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Pm_model_GAM_density_GRIIDC.Rmd
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---
title: 'GoMx sperm whale habitat models: Density with GAMs'
author:
- name: Kaitlin Frasier
affiliation: Scripps Institution of Oceanography, UC San Diego
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
toc: true
toc_depth: 4
toc_float: true
theme: spacelab
fig_caption: true
bibliography: exportlist.bibtex
csl: plos-computational-biology.csl
---
```{r setup, echo = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load packages, echo = FALSE, message = FALSE, warning = FALSE}
library(mgcv)
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)
library(lmtest)
library(zoo)
library(pracma)
library(grid)
library(knitr)
library(matrixStats)
library(zoo)
library(tmap)
library(scales)
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/multiplot.R')
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/plot_missingdata.R')
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/plot_cleveland.R')
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/plot_covarDensity.R')
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/plot_timeseries.R')
source('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/HabitatProject/R/transform_covars.R')
options(stringsAsFactors = FALSE)
# load some preferences
load('F:/GRIIDC_inputs/ModelData/Pm/setup_info_Pm.Rdata')
load('F:/GRIIDC_inputs/ModelData/Pm/PmMergedData.Rdata')
outDir <- file.path('F:/GRIIDC_inputs/ModelData',SP,'GAM_modelOutput//')
inDir <- file.path('F:/GRIIDC_inputs/ModelData',SP,'//')
```
<br>
# 1. Exploratory analysis
<br>
## 1.1 Data Inputs
NOAA SEFSC visual data goes back to 1992, but as shown in the figure below, many predictor variables are only available starting in 2003, therefore earlier visual data is currently excluded from further analyses.
Note: Future work could use monthly climatologies (averages) so that older sightings data could be used. Some dynamic drivers like eddy and front locations would not be able to be considered using that approach.
```{r Missing data, echo = FALSE}
plotCols1 <-c(7:10,14,16,19,22,23)
covarList<-names(mergedSegments[c(plotCols1)])
varUnits <- c("SST (C)","SSH (m)",expression("Chlorophyll A (mg/m"^3*")"),"MLD (m)", "Salinity (ppm)",
"Current Magnitude (m/sec)", "Upwelling (m/sec)",
"Dist. to Anti-Cyclonic Eddy (km)","Dist. to Cyclonic Eddy (km)")
names(varUnits)<-covarList
plot.missingdata(AcOnlySegments,covarList,
paste0(outDir,'AcousticOnly_',SP),varUnits)
plot.missingdata(VisOnlySegments,covarList,
paste0(outDir,'VisualOnly_',SP),varUnits)
visDataAvailPlot <- paste0(outDir,'/VisualOnly_',SP,'_missingData.png')
```
**Visual data predictor variable availability:**
<br>
### 1.1.1 Splitting into testing and training sets
The data are split into training and testing sets. In this case, visual data from 2009 and acoustic data from 2013 were used only for testing. Only observations from 2003 or later were used for modeling due to covariate limitations.
```{r test train split, echo = FALSE}
# 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"))
isVisual <- mergedSegments$Category
keepDates.train <- which(yearListIdx != 2009 &
yearListIdx >= 2003 &
yearListIdx != 2013)
keepDates.test <- which(yearListIdx == 2009 |
yearListIdx == 2013)
keepDates_AcOnly.train <- which(yearListIdx_AcOnly != 2009 &
yearListIdx_AcOnly >= 2003 & yearListIdx_AcOnly <= 2012)
keepDates_AcOnly.test <- which(yearListIdx_AcOnly == 2009 |
yearListIdx_AcOnly == 2013)
keepDatesVisOnly.train <- which(yearListIdx_VisOnly != 2009 &
yearListIdx_VisOnly >= 2003)
keepDatesVisOnly.test <- which(yearListIdx_VisOnly == 2009 |
yearListIdx_VisOnly == 2013)
Train_Joint.set<- mergedSegments[keepDates.train,]
Train_AcOnly.set <- AcOnlySegments[keepDates_AcOnly.train,]
Train_VisOnly.set<- VisOnlySegments[keepDatesVisOnly.train,]
Test_Joint.set<- mergedSegments[keepDates.test,]
Test_AcOnly.set<- AcOnlySegments[keepDates_AcOnly.test,]
Test_VisOnly.set<- VisOnlySegments[keepDatesVisOnly.test,]
```
<br>
### 1.1.2 Map of visual sightings data
The visual data selected for modeling are displayed on the map below. Data from 2009 were held back for testing. Blue markers indicate HARP locations.
```{r map inputs, warning = FALSE, echo = FALSE}
# Get test visual sightings
sightingsTrain <- Train_VisOnly.set[Train_VisOnly.set$Density>0,c('lat','long','date')]
sightingsTest <- Test_VisOnly.set[Test_VisOnly.set$Density>0,c('lat','long','date')]
HARPsites <- unique(Train_AcOnly.set[c('lat','long')])
pal <-colorFactor(palette = "RdYlGn",
domain = c(2003,2004,2009,2012,2014))
map1 <- leaflet() %>% setView(lng = -89.4, lat = 27.0, zoom = 6)%>%
addProviderTiles(providers$Esri.OceanBasemap) %>%
addCircleMarkers(data = sightingsTrain, lng = ~ long, lat = ~ lat,color = ~pal(year(date)),
stroke = TRUE, fillOpacity = 0.8, group = 'Training Set',radius = 4)%>%
addCircleMarkers(data = sightingsTest, lng = ~ long, lat = ~ lat,color = ~pal(year(date)),
stroke = TRUE, fillOpacity = 0.8, group = 'Test Set',radius = 4)%>%
addMarkers(data = HARPsites, lng = ~ long, lat = ~ lat) %>%
addLegend(pal = pal,values = c(2003,2004,2009,2012,2014),title = 'Year')
map1
```
<br>
### 1.1.3 Time series of acoustic data
The time series below show timeseries of estimated densities from passive acoustic data used for modeling (Densities were calculated following methods detailed in @RN806). Data from 2011 and 2012 were used for training, and 2013 data was held back for testing.
