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Results-Plots_HPCC.R
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Results-Plots_HPCC.R
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## Author: A.D. Wright
## Project: NCRN Amphibians - Monitoring Optimization
## Code: Results-Plots for data simulation experiments
#rm(list = ls())
#options(max.print = 100)
## TABLE OF CONTENTS
## Packages, working directory, and data 18
## Status 358
## Trend 703
## Driver/Autologistic 1046
## Driver/Site-specific 1385
## Combined Plots 1724
#########
## Part - Packages, working directory, and data
#########
##
#### Install Packages
##
if(!require(tidyverse)) {install.packages('tidyverse');require(tidyverse)}
if(!require(jagsUI)) {install.packages('jagsUI');require(jagsUI)}
if(!require(ggpubr)) {install.packages('ggpubr');require(ggpubr)}
if(!require(viridis)) {install.packages('viridis');require(viridis)}
##
#### Set Working Directory
##
#setwd('')
##
#### Load results
##
#Change memory limit if necessary
#https://stackoverflow.com/questions/10917532/memory-allocation-error-cannot-allocate-vector-of-size-75-1-mb
#memory.limit(size=1000000)
#Results
#Random Strat
#10% Effort
filenames_RS10 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RS_10", pattern = "jagsFit", full.names = T)
load(filenames_RS10[1])
output_RS10 <- results
converge_RS10 <- converge
for(i in 2:length(filenames_RS10)){
load(filenames_RS10[i])
output_RS10 <- c(output_RS10, results)
converge_RS10 <- c(converge_RS10, converge)
}
outCon_RS10 <- output_RS10[converge_RS10 < 1.1] # length(outCon_RS10)
outCon_RS10 <- outCon_RS10[1:500]
#20% Effort
filenames_RS20 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RS_20", pattern = "jagsFit", full.names = T)
load(filenames_RS20[1])
output_RS20 <- results
converge_RS20 <- converge
for(i in 2:length(filenames_RS20)){
load(filenames_RS20[i])
output_RS20 <- c(output_RS20, results)
converge_RS20 <- c(converge_RS20, converge)
}
outCon_RS20 <- output_RS20[converge_RS20 < 1.1] # length(outCon_RS20)
outCon_RS20 <- outCon_RS20[1:500]
#30% Effort
filenames_RS30 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RS_30", pattern = "jagsFit_", full.names = T)
load(filenames_RS30[1])
output_RS30 <- results
converge_RS30 <- converge
for(i in 2:length(filenames_RS30)){
load(filenames_RS30[i])
output_RS30 <- c(output_RS30, results)
converge_RS30 <- c(converge_RS30, converge)
}
outCon_RS30 <- output_RS30[converge_RS30 < 1.1] # length(outCon_RS30)
outCon_RS30 <- outCon_RS30[1:500]
#40% Effort
filenames_RS40 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RS_40", pattern = "jagsFit", full.names = T)
load(filenames_RS40[1])
output_RS40 <- results
converge_RS40 <- converge
for(i in 2:length(filenames_RS40)){
load(filenames_RS40[i])
output_RS40 <- c(output_RS40, results)
converge_RS40 <- c(converge_RS40, converge)
}
outCon_RS40 <- output_RS40[converge_RS40 < 1.1] # length(outCon_RS40)
outCon_RS40 <- outCon_RS40[1:500]
#50% Effort
filenames_RS50 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RS_50", pattern = "jagsFit", full.names = T)
load(filenames_RS50[1])
output_RS50 <- results
converge_RS50 <- converge
for(i in 2:length(filenames_RS50)){
load(filenames_RS50[i])
output_RS50 <- c(output_RS50, results)
converge_RS50 <- c(converge_RS50, converge)
}
outCon_RS50 <- output_RS50[converge_RS50 < 1.1] # length(outCon_RS50)
outCon_RS50 <- outCon_RS50[1:500]
#Indicator Units
#10% Effort
filenames_IU10 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/IU_10", pattern = "jagsFit", full.names = T)
load(filenames_IU10[1])
output_IU10 <- results
converge_IU10 <- converge
for(i in 2:length(filenames_IU10)){
load(filenames_IU10[i])
output_IU10 <- c(output_IU10, results)
converge_IU10 <- c(converge_IU10, converge)
}
outCon_IU10 <- output_IU10[converge_IU10 < 1.1] # length(outCon_IU10)
outCon_IU10 <- outCon_IU10[1:500]
#20% Effort
filenames_IU20 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/IU_20", pattern = "jagsFit", full.names = T)
load(filenames_IU20[1])
output_IU20 <- results
converge_IU20 <- converge
for(i in 2:length(filenames_IU20)){
load(filenames_IU20[i])
output_IU20 <- c(output_IU20, results)
converge_IU20 <- c(converge_IU20, converge)
}
outCon_IU20 <- output_IU20[converge_IU20 < 1.1] # length(outCon_IU20)
outCon_IU20 <- outCon_IU20[1:500]
#30% Effort
filenames_IU30 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/IU_30", pattern = "jagsFit", full.names = T)
load(filenames_IU30[1])
output_IU30 <- results
converge_IU30 <- converge
for(i in 2:length(filenames_IU30)){
load(filenames_IU30[i])
output_IU30 <- c(output_IU30, results)
converge_IU30 <- c(converge_IU30, converge)
}
outCon_IU30 <- output_IU30[converge_IU30 < 1.1] # length(outCon_IU30)
outCon_IU30 <- outCon_IU30[1:500]
#40% Effort
filenames_IU40 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/IU_40", pattern = "jagsFit", full.names = T)
load(filenames_IU40[1])
output_IU40 <- results
converge_IU40 <- converge
for(i in 2:length(filenames_IU40)){
load(filenames_IU40[i])
output_IU40 <- c(output_IU40, results)
converge_IU40 <- c(converge_IU40, converge)
