Rounding")
RNGkind(sample.kind = "Rounding")
###
#use random values for training. Random search of hyperparameters
fitControl_rando <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
repeats = 10,
search = "random")
#Download the larval data with satellite data attached appended----
larv6<-read.csv("larv6.csv")
#####################(1 Purebred larvae)####-----
#Subset the larval purebreds----
Raw_larvae2018_filt_temp.metrics5_pure<-larv6%>%
filter(Parentage %in% c("BK x BK", "LS x LS","CU x CU","SB x SB","DR x DR"))
set.seed(88901)
Larv.pure_splits <- initial_split(Raw_larvae2018_filt_temp.metrics5_pure, .99, strata = "Perc_surv", na.rm=TRUE)
set.seed(88901)
Larv18.train.pure <- training(Larv.pure_splits)
set.seed(88901)
Larv18._test.pure <- testing(Larv.pure_splits)
caret_grid.larv.hypergrid_purer.testNatComms <- data.frame(n.trees = 388, interaction.depth = 3,
shrinkage = 0.01,n.minobsinnode = 5)
#interaction depth from recomended 1 to 3 because plot squared error loss flattens out
larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom$results #check params
set.seed(25)
larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom <-
caret::train(Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Larv18.train.pure, verbose = TRUE,
tuneGrid = caret_grid.larv.hypergrid_purer.testNatComms, trControl = fitControl_rando, na.action=na.omit)
summary(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom$finalModel, plotit = TRUE)
plotres(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom)
plotmo(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom, pmethod="partdep", all1=TRUE, all2=TRUE) #testx object
###
hyper_gridjuv.LARVpure <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.LARVpure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.LARVpure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.larv.pure <- gbm(
formula = Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Larv18.train.pure,
n.trees = 3000,
interaction.depth = hyper_gridjuv.LARVpure$interaction.depth[i],
shrinkage = hyper_gridjuv.LARVpure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.LARVpure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.LARVpure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.LARVpure$optimal_trees[i] <- which.min(gbm.tune.larv.pure$valid.error)
hyper_gridjuv.LARVpure$min_RMSE[i] <- sqrt(min(gbm.tune.larv.pure$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.LARVpure)
hyper_gridjuv.LARVpure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
##Purebred larval prediction----
x2<-larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom %>% partial(pred.var = "TSA_DHWStandardDeviation") %>% autoplot(smooth = TRUE, smooth.method="auto", smooth.span= 0.5, ylab = expression(f(TSA_DHWStandardDeviation))) + theme_light() + ggtitle("ggplot2-based PDP")
x.larvpur<-ggplot_build(x2)$data[[2]]$x
y.larvpur<-ggplot_build(x2)$data[[2]]$y
TSAbuild<-data.frame(a=ggplot_build(x2)$data[[2]]$x,b=ggplot_build(x2)$data[[2]]$y)
#At this point I used write.csv to write out the data frame above and fit the polynomial in excel
#write.csv(TSAbuild, "TSAbuild_NatComms_v2final.csv")
formula.l.p <- y ~ poly(x, 3)
larv.pure.finalTSA<-ggplot(TSAbuild, aes(a, b)) +
geom_point() +
stat_smooth(aes(), method = "lm", formula = formula.l.p) +theme_bw()
################
R2.1<-larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom$resample$Rsquared
boxplot(R2.1)
mean(R2.1)
std.error(R2.1)
dim(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU$resample)
#fitControl_rando 10 x10 = 100 CV fold repeats.