<br>
**Acoustic Timeseries:**
```{r plot timeseries, message = FALSE, echo = FALSE}
plot.timeseries(siteList,outDir,AcOnlySegments)
MCTS <- paste0(outDir,SP,'_Timeseries_Site_MC.png')
GCTS <- paste0(outDir,SP,'_Timeseries_Site_GC.png')
DTTS <- paste0(outDir,SP,'_Timeseries_Site_DT.png')
```
<br>
## 1.2 Examination of covariates
```{r remove outliers, message = FALSE, results = 'hide', echo = FALSE}
### Identify Outliers
# Replace extreme outliers (bad data) with NaNs.
outlierList <-which(Train_Joint.set$CHL< -10)
Train_Joint.set$CHL[outlierList] <- NaN
#outlierList <-which(Train_Joint.set$FrontDist_Cayula>800000)
#Train_Joint.set$FrontDist_Cayula[outlierList] <- NaN
outlierList <-which(Train_Joint.set$Density>100000)
Train_Joint.set$Density[outlierList] <- NaN
outlierList <-which(Test_Joint.set$CHL< -10)
Test_Joint.set$CHL[outlierList] <- NaN
#outlierList <-which(Test_Joint.set$FrontDist_Cayula > 800000)
#Test_Joint.set$FrontDist_Cayula[outlierList] <- NaN
outlierList <-which(Test_Joint.set$Density>100000)
Test_Joint.set$Density[outlierList] <- NaN
outlierList <-which(Train_AcOnly.set$CHL< -10)
Train_AcOnly.set$CHL[outlierList] <- NaN
outlierList <-which(Train_AcOnly.set$Density > 100000)
Train_AcOnly.set$Density[outlierList] <- NaN
outlierList <-which(Test_AcOnly.set$CHL< -10)
Test_AcOnly.set$CHL[outlierList] <- NaN
outlierList <-which(Test_AcOnly.set$Density>100000)
Test_AcOnly.set$Density[outlierList] <- NaN
outlierList <-which(Train_VisOnly.set$CHL< -10)
Train_VisOnly.set$CHL[outlierList] <- NaN
outlierList <-which(Train_VisOnly.set$Density>100000)
Train_VisOnly.set$Density[outlierList] <- NaN
outlierList <-which(Test_VisOnly.set$CHL< -10)
Test_VisOnly.set$CHL[outlierList] <- NaN
outlierList <-which(Test_VisOnly.set$Density>100000)
Test_VisOnly.set$Density[outlierList] <- NaN
```
<br>
### 1.2.1 Covariate distribution check
<br>
**Distributions of covariates from acoustic observations (training data only):**
```{r dot plots, eval = TRUE, message = FALSE, results = 'hide', echo = FALSE}
plot.cleveland(Train_AcOnly.set,covarList,FALSE,paste0(outDir,'AcousticOnly_',SP),varUnits)
plot.cleveland(Train_VisOnly.set,covarList,FALSE,paste0(outDir,'VisualOnly_',SP),varUnits)
ACclevelandPlot<-paste0(outDir,'AcousticOnly_',
SP,'_clevelandDots_noTransform.png')
VisclevelandPlot<-paste0(outDir,'VisualOnly_',
SP,'_clevelandDots_noTransform.png')
```
<br>
**Distributions of covariates from the visual observations (training data only):**
<br>
Some of these covariates are more or less interrelated. Correlations are examined in the figure below. Numbers closer to 1 above the diagonal in the figure below represent correlation coefficients. If a pair of covariates is highly-correlated only one should typically be used in the model.
```{r correlation plots, eval = TRUE, message = FALSE, warning = FALSE, results = 'hide', echo = FALSE}
covarList2 <- c("Density","SST","SSH","CHL",
"HYCOM_MLD","HYCOM_SALIN_0","HYCOM_MAG_0",
"HYCOM_UPVEL_50",
"Neg_EddyDist","Pos_EddyDist",
"fac1","fac2","EffectiveArea")
# restrict covariates again to limited set
Train_Joint.set2<- Train_Joint.set[,covarList2]
Test_Joint.set2<- Test_Joint.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]
# without transform
png(paste(outDir,SP,'_correlations_noTransform_AcOnly.png',sep=''), width = 2000, height = 1600)
pairs.panels(Train_AcOnly.set2[,1:(length(covarList2)-3)], 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)-3)], ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
covarPlot <-paste0(outDir,SP,'_correlations_noTransform_AcOnly.png')
```
<br>
**Covariate Correlations:**
<br>
### 1.2.2 Transformation of predictor variables
Some variables, including chlorophyll, mixed layer depth and distance to fronts are highly skewed and were log-transformed for input to GAMs.