}
outCon_IU40 <- output_IU40[converge_IU40 < 1.1] # length(outCon_IU40)
outCon_IU40 <- outCon_IU40[1:500]
#50% Effort
filenames_IU50 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/IU_50", pattern = "jagsFit", full.names = T)
load(filenames_IU50[1])
output_IU50 <- results
converge_IU50 <- converge
for(i in 2:length(filenames_IU50)){
load(filenames_IU50[i])
output_IU50 <- c(output_IU50, results)
converge_IU50 <- c(converge_IU50, converge)
}
outCon_IU50 <- output_IU50[converge_IU50 < 1.1] # length(outCon_IU50)
outCon_IU50 <- outCon_IU50[1:500]
#Weighted Effort
#10% Effort
filenames_WE10 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/WE_10", pattern = "jagsFit", full.names = T)
load(filenames_WE10[1])
output_WE10 <- results
converge_WE10 <- converge
for(i in 2:length(filenames_WE10)){
load(filenames_WE10[i])
output_WE10 <- c(output_WE10, results)
converge_WE10 <- c(converge_WE10, converge)
}
outCon_WE10 <- output_WE10[converge_WE10 < 1.1] # length(outCon_WE10)
outCon_WE10 <- outCon_WE10[1:500]
#20% Effort
filenames_WE20 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/WE_20", pattern = "jagsFit", full.names = T)
load(filenames_WE20[1])
output_WE20 <- results
converge_WE20 <- converge
for(i in 2:length(filenames_WE20)){
load(filenames_WE20[i])
output_WE20 <- c(output_WE20, results)
converge_WE20 <- c(converge_WE20, converge)
}
outCon_WE20 <- output_WE20[converge_WE20 < 1.1] # length(outCon_WE20)
outCon_WE20 <- outCon_WE20[1:500]
#30% Effort
filenames_WE30 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/WE_30", pattern = "jagsFit", full.names = T)
load(filenames_WE30[1])
output_WE30 <- results
converge_WE30 <- converge
for(i in 2:length(filenames_WE30)){
load(filenames_WE30[i])
output_WE30 <- c(output_WE30, results)
converge_WE30 <- c(converge_WE30, converge)
}
outCon_WE30 <- output_WE30[converge_WE30 < 1.1] # length(outCon_WE30)
outCon_WE30 <- outCon_WE30[1:500]
#40% Effort
filenames_WE40 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/WE_40", pattern = "jagsFit", full.names = T)
load(filenames_WE40[1])
output_WE40 <- results
converge_WE40 <- converge
for(i in 2:length(filenames_WE40)){
load(filenames_WE40[i])
output_WE40 <- c(output_WE40, results)
converge_WE40 <- c(converge_WE40, converge)
}
outCon_WE40 <- output_WE40[converge_WE40 < 1.1] # length(outCon_WE40)
outCon_WE40 <- outCon_WE40[1:500]
#50% Effort
filenames_WE50 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/WE_50", pattern = "jagsFit", full.names = T)
load(filenames_WE50[1])
output_WE50 <- results
converge_WE50 <- converge
for(i in 2:length(filenames_WE50)){
load(filenames_WE50[i])
output_WE50 <- c(output_WE50, results)
converge_WE50 <- c(converge_WE50, converge)
}
outCon_WE50 <- output_WE50[converge_WE50 < 1.1] # length(outCon_WE50)
outCon_WE50 <- outCon_WE50[1:500]
#Rotating Panel
#10% Effort
filenames_RP10 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RP-2_10", pattern = "jagsFit", full.names = T)
load(filenames_RP10[1])
output_RP10 <- results
converge_RP10 <- converge
for(i in 2:length(filenames_RP10)){
load(filenames_RP10[i])
output_RP10 <- c(output_RP10, results)
converge_RP10 <- c(converge_RP10, converge)
}
outCon_RP10 <- output_RP10[converge_RP10 < 1.1] # length(outCon_RP10)
outCon_RP10 <- outCon_RP10[1:500]
#20% Effort
filenames_RP20 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RP-2_20", pattern = "jagsFit", full.names = T)
load(filenames_RP20[1])
output_RP20 <- results
converge_RP20 <- converge
for(i in 2:length(filenames_RP20)){
load(filenames_RP20[i])
output_RP20 <- c(output_RP20, results)
converge_RP20 <- c(converge_RP20, converge)
}
outCon_RP20 <- output_RP20[converge_RP20 < 1.1] # length(outCon_RP20)
outCon_RP20 <- outCon_RP20[1:500]
#30% Effort
filenames_RP30 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RP-2_30", pattern = "jagsFit", full.names = T)
load(filenames_RP30[1])
output_RP30 <- results
converge_RP30 <- converge
for(i in 2:length(filenames_RP30)){
load(filenames_RP30[i])
output_RP30 <- c(output_RP30, results)
converge_RP30 <- c(converge_RP30, converge)
}
outCon_RP30 <- output_RP30[converge_RP30 < 1.1] # length(outCon_RP30)
outCon_RP30 <- outCon_RP30[1:500]
#40% Effort
filenames_RP40 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RP-2_40", pattern = "jagsFit", full.names = T)
load(filenames_RP40[1])
output_RP40 <- results
converge_RP40 <- converge
for(i in 2:length(filenames_RP40)){
load(filenames_RP40[i])
output_RP40 <- c(output_RP40, results)
converge_RP40 <- c(converge_RP40, converge)
}
outCon_RP40 <- output_RP40[converge_RP40 < 1.1] # length(outCon_RP40)
outCon_RP40 <- outCon_RP40[1:500]
#50% Effort
filenames_RP50 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/RP-2_50", pattern = "jagsFit", full.names = T)
load(filenames_RP50[1])
output_RP50 <- results
converge_RP50 <- converge
for(i in 2:length(filenames_RP50)){
load(filenames_RP50[i])
output_RP50 <- c(output_RP50, results)
converge_RP50 <- c(converge_RP50, converge)
}
outCon_RP50 <- output_RP50[converge_RP50 < 1.1] # length(outCon_RP50)
outCon_RP50 <- outCon_RP50[1:500]
#Split Panel