#####################(2 Hybrid larvae)####-----
Raw_larvae2018_filt_temp.metrics5_hybrids<-larv6%>%
filter(Parentage %in% c("BK x LS", "BK x CU", "BK x SB", "BK x DR",
"DR x LS", "DR x CU", "DR x SB", "DR x BK",
"CU x LS", "CU x BK", "CU x SB", "CU x DR",
"LS x BK", "LS x CU", "LS x SB", "LS x DR",
"SB x LS", "SB x CU", "SB x BK", "SB x DR"))
set.seed(88901)
Larv.hyb_splits <- initial_split(Raw_larvae2018_filt_temp.metrics5_hybrids, .99, strata = "Perc_surv", na.rm=TRUE)
set.seed(88901)
Larv18.train.hyb <- training(Larv.hyb_splits)
set.seed(88901)
Larv18._test.hyb <- testing(Larv.hyb_splits)
#**
caret_grid.larv.hypergrid_hybr <- data.frame(n.trees = 100, interaction.depth = 1,
shrinkage = 0.3,
n.minobsinnode = 5)
set.seed(25)
larv.fit_gbm_caret.cv_HYB.hyperTune <-
caret::train(Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation + DRT, method = "gbm", data = Larv18.train.hyb, verbose = TRUE,
tuneGrid = caret_grid.larv.hypergrid_hybr, trControl = fitControl_rando)
summary(larv.fit_gbm_caret.cv_HYB.hyperTune$finalModel, plotit = TRUE)
plotres(larv.fit_gbm_caret.cv_HYB.hyperTune)
plotmo(larv.fit_gbm_caret.cv_HYB.hyperTune, pmethod="partdep", all1=TRUE, all2=TRUE) #testx object
hyper_gridjuv.LARVhyb <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.LARVhyb)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.LARVhyb)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.larv.hyb <- gbm(
formula = Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation + DRT,
distribution = "gaussian",
data = Larv18.train.hyb,
n.trees = 3000,
interaction.depth = hyper_gridjuv.LARVhyb$interaction.depth[i],
shrinkage = hyper_gridjuv.LARVhyb$shrinkage[i],
n.minobsinnode = hyper_gridjuv.LARVhyb$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.LARVhyb$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.LARVhyb$optimal_trees[i] <- which.min(gbm.tune.larv.hyb$valid.error)
hyper_gridjuv.LARVhyb$min_RMSE[i] <- sqrt(min(gbm.tune.larv.hyb$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.LARVhyb)
hyper_gridjuv.LARVhyb %>%
dplyr::arrange(min_RMSE) %>%
head(10)
####Hybrid larval prediction----
larv.hybDRT.1<-larv.fit_gbm_caret.cv_HYB.hyperTune %>% partial(pred.var = "DRT") %>% autoplot(smooth = TRUE, smooth.method="auto", smooth.span= 0.5, ylab = expression(f(DRT))) + theme_light() + ggtitle("ggplot2-based PDP") #library pdp
x.larvDRT<-ggplot_build(larv.hybDRT.1)$data[[2]]$x
y.larvDRT<-ggplot_build(larv.hybDRT.1)$data[[2]]$y
DRT.larvbuild<-data.frame(a2=ggplot_build(larv.hybDRT.1)$data[[2]]$x,b2=ggplot_build(larv.hybDRT.1)$data[[2]]$y)
#DRT.larvbuild$category <- "larvhyb"
#write.csv as explained before
formula.drt.larv <- y ~ poly(x, 5, raw = TRUE)
larv.hyb.DRT_small<-ggplot(DRT.larvbuild, aes(a2, b2)) +
geom_point() +theme_bw()+
stat_smooth(aes(), method = "lm", formula = formula.drt.larv)
R2.2<-larv.fit_gbm_caret.cv_HYB.hyperTune$resample$Rsquared
boxplot(R2.2)
mean(R2.2)
std.error(R2.2)
dim(larv.fit_gbm_caret.cv_HYB.hyperTune$resample)
#fitControl_rando 10 x10 = 100 CV fold repeats.
