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Gg_model_runs_glmm.Rmd
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Gg_model_runs_glmm.Rmd
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---
title: 'Risso''s dolphin habitat models'
output:
html_document:
toc: true
toc_depth: 4
toc_float: true
theme: spacelab
fig_caption: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load packages, include = TRUE, message = FALSE, warning = FALSE}
library(glmmTMB)
library(bbmle)
library(MASS)
library(rgdal)
library(raster)
library(ggplot2)
library(rgeos)
library(mapview)
library(leaflet)
library(psych)
library(broom)
library(plotrix)
library(magrittr)
library(colorRamps)
library(lubridate)
library(HabitatProject)
options(stringsAsFactors = FALSE)
# load some preferences
load('E:/NASData/ModelData/Gg/setup_info_Gg.Rdata')
load('E:/NASData/ModelData/Gg/GgMergedData.Rdata')
outDir <- file.path('E:/NASData/ModelData',SP,'/')
```
# I. Exploratory analysis
## Data Inputs
<br>
The visual data goes back to 1992, but many predictor variables are only available starting in 2003, therefore earlier visual data is currently excluded from further analyses.
```{r Missing data, eval = FALSE}
covarList<-names(mergedSegments[c(2,5:length(mergedSegments))])
percFilled <- plot.missingdata(mergedSegments,covarList,paste0('AcousticAndVisual_',SP))
percFilled <- plot.missingdata(AcOnlySegments,covarList,paste0('AcousticOnly_',SP))
percFilled <- plot.missingdata(VisOnlySegments,covarList,paste0('VisualOnly_',SP))
```
<br>
### Testing and Training Sets
Split datasets into training and test sets. In this case, data from 2009 and 2013 are used for testing. Only observations post-2003 are used for modeling due to covariate limitations.
```{r test train split}
# If you decide from the missing data plots that you want to restrict years going forward:
yearListIdx = as.numeric(format(mergedSegments$date,"%Y"))
yearListIdx_AcOnly = as.numeric(format(AcOnlySegments$date,"%Y"))
yearListIdx_VisOnly = as.numeric(format(VisOnlySegments$date,"%Y"))
keepDates.train <- which(yearListIdx != 2009 & yearListIdx >= 2003 & yearListIdx <= 2012)
keepDates.test <- which(yearListIdx == 2009 | yearListIdx == 2013)
keepDates_AcOnly.train <- which(yearListIdx_AcOnly != 2009 & yearListIdx_AcOnly >= 2003 & yearListIdx_AcOnly <= 2012)
keepDates_AcOnly.test <- which(yearListIdx_AcOnly == 2009 | yearListIdx_AcOnly == 2013)
keepDatesVisOnly.train <- which(yearListIdx_VisOnly != 2009 & yearListIdx_VisOnly >= 2003 & yearListIdx_VisOnly <= 2012)
keepDatesVisOnly.test <- which(yearListIdx_VisOnly == 2009 | yearListIdx_VisOnly == 2013)
mergedTrain.set<- mergedSegments[keepDates.train,]
Train_AcOnly.set <- AcOnlySegments[keepDates_AcOnly.train,]
Train_VisOnly.set<- VisOnlySegments[keepDatesVisOnly.train,]
mergedTest.set<- mergedSegments[keepDates.test,]
Test_AcOnly.set<- AcOnlySegments[keepDates_AcOnly.test,]
Test_VisOnly.set<- VisOnlySegments[keepDatesVisOnly.test,]
```
<br>
### Preliminary Mapping
The visual data selected for modeling are displayed on the map below. Data from 2009 were held back for testing. Blue markers indicate HARP locations.
```{r map inputs, eval = FALSE}
#Get test visual sightings
sightingsTrain <- Train_VisOnly.set[Train_VisOnly.set$Density>0,c('lat','long','date')]
sightingsTest <- Test_VisOnly.set[Test_VisOnly.set$Density>0,c('lat','long','date')]
HARPsites <- unique(Train_AcOnly.set[c('lat','long')])
pal <-colorFactor(palette = "RdYlGn",
domain = c(2003,2004,2009,2012))
map1 <- leaflet() %>% setView(lng = -89.4, lat = 27.0, zoom = 6)%>%
addProviderTiles(providers$Esri.OceanBasemap) %>%
addCircleMarkers(data = sightingsTrain, lng = ~ long, lat = ~ lat,color = ~pal(year(date)),
stroke = TRUE, fillOpacity = 0.8, group = 'Training Set',radius = 7)%>%
addCircleMarkers(data = sightingsTest, lng = ~ long, lat = ~ lat,color = ~pal(year(date)),
stroke = TRUE, fillOpacity = 0.8, group = 'Test Set',radius = 7)%>%
addMarkers(data = HARPsites, lng = ~ long, lat = ~ lat) %>%
addLegend(pal = pal,values = c(2003,2004,2009,2012,2014),title = 'Year')
map1
```
<br>
The following acoustic data are used, with data from 2011 and 2012 used for training, and 2013 data held back for testing.