#10% Effort
filenames_SP10 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/SP_10", pattern = "jagsFit", full.names = T)
load(filenames_SP10[1])
output_SP10 <- results
converge_SP10 <- converge
for(i in 2:length(filenames_SP10)){
load(filenames_SP10[i])
output_SP10 <- c(output_SP10, results)
converge_SP10 <- c(converge_SP10, converge)
}
outCon_SP10 <- output_SP10[converge_SP10 < 1.1] # length(outCon_SP10)
outCon_SP10 <- outCon_SP10[1:500]
#20% Effort
filenames_SP20 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/SP_20", pattern = "jagsFit", full.names = T)
load(filenames_SP20[1])
output_SP20 <- results
converge_SP20 <- converge
for(i in 2:length(filenames_SP20)){
load(filenames_SP20[i])
output_SP20 <- c(output_SP20, results)
converge_SP20 <- c(converge_SP20, converge)
}
outCon_SP20 <- output_SP20[converge_SP20 < 1.1] # length(outCon_SP20)
outCon_SP20 <- outCon_SP20[1:500]
#30% Effort
filenames_SP30 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/SP_30", pattern = "jagsFit", full.names = T)
load(filenames_SP30[1])
output_SP30 <- results
converge_SP30 <- converge
for(i in 2:length(filenames_SP30)){
load(filenames_SP30[i])
output_SP30 <- c(output_SP30, results)
converge_SP30 <- c(converge_SP30, converge)
}
outCon_SP30 <- output_SP30[converge_SP30 < 1.1] # length(outCon_SP30)
outCon_SP30 <- outCon_SP30[1:500]
#40% Effort
filenames_SP40 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/SP_40", pattern = "jagsFit", full.names = T)
load(filenames_SP40[1])
output_SP40 <- results
converge_SP40 <- converge
for(i in 2:length(filenames_SP40)){
load(filenames_SP40[i])
output_SP40 <- c(output_SP40, results)
converge_SP40 <- c(converge_SP40, converge)
}
outCon_SP40 <- output_SP40[converge_SP40 < 1.1] # length(outCon_SP40)
outCon_SP40 <- outCon_SP40[1:500]
#50% Effort
filenames_SP50 <- list.files(path = "Z:/NCRN-Amphibians/Monitoring_simulation/SP_50", pattern = "jagsFit", full.names = T)
load(filenames_SP50[1])
output_SP50 <- results
converge_SP50 <- converge
for(i in 2:length(filenames_SP50)){
load(filenames_SP50[i])
output_SP50 <- c(output_SP50, results)
converge_SP50 <- c(converge_SP50, converge)
}
outCon_SP50 <- output_SP50[converge_SP50 < 1.1] # length(outCon_SP50)
outCon_SP50 <- outCon_SP50[1:500]
#########
## Part - Status
#########
##
#### Status/Global
##
##### DATA MANAGEMENT
#RS
status_RS <- data.frame(Diff = c(unlist((lapply(outCon_RS10, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RS20, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RS30, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RS40, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RS50, function (x) x$global$mu.a0.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RS10, function (x) x$global$mu.a0.global))))),
rep(20, length(unlist((lapply(outCon_RS20, function (x) x$global$mu.a0.global))))),
rep(30, length(unlist((lapply(outCon_RS30, function (x) x$global$mu.a0.global))))),
rep(40, length(unlist((lapply(outCon_RS40, function (x) x$global$mu.a0.global))))),
rep(50, length(unlist((lapply(outCon_RS50, function (x) x$global$mu.a0.global)))))
))
status_RS$Design <- 'Stratified Random'
#Calculating RMSE
rmse_RS_10 <- status_RS %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_20 <- status_RS %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_30 <- status_RS %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_40 <- status_RS %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_50 <- status_RS %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_mua0global <- data.frame(RMSE = unlist(c(rmse_RS_10,rmse_RS_20,rmse_RS_30,rmse_RS_40,rmse_RS_50)),
Effort = c(10,20,30,40,50),
Design = rep('Stratified Random', 5),
Parameter = 'Status',
Level = "Global"
)
rmse_RS_mua0global %>% summarise(mean(RMSE))
#IU
status_IU <- data.frame(Diff = c(unlist((lapply(outCon_IU10, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_IU20, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_IU30, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_IU40, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_IU50, function (x) x$global$mu.a0.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_IU10, function (x) x$global$mu.a0.global))))),
rep(20, length(unlist((lapply(outCon_IU20, function (x) x$global$mu.a0.global))))),
rep(30, length(unlist((lapply(outCon_IU30, function (x) x$global$mu.a0.global))))),
rep(40, length(unlist((lapply(outCon_IU40, function (x) x$global$mu.a0.global))))),
rep(50, length(unlist((lapply(outCon_IU50, function (x) x$global$mu.a0.global)))))
))
status_IU$Design <- 'Indicator Unit'
#Calculating RMSE
rmse_IU_10 <- status_IU %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_20 <- status_IU %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_30 <- status_IU %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_40 <- status_IU %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_50 <- status_IU %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_mua0global <- data.frame(RMSE = unlist(c(rmse_IU_10,rmse_IU_20,rmse_IU_30,rmse_IU_40,rmse_IU_50)),
Effort = c(10,20,30,40,50),
Design = rep('Indicator Unit', 5),