#####################(3 Purebred D1)####-----
juv6.6_subset<-read.csv("juv6.6_subset.csv")
Raw_juv2018_filt_temp.metrics5_D1<-juv6.6_subset %>% filter(Zoox %in% c("D1"))
Raw_juv2018_filt_temp.metrics5_D1_pure<-Raw_juv2018_filt_temp.metrics5_D1 %>%
filter(Family %in% c(" BKxBK ", " LSxLS "," CUxCU "," SBxSB "," DRxDR "))
###Summary
set.seed(88901)
JuvD1.pure_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_D1_pure, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.D1_train.pure <- training(JuvD1.pure_splits)
set.seed(88901)
Juv18.D1_test.pure <- testing(JuvD1.pure_splits)
caret_grid.JUV.hypergrid.D1pure <- data.frame(n.trees = 30,
interaction.depth = 1,
shrinkage = 0.3,
n.minobsinnode = 10)
set.seed(25)
larv.fit_gbm_caret.cv_D1pure.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.D1_train.pure, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid.D1pure, trControl = fitControl_rando, na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_D1pure.hyperTune)
summary(larv.fit_gbm_caret.cv_D1pure.hyperTune$finalModel, plotit = F)
plotmo(larv.fit_gbm_caret.cv_D1pure.hyperTune, pmethod="partdep", all1=TRUE, all2=TRUE)
####
hyper_gridjuv.D1pure <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0,
min_RMSE = 0
)
# total number of combinations
nrow(hyper_gridjuv.D1pure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.D1pure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.D1pure <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.D1_train.pure,
n.trees = 3000,
interaction.depth = hyper_gridjuv.D1pure$interaction.depth[i],
shrinkage = hyper_gridjuv.D1pure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.D1pure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.D1pure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.D1pure$optimal_trees[i] <- which.min(gbm.tune.juvs.D1pure$valid.error)
hyper_gridjuv.D1pure$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.D1pure$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.D1pure)
hyper_gridjuv.D1pure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
##Predict D1 purebreds----
juv.pure.D1_ts_1<-larv.fit_gbm_caret.cv_D1pure.hyperTune %>% partial(pred.var = "ts_frame...2.dim.ts_frame..2...x.") %>% autoplot(smooth = TRUE, smooth.method="auto", smooth.span= 0.5, ylab = expression(f(ts_))) + theme_light() + ggtitle("ggplot2-based PDP")
x.juv.D1.ts_<-ggplot_build(juv.pure.D1_ts_1)$data[[2]]$x
y.juv.D1.ts_<-ggplot_build(juv.pure.D1_ts_1)$data[[2]]$y
ts_.JuvD1build<-data.frame(a3=x.juv.D1.ts_,b3=y.juv.D1.ts_)
#write.csv
formula_Juv_ts <- y ~ poly(x, 3, raw = TRUE)
Juv_D1.purets_small<-ggplot(ts_.JuvD1build, aes(a3, b3)) +
geom_point() +theme_bw()+
stat_smooth(aes(), method = "lm", formula = formula_Juv_ts)
R2.3<-larv.fit_gbm_caret.cv_D1pure.hyperTune$resample$Rsquared
boxplot(R2.3)
mean(R2.3)
std.error(R2.3)
#####################(4 Hybrid D1)####-----
Raw_juv2018_filt_temp.metrics5_D1_hyb<-Raw_juv2018_filt_temp.metrics5_D1 %>%
filter(Family %in% c(" BKxCU ", " BKxDR ", " BKxLS ", " BKxSB ", " CUxBK ", " CUxDR ",
" CuxLS ", " CUxSB ", " DRxBK ", " DRxCU ", " DRxSB ", " LSxBK ",
" LSxCU ", " LSxSB ", " SBxBK ", " SBxDR ", " SBxLS ", " CUxLS "))
set.seed(88901)
JuvD1.hyb_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_D1_hyb, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.D1_train.hyb <- training(JuvD1.hyb_splits)
set.seed(88901)
Juv18.D1_test.hyb <- testing(JuvD1.hyb_splits)
caret_grid.JUV.hypergrid_D1hyb <- data.frame(n.trees = 50, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 10)
set.seed(25)
larv.fit_gbm_caret.cv_D1hyb.hyperTune <-
caret::train(PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation + DRT, method = "gbm", data = Juv18.D1_train.hyb, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid_D1hyb, trControl = fitControl_rando, na.action=na.omit)