```{r transform covars, message = FALSE, results = 'hide', echo = FALSE, warning = FALSE}
# covarList2 <- c("Density","SST","SSH","CHL",
# "HYCOM_MLD","HYCOM_SALIN_0","HYCOM_MAG_0",
# "HYCOM_UPVEL_50",
# "Neg_EddyDist", "PosEddyDist",
# "fac1","fac2")
transformList <- c("none","none","none","log10",
"log10","none","log10",
"none",
"none","none",
"none","none","none")
transformedCovars.train <-
transform.covars(Train_Joint.set2,covarList2,transformList)
transformedCovars.test <-
transform.covars(Test_Joint.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)
# Generate correlation plots with transform
png(paste(outDir,SP,'_correlations_withTransform_AcOnly.png',sep=''),
width = 2000, height = 1600)
pairs.panels(transformedCovars_AcOnly.train[,1:(length(covarList2)-2)],
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)-2)],
ellipses=FALSE, method = "spearman",cex.cor=.75)
dev.off()
```
```{r transformed dot plots, eval = TRUE, message = FALSE, results = 'hide', echo = FALSE}
# Plotting the transformed variables:
plotCols = colnames(transformedCovars_AcOnly.train)[c(2:10)]
names(varUnits)<-plotCols
varUnits["log10_HYCOM_MAG_0"]<-expression("log"[10]*"(Current Magnitude (m/sec))")
varUnits["log10_HYCOM_MLD"]<- expression("log"[10]*"(MLD (m))")
varUnits["log10_CHL"] <-expression("log"[10]*"(Chorophyll A (mg/m"^3*"))")
plot.cleveland(transformedCovars.train,
plotCols,TRUE,paste0(outDir,'AcousticAndVisual_',SP),varUnits)
plot.cleveland(transformedCovars_AcOnly.train,
plotCols,TRUE,paste0(outDir,'AcousticOnly_',SP),varUnits)
plot.cleveland(transformedCovars_VisOnly.train,
plotCols,TRUE,paste0(outDir,'VisualOnly_',SP),varUnits)
```
<br>
### 1.2.3 Preliminary check of predictive power
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 `r SPLong` were present, and solid lines indicate the distribution when `r SPLong` were absent. Note that these plots do not account for effort.
```{r presence absence histograms, eval = TRUE, message = FALSE, results = 'hide', echo = FALSE}
plot.covarDensity(transformedCovars_AcOnly.train[,2:10],
colnames(transformedCovars_AcOnly.train[2:10]),
transformedCovars_AcOnly.train$Density,paste0(outDir,'AcousticOnly_',SP),varUnits)
plot.covarDensity(transformedCovars_VisOnly.train[,2:10],
colnames(transformedCovars_VisOnly.train[,2:10]),
transformedCovars_VisOnly.train$Density,paste0(outDir,'VisualOnly_',SP),varUnits)
acKernels <-paste0(outDir,'/AcousticOnly_',SP,'_density_pres_abs.png')
visKernels <- paste0(outDir,'/VisualOnly_',SP,'_density_pres_abs.png')
```
<br>
**Acoustic kernel densities:**
<br>
**Visual kernel densities:**
<br>
```{r load detection prob, echo = FALSE}
visDetProbData<-paste0(inDir,SP,'sightwTrunc_GU.Rdata')
load(visDetProbData)
visDetProb <- detFun[[bestModelIdx]]$fitted[1]
visDetProbFigure <- paste0(inDir,SP,'sightwTrunc_GU.png')
```
**Best visual detection probability model for `r SPLong`:**
<br>
# 2. Model Fitting
Models were fit using gam from the mgcv package in R [@RN783].
```{r load starting data, echo = FALSE}
pOccur <- read.csv(pOccurenceFile, header = TRUE,na.strings=c('',' ','NA','NaN'))
```
## 2.1 Set up weighting
```{r model setup, echo = FALSE}
# set up weighting
visDetProb <- detFun[[bestModelIdx]]$fitted[1]
transformedCovars_AcOnly.train$yAcOnly <- transformedCovars_AcOnly.train$Density
transformedCovars_VisOnly.train$yVisOnly <- transformedCovars_VisOnly.train$Density
transformedCovars.train$y <- transformedCovars.train$Density
transformedCovars_AcOnly.test$yAcOnly <- transformedCovars_AcOnly.test$Density
transformedCovars_VisOnly.test$yVisOnly <- transformedCovars_VisOnly.test$Density
transformedCovars.test$y <- transformedCovars.test$Density
# Remove NaNs
goodData_Ac <- which(!is.na(rowSums(transformedCovars_AcOnly.train)))
AcOnly.train.NoNa <- transformedCovars_AcOnly.train[goodData_Ac,]
goodData_Vis <- which(!is.na(rowSums(transformedCovars_VisOnly.train)))
VisOnly.train.NoNa <- transformedCovars_VisOnly.train[goodData_Vis,]
goodData_Joint <- which(!is.na(rowSums(transformedCovars.train)))
Joint.train.NoNa <- transformedCovars.train[goodData_Joint,]
goodData_Ac_test <- which(!is.na(rowSums(transformedCovars_AcOnly.test)))
AcOnly.test.NoNa <- transformedCovars_AcOnly.test[goodData_Ac_test,]
goodData_Vis_test <- which(!is.na(rowSums(transformedCovars_VisOnly.test)))
VisOnly.test.NoNa <- transformedCovars_VisOnly.test[goodData_Vis_test,]
goodData_Joint_test <- which(!is.na(rowSums(transformedCovars.test)))
Joint.test.NoNa <- transformedCovars.test[goodData_Joint_test,]
kVal <- 3
gam_full_AcOnly<- NULL
gam_full_VisOnly<- NULL
# save ranges of each scaled covariate in training set (max and min)
JointRangesMax <- apply(transformedCovars.train, MARGIN = 2,
function(x) max(x, na.rm =TRUE))
JointRangesMin <- apply(transformedCovars.train, MARGIN = 2,
function(x) min(x, na.rm =TRUE))
AcOnlyRangesMax <- apply(transformedCovars_AcOnly.train, MARGIN = 2,
function(x) max(x, na.rm=TRUE))
AcOnlyRangesMin <- apply(transformedCovars_AcOnly.train, MARGIN = 2,
function(x) min(x, na.rm=TRUE))
VisOnlyRangesMax <- apply(transformedCovars_VisOnly.train, MARGIN = 2,
function(x) max(x, na.rm =TRUE))
VisOnlyRangesMin <- apply(transformedCovars_VisOnly.train, MARGIN = 2,
function(x) min(x, na.rm=TRUE))
```
## 2.2 Run GAMs
Run GAMs on acoustic and visual datasets.