Parameter = 'Status',
Level = "Global"
)
rmse_IU_mua0global %>% summarise(mean(RMSE))
#WE
status_WE <- data.frame(Diff = c(unlist((lapply(outCon_WE10, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_WE20, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_WE30, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_WE40, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_WE50, function (x) x$global$mu.a0.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_WE10, function (x) x$global$mu.a0.global))))),
rep(20, length(unlist((lapply(outCon_WE20, function (x) x$global$mu.a0.global))))),
rep(30, length(unlist((lapply(outCon_WE30, function (x) x$global$mu.a0.global))))),
rep(40, length(unlist((lapply(outCon_WE40, function (x) x$global$mu.a0.global))))),
rep(50, length(unlist((lapply(outCon_WE50, function (x) x$global$mu.a0.global)))))
))
status_WE$Design <- 'Weighted Effort'
#RMSE
rmse_WE_10 <- status_WE %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_20 <- status_WE %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_30 <- status_WE %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_40 <- status_WE %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_50 <- status_WE %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_mua0global <- data.frame(RMSE = unlist(c(rmse_WE_10,rmse_WE_20,rmse_WE_30,rmse_WE_40,rmse_WE_50)),
Effort = c(10,20,30,40,50),
Design = rep('Weighted Effort', 5),
Parameter = 'Status',
Level = "Global"
)
rmse_WE_mua0global %>% summarise(mean(RMSE))
#RP
status_RP <- data.frame(Diff = c(unlist((lapply(outCon_RP10, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RP20, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RP30, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RP40, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_RP50, function (x) x$global$mu.a0.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RP10, function (x) x$global$mu.a0.global))))),
rep(20, length(unlist((lapply(outCon_RP20, function (x) x$global$mu.a0.global))))),
rep(30, length(unlist((lapply(outCon_RP30, function (x) x$global$mu.a0.global))))),
rep(40, length(unlist((lapply(outCon_RP40, function (x) x$global$mu.a0.global))))),
rep(50, length(unlist((lapply(outCon_RP50, function (x) x$global$mu.a0.global)))))
))
status_RP$Design <- 'Rotating Panel'
#Calculating RMSE
rmse_RP_10 <- status_RP %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_20 <- status_RP %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_30 <- status_RP %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_40 <- status_RP %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_50 <- status_RP %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_mua0global <- data.frame(RMSE = unlist(c(rmse_RP_10,rmse_RP_20,rmse_RP_30,rmse_RP_40,rmse_RP_50)),
Effort = c(10,20,30,40,50),
Design = rep('Rotating Panel', 5),
Parameter = 'Status',
Level = "Global"
)
rmse_RP_mua0global %>% summarise(mean(RMSE))
#SP
status_SP <- data.frame(Diff = c(unlist((lapply(outCon_SP10, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_SP20, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_SP30, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_SP40, function (x) x$global$mu.a0.global))),
unlist((lapply(outCon_SP50, function (x) x$global$mu.a0.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_SP10, function (x) x$global$mu.a0.global))))),
rep(20, length(unlist((lapply(outCon_SP20, function (x) x$global$mu.a0.global))))),
rep(30, length(unlist((lapply(outCon_SP30, function (x) x$global$mu.a0.global))))),
rep(40, length(unlist((lapply(outCon_SP40, function (x) x$global$mu.a0.global))))),
rep(50, length(unlist((lapply(outCon_SP50, function (x) x$global$mu.a0.global)))))
))
status_SP$Design <- 'Split Panel'
#Calculating RMSE
rmse_SP_10 <- status_SP %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_20 <- status_SP %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_30 <- status_SP %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_40 <- status_SP %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_50 <- status_SP %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_mua0global <- data.frame(RMSE = unlist(c(rmse_SP_10,rmse_SP_20,rmse_SP_30,rmse_SP_40,rmse_SP_50)),
Effort = c(10,20,30,40,50),
Design = rep('Split Panel', 5),
Parameter = 'Status',
Level = "Global"
)
rmse_SP_mua0global %>% summarise(mean(RMSE))
#Putting them all together
status_RMSE_Global <- rbind(rmse_RS_mua0global,rmse_WE_mua0global,rmse_RP_mua0global,rmse_SP_mua0global) #rmse_IU_mua0global,
status_RMSE_Global$Effort <- factor(status_RMSE_Global$Effort, levels=unique(status_RMSE_Global$Effort))
##### Plots
## Plot RMSE
a0_RMSE <- ggplot(data = status_RMSE_Global , aes(y = RMSE, x = Effort, color = Design)) + #, shape = Design)
geom_point(size = 2.5, alpha = 0.75) +
theme_bw() +
ggtitle('Region-level') +
theme(
panel.border = element_rect(fill = NA, color = 'gray35'),
panel.grid.major = element_line(color='white'),
panel.grid.minor = element_line(color='white'),
legend.title = element_blank(),
legend.text = element_text(size = 10),
plot.title = element_text(size = 10),
axis.text.y = element_text(size = 8),
axis.text.x = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank()
) +
scale_y_continuous(breaks=c(0.04,0.05,0.06,0.07,0.08,0.09,0.1), limits = c(0.045,0.105))
##
#### Status/Park
##
##### DATA MANAGEMENT
#RS
status_RS <- data.frame(Diff = c(unlist((lapply(outCon_RS10, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RS20, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RS30, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RS40, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RS50, function (x) x$park$mu.a0)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RS10, function (x) x$park$mu.a0))))),
rep(20, length(unlist((lapply(outCon_RS20, function (x) x$park$mu.a0))))),
rep(30, length(unlist((lapply(outCon_RS30, function (x) x$park$mu.a0))))),
rep(40, length(unlist((lapply(outCon_RS40, function (x) x$park$mu.a0))))),
rep(50, length(unlist((lapply(outCon_RS50, function (x) x$park$mu.a0)))))
))
status_RS$Design <- 'Stratified Random'
status_RS$Park <- rep(1:10, times = dim(status_RS)[1]/10)
status_RS$Park <- factor(status_RS$Park, levels=unique(status_RS$Park))
rmse_RS_mua0park <- status_RS %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Stratified Random',
Parameter = 'Status',
Level = "Park")
x <- rmse_RS_mua0park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#IU
status_IU <- data.frame(Diff = c(unlist((lapply(outCon_IU10, function (x) x$park$mu.a0))),
unlist((lapply(outCon_IU20, function (x) x$park$mu.a0))),
unlist((lapply(outCon_IU30, function (x) x$park$mu.a0))),
unlist((lapply(outCon_IU40, function (x) x$park$mu.a0))),
unlist((lapply(outCon_IU50, function (x) x$park$mu.a0)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_IU10, function (x) x$park$mu.a0))))),
rep(20, length(unlist((lapply(outCon_IU20, function (x) x$park$mu.a0))))),
rep(30, length(unlist((lapply(outCon_IU30, function (x) x$park$mu.a0))))),
rep(40, length(unlist((lapply(outCon_IU40, function (x) x$park$mu.a0))))),
rep(50, length(unlist((lapply(outCon_IU50, function (x) x$park$mu.a0)))))
))
status_IU$Design <- 'Indicator Unit'
status_IU$Park <- rep(1:10, times = dim(status_IU)[1]/10)
status_IU$Park <- factor(status_IU$Park, levels=unique(status_IU$Park))
#Calculating RMSE
rmse_IU_mua0park <- status_IU %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Indicator Unit',
Parameter = 'Status',
Level = "Park")
x <- rmse_IU_mua0park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#WE
status_WE <- data.frame(Diff = c(unlist((lapply(outCon_WE10, function (x) x$park$mu.a0))),
unlist((lapply(outCon_WE20, function (x) x$park$mu.a0))),
unlist((lapply(outCon_WE30, function (x) x$park$mu.a0))),
unlist((lapply(outCon_WE40, function (x) x$park$mu.a0))),
unlist((lapply(outCon_WE50, function (x) x$park$mu.a0)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_WE10, function (x) x$park$mu.a0))))),
rep(20, length(unlist((lapply(outCon_WE20, function (x) x$park$mu.a0))))),
rep(30, length(unlist((lapply(outCon_WE30, function (x) x$park$mu.a0))))),
rep(40, length(unlist((lapply(outCon_WE40, function (x) x$park$mu.a0))))),
rep(50, length(unlist((lapply(outCon_WE50, function (x) x$park$mu.a0)))))
))
status_WE$Design <- 'Weighted Effort'
status_WE$Park <- rep(1:10, times = dim(status_WE)[1]/10)
status_WE$Park <- factor(status_WE$Park, levels=unique(status_WE$Park))
#Calculating RMSE
rmse_WE_mua0park <- status_WE %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Weighted Effort',
Parameter = 'Status',
Level = "Park")
x <- rmse_WE_mua0park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#RP
status_RP <- data.frame(Diff = c(unlist((lapply(outCon_RP10, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RP20, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RP30, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RP40, function (x) x$park$mu.a0))),
unlist((lapply(outCon_RP50, function (x) x$park$mu.a0)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RP10, function (x) x$park$mu.a0))))),
rep(20, length(unlist((lapply(outCon_RP20, function (x) x$park$mu.a0))))),
rep(30, length(unlist((lapply(outCon_RP30, function (x) x$park$mu.a0))))),
rep(40, length(unlist((lapply(outCon_RP40, function (x) x$park$mu.a0))))),
rep(50, length(unlist((lapply(outCon_RP50, function (x) x$park$mu.a0)))))
))
status_RP$Design <- 'Rotating Panel'
status_RP$Park <- rep(1:10, times = dim(status_RP)[1]/10)
status_RP$Park <- factor(status_RP$Park, levels=unique(status_RP$Park))
#Calculating RMSE
rmse_RP_mua0park <- status_RP %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Rotating Panel',
Parameter = 'Status',
Level = "Park")
x <- rmse_RP_mua0park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#SP
status_SP <- data.frame(Diff = c(unlist((lapply(outCon_SP10, function (x) x$park$mu.a0))),
unlist((lapply(outCon_SP20, function (x) x$park$mu.a0))),
unlist((lapply(outCon_SP30, function (x) x$park$mu.a0))),
unlist((lapply(outCon_SP40, function (x) x$park$mu.a0))),
unlist((lapply(outCon_SP50, function (x) x$park$mu.a0)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_SP10, function (x) x$park$mu.a0))))),
rep(20, length(unlist((lapply(outCon_SP20, function (x) x$park$mu.a0))))),
rep(30, length(unlist((lapply(outCon_SP30, function (x) x$park$mu.a0))))),
rep(40, length(unlist((lapply(outCon_SP40, function (x) x$park$mu.a0))))),
rep(50, length(unlist((lapply(outCon_SP50, function (x) x$park$mu.a0)))))
))
status_SP$Design <- 'Split Panel'
status_SP$Park <- rep(1:10, times = dim(status_SP)[1]/10)
status_SP$Park <- factor(status_SP$Park, levels=unique(status_SP$Park))
#Calculating RMSE
rmse_SP_mua0park <- status_SP %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Split Panel',
Parameter = 'Status',
Level = "Park")
x <- rmse_SP_mua0park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
##### Plots
status_RMSE_Park <- rbind(rmse_RS_mua0park,rmse_WE_mua0park,rmse_RP_mua0park,rmse_SP_mua0park) # rmse_IU_mua0park,
status_RMSE_Park$Effort <- factor(status_RMSE_Park$Effort, levels=unique(status_RMSE_Park$Effort))
status_RMSE_Park$Design <- factor(status_RMSE_Park$Design, levels=unique(status_RMSE_Park$Design))
## Plot RMSE
a0_RMSE_park <- ggplot(data = status_RMSE_Park , aes(y = RMSE, x = Effort, color = Design, fill = Design)) +
geom_boxplot(alpha=0.5) +
geom_point(position=position_dodge(width=0.75),aes(group=Design)) +
theme_bw() +
ggtitle('Unit-level') +
theme(
panel.border = element_rect(fill = NA, color = 'gray35'),
panel.grid.major = element_line(color='white'),
panel.grid.minor = element_line(color='white'),
legend.title = element_blank(),
legend.text = element_text(size = 10),
plot.title = element_text(size = 10),
axis.text.y = element_text(size = 8),
axis.text.x = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank()
) +
scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4)) +
theme(legend.position = 'none')
a0_RMSE_park <- annotate_figure(a0_RMSE_park,
right = text_grob("Status (a0)", rot = 270, size = 10)
)
#########
## Part - Trend
#########
##
#### Global
##
###### DATA MANGEMENT
#RS
trend_RS <- data.frame(Diff = c(unlist((lapply(outCon_RS10, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RS20, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RS30, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RS40, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RS50, function (x) x$global$mu.a2.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RS10, function (x) x$global$mu.a2.global))))),
rep(20, length(unlist((lapply(outCon_RS20, function (x) x$global$mu.a2.global))))),
rep(30, length(unlist((lapply(outCon_RS30, function (x) x$global$mu.a2.global))))),
rep(40, length(unlist((lapply(outCon_RS40, function (x) x$global$mu.a2.global))))),
rep(50, length(unlist((lapply(outCon_RS50, function (x) x$global$mu.a2.global)))))
))
trend_RS$Design <- 'Stratified Random'
#Calculating RMSE
rmse_RS_10 <- trend_RS %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_20 <- trend_RS %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_30 <- trend_RS %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_40 <- trend_RS %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_50 <- trend_RS %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RS_mua2global <- data.frame(RMSE = unlist(c(rmse_RS_10,rmse_RS_20,rmse_RS_30,rmse_RS_40,rmse_RS_50)),
Effort = c(10,20,30,40,50),
Design = rep('Stratified Random', 5),
Parameter = 'Trend',
Level = "Global"
)
rmse_RS_mua2global %>% summarise(mean(RMSE))
#IU
trend_IU <- data.frame(Diff = c(unlist((lapply(outCon_IU10, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_IU20, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_IU30, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_IU40, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_IU50, function (x) x$global$mu.a2.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_IU10, function (x) x$global$mu.a2.global))))),
rep(20, length(unlist((lapply(outCon_IU20, function (x) x$global$mu.a2.global))))),
rep(30, length(unlist((lapply(outCon_IU30, function (x) x$global$mu.a2.global))))),
rep(40, length(unlist((lapply(outCon_IU40, function (x) x$global$mu.a2.global))))),
rep(50, length(unlist((lapply(outCon_IU50, function (x) x$global$mu.a2.global)))))
))
trend_IU$Design <- 'Indicator Unit'
#Calculating RMSE
rmse_IU_10 <- trend_IU %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_20 <- trend_IU %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_30 <- trend_IU %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_40 <- trend_IU %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_50 <- trend_IU %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_IU_mua2global <- data.frame(RMSE = unlist(c(rmse_IU_10,rmse_IU_20,rmse_IU_30,rmse_IU_40,rmse_IU_50)),
Effort = c(10,20,30,40,50),
Design = rep('Indicator Unit', 5),
Parameter = 'Trend',
Level = "Global"
)
rmse_IU_mua2global %>% summarise(mean(RMSE))
#WE
trend_WE <- data.frame(Diff = c(unlist((lapply(outCon_WE10, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_WE20, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_WE30, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_WE40, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_WE50, function (x) x$global$mu.a2.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_WE10, function (x) x$global$mu.a2.global))))),
rep(20, length(unlist((lapply(outCon_WE20, function (x) x$global$mu.a2.global))))),
rep(30, length(unlist((lapply(outCon_WE30, function (x) x$global$mu.a2.global))))),
rep(40, length(unlist((lapply(outCon_WE40, function (x) x$global$mu.a2.global))))),
rep(50, length(unlist((lapply(outCon_WE50, function (x) x$global$mu.a2.global)))))
))
trend_WE$Design <- 'Weighted Effort'
#RMSE
rmse_WE_10 <- trend_WE %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_20 <- trend_WE %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_30 <- trend_WE %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_40 <- trend_WE %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_50 <- trend_WE %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_WE_mua2global <- data.frame(RMSE = unlist(c(rmse_WE_10,rmse_WE_20,rmse_WE_30,rmse_WE_40,rmse_WE_50)),
Effort = c(10,20,30,40,50),
Design = rep('Weighted Effort', 5),
Parameter = 'Trend',
Level = "Global"
)
rmse_WE_mua2global %>% summarise(mean(RMSE))
#RP
trend_RP <- data.frame(Diff = c(unlist((lapply(outCon_RP10, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RP20, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RP30, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RP40, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_RP50, function (x) x$global$mu.a2.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RP10, function (x) x$global$mu.a2.global))))),
rep(20, length(unlist((lapply(outCon_RP20, function (x) x$global$mu.a2.global))))),
rep(30, length(unlist((lapply(outCon_RP30, function (x) x$global$mu.a2.global))))),
rep(40, length(unlist((lapply(outCon_RP40, function (x) x$global$mu.a2.global))))),
rep(50, length(unlist((lapply(outCon_RP50, function (x) x$global$mu.a2.global)))))
))
trend_RP$Design <- 'Rotating Panel'
#Calculating RMSE
rmse_RP_10 <- trend_RP %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_20 <- trend_RP %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_30 <- trend_RP %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_40 <- trend_RP %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_50 <- trend_RP %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_RP_mua2global <- data.frame(RMSE = unlist(c(rmse_RP_10,rmse_RP_20,rmse_RP_30,rmse_RP_40,rmse_RP_50)),
Effort = c(10,20,30,40,50),
Design = rep('Rotating Panel', 5),
Parameter = 'Trend',
Level = "Global"
)
rmse_RP_mua2global %>% summarise(mean(RMSE))
#SP
trend_SP <- data.frame(Diff = c(unlist((lapply(outCon_SP10, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_SP20, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_SP30, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_SP40, function (x) x$global$mu.a2.global))),
unlist((lapply(outCon_SP50, function (x) x$global$mu.a2.global)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_SP10, function (x) x$global$mu.a2.global))))),
rep(20, length(unlist((lapply(outCon_SP20, function (x) x$global$mu.a2.global))))),
rep(30, length(unlist((lapply(outCon_SP30, function (x) x$global$mu.a2.global))))),
rep(40, length(unlist((lapply(outCon_SP40, function (x) x$global$mu.a2.global))))),
rep(50, length(unlist((lapply(outCon_SP50, function (x) x$global$mu.a2.global)))))
))
trend_SP$Design <- 'Split Panel'
#Calculating RMSE
rmse_SP_10 <- trend_SP %>% filter(Effort == 10) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_20 <- trend_SP %>% filter(Effort == 20) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_30 <- trend_SP %>% filter(Effort == 30) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_40 <- trend_SP %>% filter(Effort == 40) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_50 <- trend_SP %>% filter(Effort == 50) %>% summarize(RMSE = (sqrt((1/500)*(sum(Diff^2)))))
rmse_SP_mua2global <- data.frame(RMSE = unlist(c(rmse_SP_10,rmse_SP_20,rmse_SP_30,rmse_SP_40,rmse_SP_50)),
Effort = c(10,20,30,40,50),
Design = rep('Split Panel', 5),
Parameter = 'Trend',
Level = "Global"
)
rmse_SP_mua2global %>% summarise(mean(RMSE))
# Putting it all together
trend_RMSE_Global <- rbind(rmse_RS_mua2global,rmse_WE_mua2global,rmse_RP_mua2global,rmse_SP_mua2global) #rmse_IU_mua2global,
trend_RMSE_Global$Effort <- factor(trend_RMSE_Global$Effort, levels=unique(trend_RMSE_Global$Effort))
##### Plots
## Plot RMSE
a2_RMSE <- ggplot(data = trend_RMSE_Global , aes(y = RMSE, x = Effort, color = Design)) +
geom_point(size = 2.5, alpha = 0.75) +
theme_bw() +
theme(
panel.border = element_rect(fill = NA, color = 'gray35'),
panel.grid.major = element_line(color='white'),
panel.grid.minor = element_line(color='white'),
legend.title = element_blank(),
legend.text = element_text(size = 10),
axis.text.y = element_text(size = 8),
axis.text.x = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank()
) +
scale_y_continuous(breaks=c(0.04,0.05,0.06,0.07), limits = c(0.04,0.07))
##
#### Trend/Park
##
##### DATA MANAGEMENT
#RS
trend_RS <- data.frame(Diff = c(unlist((lapply(outCon_RS10, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RS20, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RS30, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RS40, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RS50, function (x) x$park$mu.a2)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RS10, function (x) x$park$mu.a2))))),
rep(20, length(unlist((lapply(outCon_RS20, function (x) x$park$mu.a2))))),
rep(30, length(unlist((lapply(outCon_RS30, function (x) x$park$mu.a2))))),
rep(40, length(unlist((lapply(outCon_RS40, function (x) x$park$mu.a2))))),
rep(50, length(unlist((lapply(outCon_RS50, function (x) x$park$mu.a2)))))
))
trend_RS$Design <- 'Stratified Random'
trend_RS$Park <- rep(1:10, times = dim(trend_RS)[1]/10)
trend_RS$Park <- factor(trend_RS$Park, levels=unique(trend_RS$Park))
#Calculating RMSE
rmse_RS_mua2park <- trend_RS %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Stratified Random',
Parameter = 'Trend',
Level = "Park")
x <- rmse_RS_mua2park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#IU
trend_IU <- data.frame(Diff = c(unlist((lapply(outCon_IU10, function (x) x$park$mu.a2))),
unlist((lapply(outCon_IU20, function (x) x$park$mu.a2))),
unlist((lapply(outCon_IU30, function (x) x$park$mu.a2))),
unlist((lapply(outCon_IU40, function (x) x$park$mu.a2))),
unlist((lapply(outCon_IU50, function (x) x$park$mu.a2)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_IU10, function (x) x$park$mu.a2))))),
rep(20, length(unlist((lapply(outCon_IU20, function (x) x$park$mu.a2))))),
rep(30, length(unlist((lapply(outCon_IU30, function (x) x$park$mu.a2))))),
rep(40, length(unlist((lapply(outCon_IU40, function (x) x$park$mu.a2))))),
rep(50, length(unlist((lapply(outCon_IU50, function (x) x$park$mu.a2)))))
))
trend_IU$Design <- 'Indicator Unit'
trend_IU$Park <- rep(1:10, times = dim(trend_IU)[1]/10)
trend_IU$Park <- factor(trend_IU$Park, levels=unique(trend_IU$Park))
#Calculating RMSE
rmse_IU_mua2park <- trend_IU %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Indicator Unit',
Parameter = 'Status',
Level = "Park")
x <- rmse_IU_mua2park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#WE
trend_WE <- data.frame(Diff = c(unlist((lapply(outCon_WE10, function (x) x$park$mu.a2))),
unlist((lapply(outCon_WE20, function (x) x$park$mu.a2))),
unlist((lapply(outCon_WE30, function (x) x$park$mu.a2))),
unlist((lapply(outCon_WE40, function (x) x$park$mu.a2))),
unlist((lapply(outCon_WE50, function (x) x$park$mu.a2)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_WE10, function (x) x$park$mu.a2))))),
rep(20, length(unlist((lapply(outCon_WE20, function (x) x$park$mu.a2))))),
rep(30, length(unlist((lapply(outCon_WE30, function (x) x$park$mu.a2))))),
rep(40, length(unlist((lapply(outCon_WE40, function (x) x$park$mu.a2))))),
rep(50, length(unlist((lapply(outCon_WE50, function (x) x$park$mu.a2)))))
))
trend_WE$Design <- 'Weighted Effort'
trend_WE$Park <- rep(1:10, times = dim(trend_WE)[1]/10)
trend_WE$Park <- factor(trend_WE$Park, levels=unique(trend_WE$Park))
#Calculating RMSE
rmse_WE_mua2park <- trend_WE %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Weighted Effort',
Parameter = 'Trend',
Level = "Park")
x <- rmse_WE_mua2park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#RP
trend_RP <- data.frame(Diff = c(unlist((lapply(outCon_RP10, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RP20, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RP30, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RP40, function (x) x$park$mu.a2))),
unlist((lapply(outCon_RP50, function (x) x$park$mu.a2)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_RP10, function (x) x$park$mu.a2))))),
rep(20, length(unlist((lapply(outCon_RP20, function (x) x$park$mu.a2))))),
rep(30, length(unlist((lapply(outCon_RP30, function (x) x$park$mu.a2))))),
rep(40, length(unlist((lapply(outCon_RP40, function (x) x$park$mu.a2))))),
rep(50, length(unlist((lapply(outCon_RP50, function (x) x$park$mu.a2)))))
))
trend_RP$Design <- 'Rotating Panel'
trend_RP$Park <- rep(1:10, times = dim(trend_RP)[1]/10)
trend_RP$Park <- factor(trend_RP$Park, levels=unique(trend_RP$Park))
#Calculating RMSE
rmse_RP_mua2park <- trend_RP %>% group_by(Park, Effort) %>%
summarize(RMSE = (sqrt((1/500)*(sum(Diff^2))))) %>%
mutate(Design = 'Rotating Panel',
Parameter = 'Trend',
Level = "Park")
x <- rmse_RP_mua2park %>% group_by(Park) %>% summarize(Avg = mean(RMSE))
mean(x$Avg);min(x$Avg);max(x$Avg)
#SP
trend_SP <- data.frame(Diff = c(unlist((lapply(outCon_SP10, function (x) x$park$mu.a2))),
unlist((lapply(outCon_SP20, function (x) x$park$mu.a2))),
unlist((lapply(outCon_SP30, function (x) x$park$mu.a2))),
unlist((lapply(outCon_SP40, function (x) x$park$mu.a2))),
unlist((lapply(outCon_SP50, function (x) x$park$mu.a2)))
),
Effort = c(rep(10, length(unlist((lapply(outCon_SP10, function (x) x$park$mu.a2))))),
rep(20, length(unlist((lapply(outCon_SP20, function (x) x$park$mu.a2))))),
rep(30, length(unlist((lapply(outCon_SP30, function (x) x$park$mu.a2))))),
rep(40, length(unlist((lapply(outCon_SP40, function (x) x$park$mu.a2))))),
rep(50, length(unlist((lapply(outCon_SP50, function (x) x$park$mu.a2)))))
))