summary(larv.fit_gbm_caret.cv_D1hyb.hyperTune$finalModel, plotit = TRUE)
plotres(larv.fit_gbm_caret.cv_D1hyb.hyperTune)
plotmo(larv.fit_gbm_caret.cv_D1hyb.hyperTune, pmethod="partdep", all1=TRUE, all2=TRUE)
hyper_gridjuv.D1hyb <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.D1hyb)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.D1hyb)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.D1hyb <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.D1_train.hyb,
n.trees = 3000,
interaction.depth = hyper_gridjuv.D1hyb$interaction.depth[i],
shrinkage = hyper_gridjuv.D1hyb$shrinkage[i],
n.minobsinnode = hyper_gridjuv.D1hyb$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.D1hyb$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.D1hyb$optimal_trees[i] <- which.min(gbm.tune.juvs.D1hyb$valid.error)
hyper_gridjuv.D1hyb$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.D1hyb$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.D1hyb)
hyper_gridjuv.D1hyb %>%
dplyr::arrange(min_RMSE) %>%
head(10)
#Predict D1 hybrids
head(larv.fit_gbm_caret.cv_D1hyb.hyperTune$trainingData$.outcome)
gbm.model.outputs.Juv.D1.hyb.TSA<-larv.fit_gbm_caret.cv_D1hyb.hyperTune$trainingData
predict.gbm.Juv.D1.hyb.TSA<-ggscatter(gbm.model.outputs.Juv.D1.hyb.TSA, x = "TSA_DHWStandardDeviation", y = ".outcome",
add = "loess", conf.int = TRUE)+stat_regline_equation(label.y = 35)+labs(x="predicted gbm juv D1 hyb TSA", y=expression("Percent Survival"*~degree*C))+
stat_cor(method = "spearman")
###D1 predict juveniles hyb----
juv.hyb.D1_ts_1<-larv.fit_gbm_caret.cv_D1hyb.hyperTune %>% partial(pred.var = "TSA_DHWStandardDeviation") %>% autoplot(smooth = TRUE, smooth.method="auto", smooth.span= 0.5, ylab = expression(f(TSA))) + theme_light() + ggtitle("ggplot2-based PDP")
x.juv.D1.hyb_TSA<-ggplot_build(juv.hyb.D1_ts_1)$data[[2]]$x
y.juv.D1.hyb_TSA<-ggplot_build(juv.hyb.D1_ts_1)$data[[2]]$y
TSA.JuvD1.hybuild<-data.frame(a4=x.juv.D1.hyb_TSA,b4=y.juv.D1.hyb_TSA)
#write.csv
formula.TSA <- y ~ poly(x, 3, raw = TRUE)
JuvD1.hyb.finalTSA<-ggplot(TSA.JuvD1.hybuild, aes(a4, b4)) +
geom_point() +theme_bw()+
stat_smooth(aes(), method = "lm", formula = formula.TSA)
R2.4<-larv.fit_gbm_caret.cv_D1hyb.hyperTune$resample$Rsquared
boxplot(R2.4)
mean(R2.4)
std.error(R2.4)
#####################(5 Purebred C1)####-----
Raw_juv2018_filt_temp.metrics5_C1<-juv6.6_subset %>% filter(Zoox %in% c("C1"))
Raw_juv2018_filt_temp.metrics5_C1_pure<-Raw_juv2018_filt_temp.metrics5_C1 %>%
filter(Family %in% c(" BKxBK ", " LSxLS "," CUxCU "," SBxSB "," DRxDR "))
set.seed(88901)
JuvC1.pure_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_C1_pure, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.C1_train.pure <- training(JuvC1.pure_splits)
set.seed(88901)
Juv18.C1_test.pure <- testing(JuvC1.pure_splits)
caret_grid.JUV.hypergrid <- data.frame(n.trees = 600, interaction.depth = 5,
shrinkage = 0.01,
n.minobsinnode = 10)
set.seed(25)
larv.fit_gbm_caret.cv_C1pure.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation + DRT, method = "gbm", data = Juv18.C1_train.pure, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid, trControl = fitControl_rando, na.action=na.omit)
#diagnostics
plotres(larv.fit_gbm_caret.cv_C1pure.hyperTune)
summary(larv.fit_gbm_caret.cv_C1pure.hyperTune$finalModel, plotit = F)
####
hyper_gridjuv <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv)
## [1] 54
for(i in 1:nrow(hyper_gridjuv)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs <- gbm(
formula = PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation + DRT,
distribution = "gaussian",
data = Juv18.C1_train.pure,
n.trees = 3000,
interaction.depth = hyper_gridjuv$interaction.depth[i],
shrinkage = hyper_gridjuv$shrinkage[i],