```{r AcOnly model, eval = TRUE, echo = TRUE}
varNames <- names(transformedCovars_AcOnly.train)
varNamesFormula <- varNames[2:10]
formulaAcOnly_allVars <-paste('s(', varNamesFormula, ", bs = 'ts', k = kVal)", sep = "", collapse = ' + ')
formulaAcOnly_allVars <- as.formula(paste('yAcOnly ~',formulaAcOnly_allVars))
gam_full_AcOnly <- gam(formulaAcOnly_allVars,
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = tw())
```
```{r VisOnly model, eval = TRUE, include = TRUE, echo = TRUE}
formulaVisOnly_allVars <-paste('s(', varNamesFormula, ", bs = 'ts', k = kVal)", sep = "", collapse = ' + ')
formulaVisOnly_allVars <- as.formula(paste('yVisOnly ~',formulaVisOnly_allVars))
gam_full_VisOnly <- gam(formulaVisOnly_allVars,
data = transformedCovars_VisOnly.train,
na.action = na.omit,
family = Tweedie(p = 1.5, link = log))
```
```{r save models, eval = TRUE, echo = FALSE, include = FALSE}
# Save models if re-calculating everything
save(gam_full_AcOnly,file = paste(outDir,SP,'_AcOnly_tw_GAMs_Density_noWeights.Rdata',sep=''))
save(gam_full_VisOnly,file = paste(outDir,SP,'_VisOnly_tw_GAMs_Density_noWeights.Rdata',sep=''))
```
```{r load models, eval = TRUE, echo = FALSE, include = FALSE}
# alternative if models are already calculated
load(paste(outDir,SP,'_AcOnly_tw_GAMs_Density_noWeights.Rdata',sep=''))
load(paste(outDir,SP,'_VisOnly_tw_GAMs_Density_noWeights.Rdata',sep=''))
```
<br>
# 3. Model Summaries
Model summaries show that some terms were not significant.
## 3.1 Acoustic-only model
<br>
Model summary:
```{r AcOnly unpruned model summary, echo = FALSE}
AcOnlySummary <- summary(gam_full_AcOnly)
# AcOnlySummary
significantPvals_AcOnly <- which(AcOnlySummary$s.pv<=.05)
# rerun model with only significant predictors
formulaAcOnly_prunedVars <-paste('s(', varNamesFormula[significantPvals_AcOnly], ", bs = 'ts', k = kVal)", sep = "", collapse = ' + ')
formulaAcOnly_prunedVars <- as.formula(paste('yAcOnly ~',formulaAcOnly_prunedVars))
AcOnly_model <- gam(formulaAcOnly_prunedVars,
data = transformedCovars_AcOnly.train,
na.action = na.omit,family = tw())
summary(AcOnly_model)
```
<br>
Smooths of predictor variables:
```{r AcOnly model plots, echo = FALSE}
nTerms <- size(AcOnly_model$smooth,2)
png(paste(outDir,SP,'_AcOnly_smooths_density.png',sep=''), res = 300, units = "in", width = 6.5, height = ceil(nTerms/3)*2)
varUnitsAc <-varNamesFormula[significantPvals_AcOnly]
par(mfrow = c(ceil(nTerms/3),3),mar = c(4,2,1,1))
for (i in 1:nTerms) {
plot(AcOnly_model,select = i,
cex.lab = 1.3,cex.axis = 1.1,scale=0,xlab = varUnitsAc[i], ylab = "")
}
dev.off()
smoothFig <-paste0(outDir,SP,'_AcOnly_smooths_density.png')
```
<br>
Plot of residuals:
```{r AcOnly model residuals, echo = FALSE}
plot(residuals.gam(AcOnly_model))
# gam.check(AcOnly_gam_pruned_TF_best)
```
<br>
## 3.2 Visual-only model
<br>
Model summary:
```{r VisOnly model summary, echo = FALSE, warning = FALSE}
VisOnlySummary <- summary(gam_full_VisOnly)
# VisOnlySummary
significantPvals_VisOnly <- which(VisOnlySummary$s.pv<=.05)
# rerun model with only significant predictors
formulaVisOnly_prunedVars <-paste('s(', varNamesFormula[significantPvals_VisOnly], ", bs = 'ts', k = kVal)", sep = "", collapse = ' + ')
formulaVisOnly_prunedVars <- as.formula(paste('yVisOnly ~',formulaVisOnly_prunedVars))
VisOnly_model <- gam(formulaVisOnly_prunedVars,
data = transformedCovars_VisOnly.train,
family = Tweedie(p = 1.5, link = log))
summary(VisOnly_model)
```
<br>
Smooths of predictor variables:
```{r VisOnly model plots, echo = FALSE}
nTerms <- size(VisOnly_model$smooth,2)
png(paste(outDir,SP,'_VisOnly_smooths_density.png',sep=''), res = 300, units = "in", width = 6.5, height = ceil(nTerms/3)*2)
par(mfrow = c(ceil(nTerms/3),3),mar = c(4,2,1,1))
for (i in 1:nTerms) {
plot(VisOnly_model,select = i,
cex.lab = 1.3,cex.axis = 1.1,scale=0,xlab = varUnits[i], ylab = "")
}
dev.off()
smoothFig <-paste0(outDir,SP,'_VisOnly_smooths_density.png')
```
<br>
Plot of residuals:
```{r VisOnly model residuals, echo = FALSE}
plot(residuals.gam(VisOnly_model))
# gam.check(VisOnly_gam_pruned_TF_best)
```
<br>
## 3.4 Model prediction RMSE
```{r compare model predictions, echo = FALSE}
# predict on visual data
pr <- NULL