```{r plot timeseries, message = FALSE, eval = FALSE}
plot.timeseries(siteList,outDir,AcOnlySegments)
```
<br>
## Examine Covariates
### Identify Outliers
Replace extreme outliers (bad data) with NaNs.
```{r remove outliers, message = FALSE}
# Identify and clear problematic outliers
outlierList <-which(mergedSegments$CHL< -10)
mergedSegments$CHL[outlierList] <- NaN
outlierList <-which(mergedSegments$FrontDist_Cayula>800000)
mergedSegments$FrontDist_Cayula[outlierList] <- NaN
outlierList <-which(mergedSegments$Density>10000)
mergedSegments$Density[outlierList] <- NaN
outlierList <-which(AcOnlySegments$CHL< -10)
AcOnlySegments$CHL[outlierList] <- NaN
outlierList <-which(AcOnlySegments$FrontDist_Cayula>800000)
AcOnlySegments$FrontDist_Cayula[outlierList] <- NaN
outlierList <-which(AcOnlySegments$Density>10000)
AcOnlySegments$Density[outlierList] <- NaN
outlierList <-which(VisOnlySegments$CHL< -10)
VisOnlySegments$CHL[outlierList] <- NaN
outlierList <-which(VisOnlySegments$FrontDist_Cayula>800000)
VisOnlySegments$FrontDist_Cayula[outlierList] <- NaN
outlierList <-which(VisOnlySegments$Density>10000)
VisOnlySegments$Density[outlierList] <- NaN
```
<br>
### Check Covariate Distributions
Covariates have different distributions across the observations.
Here are the distributions of covariates from the acoustic observations:
```{r dot plots, eval = FALSE, message = FALSE}
plot.cleveland(mergedTrain.set,covarList,FALSE,paste0('AcousticAndVisual_',SP))
plot.cleveland(Train_AcOnly.set,covarList,FALSE,paste0('AcousticOnly_',SP))
plot.cleveland(Train_VisOnly.set,covarList,FALSE,paste0('VisualOnly_',SP))
```
Here are the distributions of covariates from the visual observations:
<br>
Some of these covariates are more or less interrelated. HYCOM estimates of salinity at the surface (HYCOM_SALIN_0) and at 100m (HYCOM_SALIN_100) are very similar, so I selected surface salinity for simplicity. HYCOM current and upwelling estimates at 100m were also removed (HYCOM_MAG_100,HYCOM_UPVEL_100). HYCOM current direction is site specific in the acoustic data, so it was excluded for now.
Remaining correlations are examined in the figure below. Numbers closer to 1 above the diagonal in the figure below represent correlation coefficients. Highly correlated covariates should not be used together in the same model.
<br>
### Transform Predictor Variables
Some variables, including chlorophyll and distance to fronts are highly skewed and can be log-transformed.
```{r transform covars}
covarList2 <- c("SST","SSH","CHL","HYCOM_MLD",
"HYCOM_SALIN_0","HYCOM_DIR_0",
"HYCOM_MAG_0",
"HYCOM_UPVEL_50","FrontDist_Cayula",
"EddyDist","Neg_EddyDist","DayOfYear",
"fac1")
transformList <- c("none","none","log10","log10",
"none","none",
"log10",
"none","log10","none",
"none","none",
"none")
# restrict covariates again to limited set
mergedTrain.set2<- mergedTrain.set[,covarList2]
mergedTest.set2<- mergedTest.set[,covarList2]
Train_AcOnly.set2<- Train_AcOnly.set[,covarList2]
Test_AcOnly.set2<- Test_AcOnly.set[,covarList2]
Train_VisOnly.set2<- Train_VisOnly.set[,covarList2]
Test_VisOnly.set2<- Test_VisOnly.set[,covarList2]
transformedCovars.train <- transform.covars(mergedTrain.set2,
covarList2,transformList)
transformedCovars.test <- transform.covars(mergedTest.set2,
covarList2,transformList)
transformedCovars_AcOnly.train <-
transform.covars(Train_AcOnly.set2,covarList2,transformList)
transformedCovars_AcOnly.test <-
transform.covars(Test_AcOnly.set2,covarList2,transformList)
transformedCovars_VisOnly.train <-
transform.covars(Train_VisOnly.set2,covarList2,transformList)
transformedCovars_VisOnly.test <-
transform.covars(Test_VisOnly.set2,covarList2,transformList)
```
```{r transformed dot plots, eval = FALSE, message = FALSE}
plotCols = colnames(transformedCovars.train)[1:length(covarList2)-1]
plot.cleveland(transformedCovars.train,plotCols,
paste0('AcousticAndVisual_',SP))
plot.cleveland(transformedCovars_AcOnly.train,
plotCols,TRUE,paste0('AcousticOnly_',SP))
plot.cleveland(transformedCovars_VisOnly.train,
plotCols,TRUE,paste0('VisualOnly_',SP))
```
<br>
Below, the two sets of covariates have been combined and transformed.