n.minobsinnode = hyper_gridjuv$n.minobsinnode[i],
bag.fraction = hyper_gridjuv$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv$optimal_trees[i] <- which.min(gbm.tune.juvs$valid.error)
hyper_gridjuv$min_RMSE[i] <- sqrt(min(gbm.tune.juvs$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv)
hyper_gridjuv %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.5<-larv.fit_gbm_caret.cv_C1pure.hyperTune$resample$Rsquared
boxplot(R2.5)
mean(R2.5)
std.error(R2.5)
#####################(6 Hybrid C1)####-----
Raw_juv2018_filt_temp.metrics5_C1_hyb<-Raw_juv2018_filt_temp.metrics5_C1 %>%
filter(Family %in% c(" BKxCU ", " BKxDR ", " BKxLS ", " BKxSB ", " CUxBK ", " CUxDR ",
" CuxLS ", " CUxSB ", " DRxBK ", " DRxCU ", " DRxSB ", " LSxBK ",
" LSxCU ", " LSxSB ", " SBxBK ", " SBxDR ", " SBxLS ", " CUxLS "))
# split
set.seed(88901)
JuvC1.hyb_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_C1_hyb, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.C1_train.hyb <- training(JuvC1.hyb_splits)
set.seed(88901)
Juv18.C1_test.hyb <- testing(JuvC1.hyb_splits)
caret_grid.JUV.hypergrid_C1hyb <- data.frame(n.trees = 50, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 10)
set.seed(25)
larv.fit_gbm_caret.cv_C1hyb.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.C1_train.hyb, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid_C1hyb, trControl = fitControl_rando, na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_C1hyb.hyperTune)
summary(larv.fit_gbm_caret.cv_C1hyb.hyperTune$finalModel, plotit = TRUE)
hyper_gridjuv.C1hyb <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.C1hyb)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.C1hyb)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.C1hyb <- gbm(
formula = PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.C1_train.hyb, #Raw_juv2018_filt_temp.metrics5_C1_hyb,
n.trees = 3000,
interaction.depth = hyper_gridjuv.C1hyb$interaction.depth[i],
shrinkage = hyper_gridjuv.C1hyb$shrinkage[i],
n.minobsinnode = hyper_gridjuv.C1hyb$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.C1hyb$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.C1hyb$optimal_trees[i] <- which.min(gbm.tune.juvs.C1hyb$valid.error)
hyper_gridjuv.C1hyb$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.C1hyb$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.C1hyb)
hyper_gridjuv.C1hyb %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.6<-larv.fit_gbm_caret.cv_C1hyb.hyperTune$resample$Rsquared
boxplot(R2.6)
mean(R2.6)
std.error(R2.6)
#####################(7 Purebred SS)####-----
Raw_juv2018_filt_temp.metrics5_SS<-juv6.6_subset %>% filter(Zoox %in% c("SS"))
Raw_juv2018_filt_temp.metrics5_SS_pure<-Raw_juv2018_filt_temp.metrics5_SS %>%
filter(Family %in% c(" BKxBK ", " LSxLS "," CUxCU "," SBxSB "," DRxDR "))
set.seed(88901)
JuvSS.pure_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_SS_pure, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.SS_train.pure <- training(JuvSS.pure_splits)
set.seed(88901)
Juv18.SS_test.pure <- testing(JuvSS.pure_splits)
caret_grid.JUV.hypergrid.SSpure <- data.frame(n.trees = 30, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 15)
set.seed(25)
larv.fit_gbm_caret.cv_SSpure.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.SS_train.pure, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid.SSpure, trControl = fitControl_rando, na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_SSpure.hyperTune)
summary(larv.fit_gbm_caret.cv_SSpure.hyperTune$finalModel, plotit = TRUE)
hyper_gridjuv.SSpure <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.SSpure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.SSpure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.SSpure <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.SS_train.pure, #Raw_juv2018_filt_temp.metrics5_SS_pure,
n.trees = 3000,
interaction.depth = hyper_gridjuv.SSpure$interaction.depth[i],
shrinkage = hyper_gridjuv.SSpure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.SSpure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.SSpure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.SSpure$optimal_trees[i] <- which.min(gbm.tune.juvs.SSpure$valid.error)
hyper_gridjuv.SSpure$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.SSpure$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.SSpure)
hyper_gridjuv.SSpure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.7<-larv.fit_gbm_caret.cv_SSpure.hyperTune$resample$Rsquared
boxplot(R2.7)
mean(R2.7)
std.error(R2.7)
#####################(8 Hybrid SS)####-----
Raw_juv2018_filt_temp.metrics5_SS_hyb<-Raw_juv2018_filt_temp.metrics5_SS %>%
filter(Family %in% c(" BKxCU ", " BKxDR ", " BKxLS ", " BKxSB ", " CUxBK ", " CUxDR ",
" CuxLS ", " CUxSB ", " DRxBK ", " DRxCU ", " DRxSB ", " LSxBK ",
" LSxCU ", " LSxSB ", " SBxBK ", " SBxDR ", " SBxLS ", " CUxLS "))
set.seed(88901)
JuvSS.hyb_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_SS_hyb, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.SS_train.hyb <- training(JuvSS.hyb_splits)
set.seed(88901)
Juv18.SS_test.hyb <- testing(JuvSS.hyb_splits)
caret_grid.JUV.hypergrid_SShyb <- data.frame(n.trees = 50, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 5)
set.seed(25)
larv.fit_gbm_caret.cv_SShyb.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.SS_train.hyb, verbose = TRUE,tuneGrid = caret_grid.JUV.hypergrid_SShyb, trControl = fitControl_rando,
distribution="gaussian", na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_SShyb.hyperTune)
summary(larv.fit_gbm_caret.cv_SShyb.hyperTune$finalModel, plotit = TRUE)
hyper_gridjuv.SShyb <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.SShyb)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.SShyb)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.SShyb <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.SS_train.hyb,
n.trees = 3000,
interaction.depth = hyper_gridjuv.SShyb$interaction.depth[i],
shrinkage = hyper_gridjuv.SShyb$shrinkage[i],
n.minobsinnode = hyper_gridjuv.SShyb$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.SShyb$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.SShyb$optimal_trees[i] <- which.min(gbm.tune.juvs.SShyb$valid.error)
hyper_gridjuv.SShyb$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.SShyb$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.SShyb)
hyper_gridjuv.SShyb %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.8<-larv.fit_gbm_caret.cv_SShyb.hyperTune$resample$Rsquared
boxplot(R2.8)
mean(R2.8)
std.error(R2.8)
#####################(9 Purebred SED)####-----
Raw_juv2018_filt_temp.metrics5_SED<-juv6.6_subset %>% filter(Zoox %in% c("SED"))
Raw_juv2018_filt_temp.metrics5_SED_pure<-Raw_juv2018_filt_temp.metrics5_SED %>%
filter(Family %in% c(" BKxBK ", " LSxLS "," CUxCU "," SBxSB "," DRxDR "))
set.seed(88901)
JuvSED.pure_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_SED_pure, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.SED_train.pure <- training(JuvSED.pure_splits)
set.seed(88901)
Juv18.SED_test.pure <- testing(JuvSED.pure_splits)
caret_grid.JUV.hypergrid.SEDpure <- data.frame(n.trees = 30, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 15)
set.seed(25)
larv.fit_gbm_caret.cv_SEDpure.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.SED_train.pure, verbose = TRUE,
tuneGrid = caret_grid.JUV.hypergrid.SEDpure, trControl = fitControl_rando, na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_SEDpure.hyperTune)
summary(larv.fit_gbm_caret.cv_SEDpure.hyperTune$finalModel, plotit = TRUE)
hyper_gridjuv.SEDpure <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.SEDpure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.SEDpure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.SEDpure <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.SED_train.pure, #Raw_juv2018_filt_temp.metrics5_SED_pure,
n.trees = 3000,
interaction.depth = hyper_gridjuv.SEDpure$interaction.depth[i],
shrinkage = hyper_gridjuv.SEDpure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.SEDpure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.SEDpure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.SEDpure$optimal_trees[i] <- which.min(gbm.tune.juvs.SEDpure$valid.error)
hyper_gridjuv.SEDpure$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.SEDpure$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.SEDpure)
hyper_gridjuv.SEDpure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.9<-larv.fit_gbm_caret.cv_SEDpure.hyperTune$resample$Rsquared
boxplot(R2.9)
mean(R2.9)
std.error(R2.9)
#####################(10 Hybrid SED)####-----
Raw_juv2018_filt_temp.metrics5_SED_hyb<-Raw_juv2018_filt_temp.metrics5_SED %>%
filter(Family %in% c(" BKxCU ", " BKxDR ", " BKxLS ", " BKxSB ", " CUxBK ", " CUxDR ",
" CuxLS ", " CUxSB ", " DRxBK ", " DRxCU ", " DRxSB ", " LSxBK ",
" LSxCU ", " LSxSB ", " SBxBK ", " SBxDR ", " SBxLS ", " CUxLS "))
set.seed(88901)
JuvSED.hyb_splits <- initial_split(Raw_juv2018_filt_temp.metrics5_SED_hyb, .99, strata = "PercAlive", na.rm=TRUE)
set.seed(88901)
Juv18.SED_train.hyb <- training(JuvSED.hyb_splits)
set.seed(88901)
Juv18.SED_test.hyb <- testing(JuvSED.hyb_splits)
caret_grid.JUV.hypergrid_SEDhyb <- data.frame(n.trees = 50, interaction.depth = 1, shrinkage = 0.3,
n.minobsinnode = 15)
set.seed(25)
larv.fit_gbm_caret.cv_SEDhyb.hyperTune <-
caret::train(PercAlive ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Juv18.SED_train.hyb, verbose = TRUE,tuneGrid = caret_grid.JUV.hypergrid_SEDhyb, trControl = fitControl_rando,
distribution="gaussian", na.action=na.omit)
plotres(larv.fit_gbm_caret.cv_SEDhyb.hyperTune) #
summary(larv.fit_gbm_caret.cv_SEDhyb.hyperTune$finalModel, plotit = TRUE)
hyper_gridjuv.SEDhyb <- expand.grid(
shrinkage = c(.01, .1, .3),
interaction.depth = c(1, 3, 5),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0 # a place to dump results
)
# total number of combinations
nrow(hyper_gridjuv.SEDhyb)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.SEDhyb)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.juvs.SEDhyb <- gbm(
formula = PercAlive ~ ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.x + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Juv18.SED_train.hyb,
n.trees = 3000,
interaction.depth = hyper_gridjuv.SEDhyb$interaction.depth[i],
shrinkage = hyper_gridjuv.SEDhyb$shrinkage[i],
n.minobsinnode = hyper_gridjuv.SEDhyb$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.SEDhyb$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.SEDhyb$optimal_trees[i] <- which.min(gbm.tune.juvs.SEDhyb$valid.error)
hyper_gridjuv.SEDhyb$min_RMSE[i] <- sqrt(min(gbm.tune.juvs.SEDhyb$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.SEDhyb)
hyper_gridjuv.SEDhyb %>%
dplyr::arrange(min_RMSE) %>%
head(10)
R2.10<-larv.fit_gbm_caret.cv_SEDhyb.hyperTune$resample$Rsquared
boxplot(R2.10)
mean(R2.10)
std.error(R2.10)
#######################################################################################
#####################(1.5 test cross validation procedure)####-----
##larv pure no CU----
Raw_larvae2018_filt_temp.metrics5_pure_noCU<-larv6%>%
filter(Parentage %in% c("BK x BK", "LS x LS","SB x SB","DR x DR"))
set.seed(88901)
Larv.pure_splits_noCU <- initial_split(Raw_larvae2018_filt_temp.metrics5_pure_noCU, .99, strata = "Perc_surv", na.rm=TRUE)
set.seed(88901)
Larv18.train.pure_noCU <- training(Larv.pure_splits_noCU)
set.seed(88901)
Larv18._test.pure_noCU <- testing(Larv.pure_splits_noCU)
# total number of combinations
nrow(hyper_gridjuv.LARVpure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.LARVpure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.larv.pure_noCU <- gbm(
formula = Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Larv18.train.pure_noCU,
n.trees = 3000,
interaction.depth = hyper_gridjuv.LARVpure$interaction.depth[i],
shrinkage = hyper_gridjuv.LARVpure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.LARVpure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.LARVpure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.LARVpure$optimal_trees[i] <- which.min(gbm.tune.larv.pure_noCU$valid.error)
hyper_gridjuv.LARVpure$min_RMSE[i] <- sqrt(min(gbm.tune.larv.pure_noCU$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.LARVpure)
hyper_gridjuv.LARVpure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
caret_grid.larv.hypergrid_purer.testNatComms_noCUsame <- data.frame(n.trees = 388, interaction.depth = 3,
shrinkage = 0.01,n.minobsinnode = 5)
caret_grid.larv.hypergrid_purer.testNatComms_noCUmodif <- data.frame(n.trees = 38, interaction.depth = 3,
shrinkage = 0.3,n.minobsinnode = 5)
set.seed(25)
larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU <-
caret::train(Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT, method = "gbm", data = Larv18.train.pure_noCU, verbose = TRUE,
tuneGrid = caret_grid.larv.hypergrid_purer.testNatComms_noCUmodif, trControl = fitControl_rando, na.action=na.omit)
#interaction depth from recomended 1 to 3 because plot squared error loss flattens out
larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU$results #check params
plotres(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU)
summary(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU$finalModel, plotit = TRUE)
plotmo(larv.fit_gbm_caret.cv_pure.hyperTuneRAW.NatCom.NoCU, pmethod="partdep", all1=TRUE, all2=TRUE) #testx object
###################### larva pure no bk----
Raw_larvae2018_filt_temp.metrics5_pure_noBK<-larv6%>%
filter(Parentage %in% c("CU x CU", "LS x LS","SB x SB","DR x DR"))
set.seed(88901)
Larv.pure_splits_noBK <- initial_split(Raw_larvae2018_filt_temp.metrics5_pure_noBK, .99, strata = "Perc_surv", na.rm=TRUE)
set.seed(88901)
Larv18.train.pure_noBK <- training(Larv.pure_splits_noBK)
set.seed(88901)
Larv18._test.pure_noBK <- testing(Larv.pure_splits_noBK)
# total number of combinations
nrow(hyper_gridjuv.LARVpure)
## [1] 54
for(i in 1:nrow(hyper_gridjuv.LARVpure)) {
# reproducibility
set.seed(123)
# train model
gbm.tune.larv.pure_noBK <- gbm(
formula = Perc_surv ~ts_frame...2.dim.ts_frame..2...x. + Temp + Lat + sd.y + TSA_DHWStandardDeviation + SSTA_FrequencyStandardDeviation +DRT,
distribution = "gaussian",
data = Larv18.train.pure_noBK,
n.trees = 3000,
interaction.depth = hyper_gridjuv.LARVpure$interaction.depth[i],
shrinkage = hyper_gridjuv.LARVpure$shrinkage[i],
n.minobsinnode = hyper_gridjuv.LARVpure$n.minobsinnode[i],
bag.fraction = hyper_gridjuv.LARVpure$bag.fraction[i],
train.fraction = .99,
n.cores = NULL, # will use all cores by default
verbose = TRUE
)
# add min training error and trees to grid
hyper_gridjuv.LARVpure$optimal_trees[i] <- which.min(gbm.tune.larv.pure_noBK$valid.error)
hyper_gridjuv.LARVpure$min_RMSE[i] <- sqrt(min(gbm.tune.larv.pure_noBK$valid.error))
}
#the new values will now be added to the formally empty rows of the hyper_grid
head(hyper_gridjuv.LARVpure)
hyper_gridjuv.LARVpure %>%
dplyr::arrange(min_RMSE) %>%
head(10)
caret_grid.larv.hypergrid_purer.testNatComms_noBKmodif <- data.frame(n.trees = 383, interaction.depth = 3,
shrinkage = 0.01,n.minobsinnode = 5)