pr$AcOnly_VisData <- predict.gam(AcOnly_model,
transformedCovars_VisOnly.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
pr$VisOnly_VisData <- predict.gam(VisOnly_model,
transformedCovars_VisOnly.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
RMSE <- NULL
RMSE$AcOnly_test_VisData <- sqrt(mean((as.vector(transformedCovars_VisOnly.test$yVisOnly )-
pr$AcOnly_VisData$fit)^2,na.rm = TRUE))
RMSE$VisOnly_test_VisData <- sqrt(mean((as.vector(transformedCovars_VisOnly.test$yVisOnly )-
pr$VisOnly_VisData$fit)^2,na.rm = TRUE))
# predict on acoustic data
pr$AcOnly_AcData <- predict.gam(AcOnly_model,
transformedCovars_AcOnly.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
pr$VisOnly_AcData <- predict.gam(VisOnly_model,
transformedCovars_AcOnly.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
RMSE$AcOnly_test_AcData <- sqrt(mean((as.vector(transformedCovars_AcOnly.test$yAcOnly )-
pr$AcOnly_AcData$fit)^2,na.rm = TRUE))
RMSE$VisOnly_test_AcData <- sqrt(mean((as.vector(transformedCovars_AcOnly.test$yAcOnly )-
pr$VisOnly_AcData$fit)^2,na.rm = TRUE))
# predict on joint data
pr$AcOnly_JointData <- predict.gam(AcOnly_model,
transformedCovars.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
pr$VisOnly_JointData <- predict.gam(VisOnly_model,
transformedCovars.test,
type = 'response',na.action = na.pass,se.fit=TRUE)
RMSE$AcOnly_test_JointData <- sqrt(mean((as.vector(transformedCovars.test$y )-
pr$AcOnly_JointData$fit)^2,na.rm = TRUE))
RMSE$VisOnly_test_JointData <- sqrt(mean((as.vector(transformedCovars.test$y )-
pr$VisOnly_JointData$fit)^2,na.rm = TRUE))
RMSETable <- rbind(RMSE$AcOnly_test_AcData,RMSE$AcOnly_test_VisData,RMSE$AcOnly_test_JointData,
RMSE$VisOnly_test_AcData,RMSE$VisOnly_test_VisData,RMSE$VisOnly_test_JointData)
colnames(RMSETable)<- 'RMSE'
rownames(RMSETable)<- c("Acoustic - Test Acoustic Data",
"Acoustic - Test Visual Data",
"Acoustic - Test Joint Data",
"Visual - Test Acoustic Data",
"Visual - Test Visual Data",
"Visual - Test Joint Data"
)
print('RMSE scores (lower is better)')
print(round(RMSETable, digits = 3))
```
<br>
# 4. Model Predictions
## 4.1 Temporal predictions
Predictions were made on the acoustic test dataset, and compared with actual observations for 2013. The predicted density of animals at each site was compared with the estimated daily density from the pasive acoustic record.
<br>
### 4.1.1 Acoustic-only prediction
```{r Ac Temporal Predict, echo = FALSE, warning = FALSE, message = FALSE}
# Predict on acoustic test data, using acoustic only model for comparison...
allCovars <- NA
allCovars <- rbind(transformedCovars_AcOnly.train,
transformedCovars_AcOnly.test)
allCovars$date <- c(Train_AcOnly.set$date,Test_AcOnly.set$date)
compAcSet_MC_all <- which((allCovars$fac2)<=5)
compAcSet_GC_all <- which((allCovars$fac2)>5 & (allCovars$fac2)<=10)
compAcSet_DT_all <- which((allCovars$fac2)>10 &
(allCovars$fac2)<=16)
allObservations<-c(Train_AcOnly.set$Density,
Test_AcOnly.set$Density)
allTheDays = seq(min(Train_AcOnly.set$date),max(Test_AcOnly.set$date), by="days")
# Predict in time
dateTicks = as.POSIXct(c('2013-01-01 GMT','2013-04-01 GMT',
'2013-07-01 GMT','2013-10-01 GMT',
'2014-01-01 GMT'))
dateLabels = c('Jan. 2013','Apr. 2013','Jul. 2013','Oct 2013','Jan. 2014')
predAc<- (predict.gam(AcOnly_model,
allCovars,
type = 'response',na.action = na.pass,se.fit=TRUE))
allCovars$A_pred <- predAc$fit
allCovars$A_predSd <- predAc$se.fit
allCovars$observations <- allObservations
allCovars$observations_smooth <- rollmean(allObservations,k = 7,fill=NA)
Ac_predictionSet <- NA
Ac_predictionSet<- data.frame(allTheDays)
MC_times <- allCovars$date[compAcSet_MC_all]
m1 <- match(MC_times,allTheDays)
Ac_predictionSet$MC<- NA
Ac_predictionSet$MC[m1] <- allCovars$A_pred[compAcSet_MC_all]
Ac_predictionSet$predSd_MC<- NA
Ac_predictionSet$predSd_MC[m1] <- allCovars$A_predSd[compAcSet_MC_all]
Ac_predictionSet$MC_data_smooth <- NA
Ac_predictionSet$MC_data_smooth[m1] <- rollmean(allCovars$observations[compAcSet_MC_all],k = 7,fill=NA)
Ac_predictionSet$MC_data <- NA
Ac_predictionSet$MC_data[m1] <- allCovars$observations[compAcSet_MC_all]
Ac_predictionSet$Legend1 <- "1 Observations"