<br>
### Check Predictors
To get an idea of the basic predictive power of these covariates, we can look at presence/absence relative to each variable. This also provides an opportunity to look at the range of values observed for each covariate in the visual and acoustic datasets. In the plots below dotted lines indicate the distribution of each covariate when animals were present, and solid lines indicate the distribution when animals were absent. Note that these plots do not account for effort.
```{r pres abs plots, message = FALSE, eval = FALSE}
# without transform
png(paste(outDir,SP,'_correlations_noTransform.png',sep=''), width = 2000, height = 1600)
pairs.panels(mergedTrain.set2[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
png(paste(outDir,SP,'_correlations_noTransform_AcOnly.png',sep=''), width = 2000, height = 1600)
pairs.panels(Train_AcOnly.set2[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
png(paste(outDir,SP,'_correlations_noTransform_visOnly.png',sep=''), width = 2000, height = 1600)
pairs.panels(Train_VisOnly.set2[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
# with transform
png(paste(outDir,SP,'_correlations_withTransform.png',sep=''), width = 2000, height = 1600)
pairs.panels(transformedCovars.train[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
png(paste(outDir,SP,'_correlations_withTransform_AcOnly.png',sep=''), width = 2000, height = 1600)
pairs.panels(transformedCovars_AcOnly.train[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
png(paste(outDir,SP,'_correlations_withTransform_visOnly.png',sep=''), width = 2000, height = 1600)
pairs.panels(transformedCovars_VisOnly.train[,1:length(covarList2)-1], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
```
<br>
# II. Model Fitting
```{r load starting data}
weeklyOccurrenceFile = paste0(outDir,'ALLSITES_weeklyPOccurrence_Gg_jahStart.csv')
pOccur <- read.csv(weeklyOccurrenceFile, header = TRUE,na.strings=c('',' ','NA','NaN'))
```
<br>
Initialize model parameters
```{r model setup}
# Run & evaluate models
kVal <- 5 # choose a low value to avoid overfitting
yAcOnly <- round(Train_AcOnly.set$Density)
yVisOnly <- round(Train_VisOnly.set$Density)
y <- as.logical(mergedTrain.set$Density)
# initialize empty structure for model storage
gamm_full_AcOnly<-NULL
glmm_full_VisOnly<-NULL
glmm_full<-NULL
```
<br>
Calculate weights to use in combined visual/acoustic model.
```{r Calculate weights}
# Compute weights
myCat <- as.factor(mergedTrain.set$Category)
myWeights <- rep(0,times = length(myCat))
AcTrainSize <- length(which(myCat==2))
VisTrainSize <- length(which(myCat==1))
if (is.null(weight_Ac)|is.null(weight_Vis)){
# give equal weight to the two datasets
if (AcTrainSize>=VisTrainSize){
weight_Vis = round(AcTrainSize/VisTrainSize)
weight_Ac = 1
} else {
weight_Vis = 1
weight_Ac = round(VisTrainSize/AcTrainSize)
}
}
myWeights[which(myCat==1)] <- weight_Vis
myWeights[which(myCat==2)] <- weight_Ac
cat(paste0('Visual data point weight: ', weight_Vis,
'\nAcoustic data point weight: ', weight_Ac))
```
<br>
## Run Full Models
Run full binomial GAMs on Acoustic only, Visual only, and combined Acoustic/Visual datasets.