Ac_predictionSet$Legend <- "2 Observations (mean)"
Ac_predictionSet$LegendTest <- "3 Acoustic"
GC_times <- allCovars$date[compAcSet_GC_all]
m2 <- match(GC_times,allTheDays)
Ac_predictionSet$GC<- NA
Ac_predictionSet$GC[m2] <- allCovars$A_pred[compAcSet_GC_all]
Ac_predictionSet$predSd_GC<- NA
Ac_predictionSet$predSd_GC[m2] <- allCovars$A_predSd[compAcSet_GC_all]
Ac_predictionSet$GC_data_smooth <- NA
Ac_predictionSet$GC_data_smooth[m2] <- rollmean(allCovars$observations[compAcSet_GC_all],k = 7,fill=NA)
Ac_predictionSet$GC_data <- NA
Ac_predictionSet$GC_data[m2] <- allCovars$observations[compAcSet_GC_all]
DT_times <- allCovars$date[compAcSet_DT_all]
m3 <- match(DT_times,allTheDays)
Ac_predictionSet$DT<- NA
Ac_predictionSet$DT[m3] <- allCovars$A_pred[compAcSet_DT_all]
Ac_predictionSet$predSd_DT<- NA
Ac_predictionSet$predSd_DT[m3] <- allCovars$A_predSd[compAcSet_DT_all]
Ac_predictionSet$DT_data_smooth <- NA
Ac_predictionSet$DT_data_smooth[m3] <- rollmean(allCovars$observations[compAcSet_DT_all],k = 7,fill=NA)
Ac_predictionSet$DT_data <- NA
Ac_predictionSet$DT_data[m3] <- allCovars$observations[compAcSet_DT_all]
allCovars$Legend <- "Predictions"
allCovars$DataGroup <- "Observations"
allCovars$DE <- "Density"
```
**Predicted and observed densities at passive acoustic monitoring sites:**
```{r,fig.height = 10, fig.width = 7, message = FALSE, warning = FALSE, echo = FALSE}
# par(mfrow = c(5,1),oma=c(3,0,5,0)),colour="#000099",colour="#CC0000"
mcLabel <- grobTree(textGrob("MC", x=0.01, y=0.90, hjust=0,
gp=gpar(col="black", fontsize=13, fontface="bold")))
gcLabel <- grobTree(textGrob("GC", x=0.01, y=0.90, hjust=0,
gp=gpar(col="black", fontsize=13, fontface="bold")))
dtLabel <- grobTree(textGrob("DT", x=0.01, y=0.90, hjust=0,
gp=gpar(col="black", fontsize=13, fontface="bold")))
dcLabel <- grobTree(textGrob("DC", x=0.01, y=0.90, hjust=0,
gp=gpar(col="black", fontsize=13, fontface="bold")))
mpLabel <- grobTree(textGrob("MP", x=0.01, y=0.90, hjust=0,
gp=gpar(col="black", fontsize=13, fontface="bold")))
```
### 4.1.2 Visual-only prediction
```{r Vis Temporal Predict, warning = FALSE, echo = FALSE}
# Confusing, but acoustic only predictors are passed in to since
# we're predicting on the acoustic timeseries.
# Predict in time
dateTicks = as.POSIXct(c('2013-01-01 GMT','2013-04-01 GMT',
'2013-07-01 GMT','2013-10-01 GMT',
'2014-01-01 GMT'))
dateLabels = c('Jan. 2013','Apr. 2013','Jul. 2013','Oct 2013','Jan. 2014')
predVis<- (predict.gam(VisOnly_model,
allCovars,
type = 'response',na.action = na.pass,se.fit=TRUE))
allCovars$V_pred <- predVis$fit
allCovars$V_predSd <- predVis$se.fit
Vis_predictionSet <- NA
Vis_predictionSet<- data.frame(allTheDays)
MC_times <- allCovars$date[compAcSet_MC_all]
m1 <- match(MC_times,allTheDays)
Vis_predictionSet$MC<- NA
Vis_predictionSet$MC[m1] <- allCovars$V_pred[compAcSet_MC_all]
Vis_predictionSet$predSd_MC<- NA
Vis_predictionSet$predSd_MC[m1] <- allCovars$V_predSd[compAcSet_MC_all]
Vis_predictionSet$MC_data_smooth <- NA
Vis_predictionSet$MC_data_smooth[m1] <- rollmean(allCovars$observations[compAcSet_MC_all],k = 7,fill=NA)
GC_times <- allCovars$date[compAcSet_GC_all]
m2 <- match(GC_times,allTheDays)
Vis_predictionSet$GC<- NA
Vis_predictionSet$GC[m2] <- allCovars$V_pred[compAcSet_GC_all]
Vis_predictionSet$predSd_GC<- NA
Vis_predictionSet$predSd_GC[m2] <- allCovars$V_predSd[compAcSet_GC_all]
Vis_predictionSet$GC_data_smooth <- NA
Vis_predictionSet$GC_data_smooth[m2] <- rollmean(allCovars$observations[compAcSet_GC_all],k = 7,fill=NA)
DT_times <- allCovars$date[compAcSet_DT_all]
m3 <- match(DT_times,allTheDays)
Vis_predictionSet$DT<- NA
Vis_predictionSet$DT[m3] <- allCovars$V_pred[compAcSet_DT_all]
Vis_predictionSet$predSd_DT<- NA
Vis_predictionSet$predSd_DT[m3] <- allCovars$V_predSd[compAcSet_DT_all]
Vis_predictionSet$DT_data_smooth <- NA
Vis_predictionSet$DT_data_smooth[m3] <- rollmean(allCovars$observations[compAcSet_DT_all],k = 7,fill=NA)
Vis_predictionSet$Legend1 <- "Observations"
Vis_predictionSet$StDev <- "Std. Error"
Vis_predictionSet$Legend <- "Predictions - Train"
Vis_predictionSet$LegendTest <- "4 Predictions - Test"
```
**Predicted and observed densities at passive acoustic monitoring sites using the visual-only model:**
```{r,fig.height = 10, fig.width = 7, message = FALSE, warning = FALSE, echo = FALSE}
p1 <- ggplot() +
geom_rect(data =Vis_predictionSet, aes(xmin=allTheDays[731], xmax=allTheDays[1095], ymin=0, ymax=25), fill="gray85")+
geom_col(data = Ac_predictionSet,
aes(x = allTheDays, y = MC_data, color = Legend1), alpha = 1,width = .1,size = .25,key_glyph = "rect")+
geom_line(data = Ac_predictionSet,
aes(x = allTheDays, y = MC_data_smooth, color = Legend), alpha = 1,width = .1,size = .25,key_glyph = "rect")+
geom_line(data = Ac_predictionSet,
aes(x=allTheDays, y=MC, color = LegendTest), size = .5,key_glyph = "rect") +
geom_line(data = Vis_predictionSet,
aes(x=allTheDays, y=MC, color = LegendTest),size = .5,key_glyph = "rect") +
labs(y = "", x = NULL)+coord_cartesian(ylim = c(0,45))+ theme_bw()+
theme(legend.position=c(.90,.7),legend.title=element_blank(),
legend.text = element_text(size = 6),axis.title.y = element_text(size = 8,),
plot.margin = unit(c(0,0,10,9), "pt"),
axis.text.x = element_blank(),
legend.key=element_rect(fill=NA),
legend.box.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt"))+
scale_color_manual(labels = c("Observations","Obs. (7-day smooth)","Model - Acoustic","Model - Visual"),
values= c("grey46","gray34","deepskyblue","red2"),
guide = guide_legend(keyheight = unit(1, units = "mm") , keywidth = unit(2, units = "mm")))+
annotation_custom(mcLabel)
p2 <- ggplot() +
geom_rect(data =Vis_predictionSet, aes(xmin=allTheDays[731], xmax=allTheDays[1095], ymin=0, ymax=25), fill="gray85")+
geom_col(data = Ac_predictionSet,
aes(x = allTheDays, y = GC_data, color = Legend1), alpha = 1,width = .1,size = .25,key_glyph = "rect")+
geom_line(data = Ac_predictionSet,
aes(x = allTheDays, y = GC_data_smooth, color = Legend), alpha = 1,width = .1,size = .25,key_glyph = "rect")+
geom_line(data = Ac_predictionSet,
aes(x=allTheDays, y=GC, color = LegendTest), size = .5,key_glyph = "rect") +
geom_line(data = Vis_predictionSet,
aes(x=allTheDays, y=GC, color = LegendTest),size = .3,key_glyph = "rect") +
labs(y = "", x = NULL)+coord_cartesian(ylim = c(0,25))+ theme_bw()+
scale_color_manual(labels = c("Observations","Obs. (7-day smooth)","Model - Acoustic","Model - Visual"),
values= c("grey46","gray34","deepskyblue","red2"),
guide = guide_legend(keyheight = unit(1, units = "mm") , keywidth = unit(2, units = "mm")))+
theme(legend.position="none", axis.title.y = element_text(size = 8,),panel.spacing.y=unit(0, "lines"),plot.margin = unit(c(0,0,10,9), "pt"),axis.text.x = element_blank())+labs(y =bquote("Density (per 1000"~km^2~")"), x = NULL)+
annotation_custom(gcLabel)
p3 <- ggplot() +
geom_rect(data =Vis_predictionSet, aes(xmin=allTheDays[731], xmax=allTheDays[1095], ymin=0, ymax=25), fill="gray85")+
geom_col(data = Ac_predictionSet,
aes(x = allTheDays, y = DT_data, color = Legend1), alpha = 1,width = .1,size = .25)+
geom_line(data = Ac_predictionSet,
aes(x = allTheDays, y = DT_data_smooth, color = Legend), alpha = 1,width = .1,size = .25,key_glyph ="rect")+
geom_line(data = Ac_predictionSet,
aes(x=allTheDays, y=DT, color = LegendTest), size = .5) +
geom_path(data = Vis_predictionSet,
aes(x=allTheDays, y=DT, color = LegendTest),size = .5) +
labs(y = "", x = NULL)+coord_cartesian(ylim = c(0,25))+
scale_color_manual(labels = c("Observations","Obs. (7-day smooth)","Model - Acoustic","Model - Visual"),
values= c("grey46","gray34","deepskyblue","red2"),
guide = guide_legend(keyheight = unit(1, units = "mm") , keywidth = unit(2, units = "mm")))+theme_bw()+
theme(legend.position="none",axis.title.y = element_text(size = 8,),plot.margin = unit(c(0,0,10,12), "pt"),
axis.text.x = element_blank())+
annotation_custom(dtLabel)
timeSeriesFig = paste(outDir,SP,'_Timeseries_Prediction_GAM_Density.png',sep='')
png(timeSeriesFig, width = 6.6, height = 6.7, units = "in", res=600)
multiplot(p1, p2, p3, cols = 1)
dev.off()
```
<br>
## 4.2 Spatial predictions
```{r load raster bricks, echo = FALSE}
load('F:/NASData/AcoustoVisualDE/AcoustoVisualDE/2009_prediction_rasters_UNscaled.Rdata')
```
Models were evaluated for summer (July 2009) and winter(January 2009) across the entire Gulf of Mexico (US EEZ beyond the 200m contour).
```{r Ac Spatial Predict, warning = FALSE, echo = FALSE}
### Acoustic Only spatial prediction
# set out of range values in rasters to NA
jan2009_rastersAc <- jan2009_rasters
july2009_rastersAc <- july2009_rasters
for (thisVarName in names(jan2009_rastersAc)){
jan2009_rastersAc[[thisVarName]][jan2009_rastersAc[[thisVarName]]<
AcOnlyRangesMin[thisVarName]]<- NA
jan2009_rastersAc[[thisVarName]][jan2009_rastersAc[[thisVarName]]>
AcOnlyRangesMax[thisVarName]]<- NA
july2009_rastersAc[[thisVarName]][july2009_rastersAc[[thisVarName]]<
AcOnlyRangesMin[thisVarName]]<- NA
july2009_rastersAc[[thisVarName]][july2009_rastersAc[[thisVarName]]>
AcOnlyRangesMax[thisVarName]]<- NA
}
jan2009_AcOnly_prediction <- raster::predict(jan2009_rastersAc,
AcOnly_model,
type = 'response',na.action = na.pass)/10
jan2009_AcOnly_prediction_IncludingOutsideObs <- raster::predict(jan2009_rasters,
AcOnly_model,
type = 'response',na.action = na.pass)/10
july2009_AcOnly_prediction <- raster::predict(july2009_rastersAc,
AcOnly_model,
type = 'response',na.action = na.pass)/10
july2009_AcOnly_prediction_IncludingOutsideObs <- raster::predict(july2009_rasters,
AcOnly_model,
type = 'response',na.action = na.pass)/10
# compute standard error raster. Have to convert to points and back to make this work.
july2009_rastersAc_points = na.omit(rasterToPoints(july2009_rastersAc))
july2009_Ac_se_predict_points = predict.gam(AcOnly_model,data.frame(july2009_rastersAc_points),
type = 'response', na.action = na.pass,se.fit = TRUE)
myXY = cbind(july2009_rastersAc_points[,1],july2009_rastersAc_points[,2])
july2009_Ac_prediction_se = rasterize(myXY,july2009_AcOnly_prediction,july2009_Ac_se_predict_points$se.fit,na.rm = TRUE)/10
```
```{r Vis Spatial Predict, warning = FALSE, echo = FALSE}
### Visual Only spatial prediction
jan2009_rastersVis <- jan2009_rasters
july2009_rastersVis <- july2009_rasters
for (thisVarName in names(jan2009_rastersVis)){
jan2009_rastersVis[[thisVarName]][jan2009_rastersVis[[thisVarName]]<
VisOnlyRangesMin[thisVarName]]<- NA
jan2009_rastersVis[[thisVarName]][jan2009_rastersVis[[thisVarName]]>
VisOnlyRangesMax[thisVarName]]<- NA
july2009_rastersVis[[thisVarName]][july2009_rastersVis[[thisVarName]]<
VisOnlyRangesMin[thisVarName]]<- NA
july2009_rastersVis[[thisVarName]][july2009_rastersVis[[thisVarName]]>
VisOnlyRangesMax[thisVarName]]<- NA
}
jan2009_VisOnly_prediction <- raster::predict(jan2009_rastersVis,VisOnly_model,
type = 'response',na.action = na.pass)/10
jan2009_VisOnly_prediction_IncludingOutsideObs <- raster::predict(jan2009_rasters,VisOnly_model,
type = 'response',na.action = na.pass)/10
july2009_VisOnly_prediction <- raster::predict(july2009_rastersVis,VisOnly_model,
type = 'response',na.action = na.pass)/10
july2009_VisOnly_prediction_IncludingOutsideObs <- raster::predict(july2009_rasters,VisOnly_model,
type = 'response',na.action = na.pass)/10
# compute standard error raster. Have to convert to points and back to make this work.
july2009_rastersVis_points = na.omit(rasterToPoints(july2009_rastersVis))
july2009_Vis_se_predict_points = predict.gam(VisOnly_model,data.frame(july2009_rastersVis_points),
type = 'response', na.action = na.pass,se.fit = TRUE)
myXY = cbind(july2009_rastersVis_points[,1],july2009_rastersVis_points[,2])
july2009_Vis_prediction_se = rasterize(myXY,july2009_VisOnly_prediction,july2009_Vis_se_predict_points$se.fit,na.rm = TRUE)/10
```
```{r wrangle projections, message = FALSE, warning = FALSE, echo = FALSE, results = 'hide'}
# Wrangle projections for mapping:
predTemplate <-raster('F:/GRIIDC_inputs/RefRaster.tif')
map_proj <- crs(predTemplate)
crop_limits <- readOGR('F:/GRIIDC_inputs/prediction_polygon.shp',verbose=FALSE)
crop_limits_proj <- spTransform(crop_limits, CRSobj = map_proj)
# Acoustic
jan2009_AcOnly_prediction_crop <- mask(jan2009_AcOnly_prediction,crop_limits_proj)
july2009_AcOnly_prediction_crop <-
mask(july2009_AcOnly_prediction,crop_limits_proj)
july2009_AcOnly_prediction_se_crop = mask(july2009_Ac_prediction_se,crop_limits_proj)
# Visual
jan2009_VisOnly_prediction_crop <-
mask(jan2009_VisOnly_prediction,crop_limits_proj)
july2009_VisOnly_prediction_crop <- mask(july2009_VisOnly_prediction,crop_limits_proj)
july2009_VisOnly_prediction_se_crop = mask(july2009_Vis_prediction_se,crop_limits_proj)