<br>
### Model 1
- Negative binomial distribution,
- corAR1 correlation structure,
- Include distance to both cyclonic and anticyclonic eddies,
- ts splines
```{r AC model 1, eval=FALSE}
gamm_full_AcOnly$v01 <- glmmTMB(yAcOnly~ SST+SSH+EddyDist+log10_HYCOM_MAG_0+
log10_FrontDist_Cayula+HYCOM_SALIN_0+log10_HYCOM_MLD,
ziformula = ~fac1,
family = poisson,
na.action = na.omit,
data = transformedCovars_AcOnly.train)
```
```{r other models 1, eval = FALSE, echo=FALSE, include = FALSE}
gamm_full_VisOnly_TF$v01 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v01 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = negbin(10),
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 20)
```
<br>
### Model 2
Substitute regular binomial distribution:
```{r AC model 2, eval=FALSE}
gamm_full_AcOnly_TF$v02 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))#
```
```{r other models 2, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v02 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v02 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = binomial(),
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 20)
```
<br>
### Model 3
Try a quasibinomial distribution:
```{r AC model 3, eval=FALSE}
gamm_full_AcOnly_TF$v03 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
```
```{r Other models 3, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v03 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v03 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 50)
```
<br>
### Model 4
Repeat the above models, but substitute negative eddy distances instead of looking at all eddies:
```{r AC model 4, eval=FALSE}
gamm_full_AcOnly_TF$v04 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
```
```{r Other models 4, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v04 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v04 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = negbin(10),
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 20)
```
<br>
### Model 5
Substitute both negative eddy distances AND binomial distribution:
```{r AC model 5, eval=FALSE}
gamm_full_AcOnly_TF$v05 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
```
```{r Other models 5, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v05 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v05 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = binomial,
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 20)
```
<br>
### Model 6
Substitute negative eddy distance with quasibinomial distribution.
```{r AC model 6, eval=FALSE}
gamm_full_AcOnly_TF$v06 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))#
```
```{r Other models 6, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v06 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v06 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 50)
```
<br>
### Model 7
Try adding a random effects structure:
```{r AC model 7, eval=FALSE}
gamm_full_AcOnly_TF$v07 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))#
```
```{r Other models 7, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v07 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))
gamm_full_TF$v07 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = negbin(10),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 8
Again substituting binomial distribution, retaining random effects.
```{r AC model 8, eval=FALSE}
gamm_full_AcOnly_TF$v08 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1), random=list(fac1=~1))
```
```{r Other models 8, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v08 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1), random=list(fac1=~1))
gamm_full_TF$v08 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = binomial(),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 9
Now substituting quasibinomial distribution, retaining random effects.
```{r AC model 9, eval=FALSE}
gamm_full_AcOnly_TF$v09 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))
```
```{r Other models 9, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v09 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1), random=list(fac1=~1))
gamm_full_TF$v09 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 10
Try distances to negative eddies only, negative binomial, retaining random effects structure:
```{r AC model 10, eval=FALSE}
gamm_full_AcOnly_TF$v10 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))#
```
```{r Other models 10, eval = FALSE, echo = FALSE}
gamm_full_VisOnly_TF$v10 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))
gamm_full_TF$v10 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = negbin(10),
control = list(opt='optim',maxIter = 100,msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 11
Try negative eddies with binomial distribution and random effects:
```{r AC model 11, eval=FALSE}
gamm_full_AcOnly_TF$v11 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))
```
```{r Other models 11, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v11 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = binomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1), random=list(fac1=~1))
gamm_full_TF$v11 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = binomial(),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 12
Try negative eddies with quasibinomial distribution and random effects:
```{r AC model 12, eval=FALSE}
gamm_full_AcOnly_TF$v12 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1))
```
```{r Other models 12, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v12 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim', niterEM=0),
correlation = corAR1(form=~1|fac1), random=list(fac1=~1))
gamm_full_TF$v12 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(SSH, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(Neg_EddyDist, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = quasibinomial(),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),
random=list(fac1=~1),niterPQL = 50)
```
<br>
### Model 13
Try eddy distance and SSH as an interaction term instead of negative vs. positive eddies, with negative binomial distribution:
```{r AC model 13, eval=FALSE}
gamm_full_AcOnly_TF$v13 <- gamm(yAcOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist,SSH, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim'),
correlation = corAR1(form=~1|fac1))
```
```{r Other models 13, eval = FALSE, echo = FALSE, include = FALSE}
gamm_full_VisOnly_TF$v13 <- gamm(yVisOnly_TF~ s(SST, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist,SSH, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars_VisOnly.train,
na.action = na.omit,family = negbin(10),
control = list(opt='optim'),
correlation = corAR1(form=~1|fac1))
gamm_full_TF$v13 <- gamm(y_TF~ s(SST, bs='ts', k=kVal)
+ s(log10_CHL, bs='ts', k=kVal)
+ s(EddyDist, SSH, bs='ts', k=kVal)
+ s(log10_HYCOM_MAG_0,bs='ts', k=kVal)
+ s(HYCOM_SALIN_0, bs='ts', k=kVal)
+ s(log10_FrontDist_Cayula,bs='ts', k=kVal)
+ s(log10_HYCOM_MLD, bs='ts', k=kVal),
data = transformedCovars.train, weights = myWeights,
na.action = na.omit,family = negbin(10),
control = list(opt='optim',maxIter = 100,
msMaxIter = 100,niterEM=0),
correlation = corAR1(form=~1|fac1),niterPQL = 50)
```
<br>
### Model 14
Try eddy distance and SSH as an interaction term with binomial distribution: