TestScripts/stratified_mean_limited.R
In Blevy2/READ-PDB-blevy2-MFS2: Mixed Fishery fleet dynamics simulation tool
#THIS SCRIPT IS A PIECE OF RUN_SURVEY_BENS_NEW WHERE THE STRATIFIED MEAN CALCULATION IS MADE
#THIS SCRIPT ALLOWS US TO CALULATE THE STRATIFIED MEAN FOR A SMALLER NUMER OF NOISELESS SURVEYS
##################################################################################################
#THINGS WE NEED
##################################################################################################
scenario <- "DecPop_IncTemp"
n_spp <- 3
#survey results without noise
list_all <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\list_all_",scenario,".RDS",sep=""))
#simulation results
result <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\result_",scenario,".RDS",sep=""))
#random survey locations
surv_random <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\surv_random_",scenario,".RDS",sep=""))
#spp1 spp2 spp3
short_names <- c("YT","Cod","Had")
#strata that each species occupies. Used to calculate stratified random mean of each
strata_species <- list()
strata_species[["YT"]] <- c(13,14,15,16,17,18,19,20,21)
strata_species[["Cod"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25)
strata_species[["Had"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25,29,30)
##################################################################################################
library(raster)
library(sp)
library(TMB)
library(VAST)
library(dplyr)
library(ggplot2)
#read in habitat matrix
hab <- readRDS(file="hab_GB_3species.RDS") #courser resolution
#read in GB strata
#haddock contains all stratas used
Had_ras <- readRDS(file="TestScripts/Habitat_plots/Haddock/Had_Weighted_AdaptFalse_RASTER_res2.RDS")
#plot(Had_ras)
#translate habitat matrix back into raster
hab_ras <-raster(hab$hab$spp3)
extent(hab_ras) <- extent(Had_ras)
#plot(hab_ras)
#ADD TRUE MODEL POPULATION VALUES TO SURVEY DATA TABLES
for(iter in seq(length(list_all))){
print(iter)
temp <- matrix(data=0,nrow=length(list_all[[iter]][,1]),ncol=n_spp)
lat <- vector()
lon <- vector()
for(samp in seq(length(list_all[[iter]][,1]))){
#ADDING TRUE POPULATION
x = as.numeric(list_all[[iter]][samp,2]) #x in second column
y = as.numeric(list_all[[iter]][samp,3]) #y in third column
wk = as.numeric(list_all[[iter]][samp,11]) #week in 11th column
yr = as.numeric(list_all[[iter]][samp,7]) #year in 7th column
temp[samp,1] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp1"]],na.rm=T) #YT is spp1
temp[samp,2] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp2"]],na.rm=T) #Cod is spp2
temp[samp,3] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp3"]],na.rm=T) #Had is spp3
#ADDING LAT LON LOCATIONS
rw <- as.numeric(list_all[[iter]][samp,"x"]) #x in col 2
cl <- as.numeric(list_all[[iter]][samp,"y"]) #y in col 3
lon[samp] <- xFromCol(hab_ras, col = cl)
lat[samp] <- yFromRow(hab_ras, row = rw)
}
temp <- cbind(temp,lat,lon)
colnames(temp) <- c("YT","Cod","Had","Latitude","Longitude")
list_all[[iter]] <- cbind(list_all[[iter]],temp)
colnames(list_all[[iter]]) <- c("station_no","x","y","stratum","day","tow","year","YT_samp","Cod_samp","Had_samp","week","Season","YT_pop","Cod_pop","Had_pop","Lat","Lon")
}
#FIND MEAN VALUE BY SEASON USING ABOVE INFORMATION. USE MEAN OF TWO SURVEY WEEKS FOR EACH SEASON
season_wks <- list(c(13,14),c(37,38))
pop_by_season <- list()
for(iter in seq(length(list_all))){
for(s in short_names){
temp <- data.frame()
idx <- 1
for(yr in seq(3,22)){
for(season in seq(2)){
temp[idx,1] <- yr
temp[idx,2] <- season
#use values in given year for weeks in specified season. only use single strata because entire population summarized in each strata in above loop
temp[idx,3] <- mean(as.numeric(list_all[[iter]][((as.numeric(list_all[[iter]][,"year"]==yr)) & (as.numeric(list_all[[iter]][,"week"]) %in% season_wks[[season]]) & (as.numeric(list_all[[iter]][,"stratum"]==29)) ),paste(s,"_pop",sep="")]))
idx <- idx + 1
}
}
colnames(temp) <- c("year","season","biomass")
pop_by_season[[s]][[iter]] <- temp
}
}
#BELOW WILL TAKE A MINUTE
#choose some strata to exclude, if desired
exclude_strata <- TRUE
ifelse(exclude_strata,
#George's Bank Setup by species
#YT Cod Haddock
exclude <- list(c(13,14,15,17,18), c(23,24,25), c(23,24,25,29,30)),
#exclude none
exclude <- list(c(0),c(0),c(0)) #3 species
)
#NOW WE NEED TO CREATE A STRATIFIED MEAN FROM EACH OF THESE SAMPLES
#there are #strata * #iterations * #samp_per_iter total samples
#I AM COPYING FROM CALC_SRS_INDEX_SURVEY_BENS, which was adapted from Liz's code to create below
library(tibble)
library(ggplot2)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(here)
#stop output from below using this option
options(dplyr.summarise.inform = FALSE)
#load file to calculate the stratified mean
source("TestScripts/Calc_strat_mean/fn_srs_survey_BENS.R")
#setup dimensions for each species- 1 for each strata
strat_mean_all <- vector("list",length(seq(n_spp)))
for(s in seq(n_spp)){
strat_mean_all[[s]] <- vector("list",length(list_all))
}
#go through each strata survey, iteration, sample
for(iter in seq(length(list_all))){
print(iter)
#if any NA columns make them zero
list_all[[iter]][is.na(list_all[[iter]])]=0
# calculate SRS estimates ====
for(s in seq(n_spp)){
#DEFINE INDIVIDUAL STRATUM AREAS
stratum <- sort(unique(surv_random$log.mat[,4]))
STRATUM_AREA <- na.omit(surv_random$cells_per_strata) # old way: rep(10000/nstrata,nstrata) #100x100 grid so each corner has area 2500
sv.area <- as_tibble(data.frame(stratum,STRATUM_AREA))
#remove stratum that species does not occupy
sv.area <- sv.area[(sv.area$stratum %in% strata_species[[short_names[s]]]),]#sv.area %>% slice(-exclude)
#remove strata to exclude from stratified mean calculation
sv.area <- sv.area[!(sv.area$stratum %in% exclude[[s]]),]#sv.area %>% slice(-exclude)
spp <- as_tibble(list_all[[iter]],header=T) #pull out entire survey matrix
##remove strata to exclude from stratified mean calculation
spp <- spp[(spp$stratum %in% strata_species[[short_names[s]]]),]
spp <- spp[!(spp$stratum %in% exclude[[s]]),]
spp$year <- as.numeric(spp$year)
# get total area of stock ====
spp.strata <- unique(spp$stratum)
spp.strata <- as.numeric(spp.strata)
spp.area <- sum(sv.area$STRATUM_AREA[sv.area$stratum %in% spp.strata]) #TOTAL AREA OF ALL STRATA
temp <- srs_survey(df=spp, sa=sv.area, str=NULL, ta=1, sppname = paste0(short_names[s],"_samp", sep="") ) # if strata=NULL, the function will use the unique strata set found in df
# View(temp)
strat_mean_all[[s]][[iter]] <- temp %>%
mutate(mean.yr.absolute=mean.yr*spp.area, sd.mean.yr.absolute=sd.mean.yr*spp.area,
CV.absolute=sd.mean.yr.absolute/mean.yr.absolute) # if strata=NULL, the function will use the unique strata set found in df
strat_mean_all[[s]][[iter]] <- data.matrix(strat_mean_all[[s]][[iter]])
}
colnames(strat_mean_all[[s]][[iter]]) <- c("year","mean.yr","var.mean.yr","sd.mean.yr","CV","season","mean.yr.absolute","sd.mean.yr.absolute","CV.absolute")
}
#initial scenario folder
dir.create( paste0(getwd(),"/VAST/",scenario)) #create folder to store upcoming subfolders
names(strat_mean_all) <- short_names
saveRDS(strat_mean_all,paste0(getwd(),"/VAST/",scenario,"/strat_mean_all_",scenario,".RDS"))
names(strat_mean_all) <- short_names
if(exclude_strata == FALSE){
strat_mean_all_allstrata <- strat_mean_all
}
if(exclude_strata == TRUE){
strat_mean_all_exclude <- strat_mean_all
}
##########################################################################################
# Preparing things to plot
##########################################################################################
#copying plot_pop_summary to summarize yearly population estimates
#assumes we have summarized version of simulations called res
results_df <- list()
for(iter in seq(length(list_all))){
res <- result[[iter]]
n_spp <- length(res[["pop_summary"]])
res_df <- lapply(seq_len(n_spp), function(x) {
res_spp <- lapply(names(res[["pop_summary"]][[x]]), function(x1) {
x1_res <- tidyr::gather(as.data.frame(t(res[["pop_summary"]][[x]][[x1]])), key = "year", factor_key = T)
if(x1 == "Bio.mat" | x1 == "Bio.mat.sd") { res_out <- data.frame("pop" = rep(short_names[[x]], length.out = nrow(x1_res)),
"metric" = x1,
"year" = as.numeric(x1_res$year),
"day" = rep(1:358, length.out = nrow(x1_res)),#changed 362 to 358
"julien_day" = seq_len(nrow(x1_res)),
"data" = x1_res$value)
return(res_out)
}
if(x1 == "Rec.mat" | x1 == "Rec.mat.sd") { res_out <- data.frame("pop" = rep(short_names[[x]], length.out = nrow(x1_res)),
"metric" = x1 ,
"year" = as.numeric(seq_len(nrow(x1_res))),
"day" = rep(1, length.out = nrow(x1_res)),
"julien_day" = rep(1, length.out = nrow(x1_res)),
"data" = x1_res$value)
return(res_out)
}
})
return(do.call(rbind, res_spp))
})
results_df[[iter]] <- do.call(rbind, res_df)
}
# #ANNUAL POP BY SPECIES
annual_species <- list()
for(iter in seq(length(list_all))){
for(s in seq(length(short_names))){
annual_species[[short_names[s]]][[iter]] <- results_df[[iter]] %>% filter(metric == "Bio.mat", day == 1, pop == short_names[s]) %>%
group_by(pop,year) %>% summarise(data = sum(data))
}
}
pdf(file=paste("Results/GB_error_plots/Individual_SRS_",scenario,".pdf",sep=""))
nyears <- 20
#for error calculation
if(exclude_strata == TRUE){
SRS_error_spring_exclude <- list()
SRS_error_fall_exclude <- list()
SRS_spring_exclude <- list()
SRS_fall_exclude <- list()}
if(exclude_strata == FALSE){
SRS_error_spring_allstrata <- list()
SRS_error_fall_allstrata <- list()
SRS_spring_allstrata <- list()
SRS_fall_allstrata <- list()}
#plot stratified calculation and population estimate on same plot
#first make model output have 2 seasons to match the stratified mean calcs
for(iter in seq(length(list_all))){
print(iter)
for(s in seq(length(annual_species))){
annual_species[[short_names[s]]][[iter]] <- rbind(annual_species[[short_names[s]]][[iter]][3:22,],annual_species[[short_names[s]]][[iter]][3:22,])
annual_species[[short_names[s]]][[iter]]$season <- c(rep(1,nyears),rep(2,nyears)) #spring = season 1, fall = season 2
annual_species[[short_names[s]]][[iter]]$year <- as.numeric(rep(seq(3,22),2))
}
SRS_data <- list()
for(s in short_names){
#pull out strat mean calc
ifelse(exclude_strata == FALSE, SRS_data[[s]] <- strat_mean_all_allstrata[[s]][[iter]], SRS_data[[s]] <- strat_mean_all_exclude[[s]][[iter]])
#model value (old way)
#model[[s]][[iter]] <- annual_species[[s]][[iter]][annual_species[[s]][[iter]]$season==1,"data"]
#
# #SRS spring estimate
# SRS_spring[[s]][[iter]] <- strat_mean_all[[s]][[iter]][strat_mean_all[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#
# #SRS fall estimate
# SRS_fall[[s]][[iter]] <- strat_mean_all[[s]][[iter]][strat_mean_all[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#
# #calculate SPRING error from each iteration
# SRS_error_spring[[s]][[iter]] <- norm(model[[s]][[iter]] - SRS_spring[[s]][[iter]] , type="2") / norm(model[[s]][[iter]] , type ="2")
#
# #calculate FALL error from each iteration
# SRS_error_fall[[s]][[iter]] <- norm(model[[s]][[iter]] - SRS_fall[[s]][[iter]] , type="2") / norm(model[[s]][[iter]] , type ="2")
#
model_spring = pop_by_season[[s]][[iter]][pop_by_season[[s]][[iter]]$season==1,"biomass"]
model_fall = pop_by_season[[s]][[iter]][pop_by_season[[s]][[iter]]$season==2,"biomass"]
if(exclude_strata == FALSE){
#SRS spring estimate
SRS_spring_allstrata[[s]][[iter]] <- strat_mean_all_allstrata[[s]][[iter]][strat_mean_all_allstrata[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#SRS fall estimate
SRS_fall_allstrata[[s]][[iter]] <- strat_mean_all_allstrata[[s]][[iter]][strat_mean_all_allstrata[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#calculate SPRING error from each iteration
SRS_error_spring_allstrata[[s]][[iter]] <- norm(model_spring- SRS_spring_allstrata[[s]][[iter]] , type="2") / norm(model_spring , type ="2")
#calculate FALL error from each iteration
SRS_error_fall_allstrata[[s]][[iter]] <- norm(model_fall - SRS_fall_allstrata[[s]][[iter]] , type="2") / norm(model_fall , type ="2")
}
if(exclude_strata == TRUE){
#SRS spring estimate
SRS_spring_exclude[[s]][[iter]] <- strat_mean_all_exclude[[s]][[iter]][strat_mean_all_exclude[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#SRS fall estimate
SRS_fall_exclude[[s]][[iter]] <- strat_mean_all_exclude[[s]][[iter]][strat_mean_all_exclude[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#calculate SPRING error from each iteration
SRS_error_spring_exclude[[s]][[iter]] <- norm(model_spring- SRS_spring_exclude[[s]][[iter]] , type="2") / norm(model_spring , type ="2")
#calculate FALL error from each iteration
SRS_error_fall_exclude[[s]][[iter]] <- norm(model_fall - SRS_fall_exclude[[s]][[iter]] , type="2") / norm(model_fall , type ="2")
}
}
long_names <- c("Yellowtail Flounder","Cod","Haddock")
#par(mfrow = c(1,3), mar = c(1, 1, 1, 1))
#for(s in short_names){
#
# #OLD WAY
# #initiate ggplot
# p<- ggplot() +
# #plot stratified calculation data
# geom_errorbar(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
# geom_point(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
# geom_line(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
# #plot model data
# geom_point(data = as.data.frame(annual_species[[s]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
# geom_line(data = as.data.frame(annual_species[[s]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#
# facet_wrap(~ season) +
# labs(x="year",y="Biomass", title = long_names[idx], color ="" )
# idx<-idx+1
#
# print(p)
#}
#NEW WAY PLOTTING 3 TOGETHER ON SAME PAGE
#YTF
p1<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[1]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[1]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[1]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[1]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[1], color ="" )
#COD
p2<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[2]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[2]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[2]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[2]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[2], color ="" )
#HAD
p3<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[3]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[3]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[3]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[3]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[3], color ="" )
gridExtra::grid.arrange(p1,p2,p3,nrow=3)
}
dev.off()
if(exclude_strata == FALSE){
df_spring_allstrata <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring_allstrata[[1]], SRS_error_spring_allstrata[[2]], SRS_error_spring_allstrata[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Type = rep(rep("All Strata",length(list_all)),n_spp)
)
df_fall_allstrata <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall_allstrata[[1]], SRS_error_fall_allstrata[[2]], SRS_error_fall_allstrata[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Type = rep(rep("All Strata",length(list_all)),n_spp)
)
}
if(exclude_strata == TRUE){
df_spring_exclude <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring_exclude[[1]], SRS_error_spring_exclude[[2]], SRS_error_spring_exclude[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Type = rep(rep("Exclude Strata",length(list_all)),n_spp)
)
df_fall_exclude <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall_exclude[[1]], SRS_error_fall_exclude[[2]], SRS_error_fall_exclude[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Type = rep(rep("Exclude Strata",length(list_all)),n_spp)
)
}
ifelse(exclude_strata == FALSE, df_allstrata <- rbind(df_fall_allstrata,df_spring_allstrata), df_exclude <- rbind(df_fall_exclude,df_spring_exclude))
#create data frame containing mean values for each group
if(exclude_strata == FALSE){
means_sd_allstrata <- ddply(df_allstrata, .(species,season), summarise, mean = mean(as.numeric(error)), std_dev = sd(as.numeric(error)), Type = Type)
}
if(exclude_strata == TRUE){
means_sd_exclude <- ddply(df_exclude, .(species,season), summarise, mean = mean(as.numeric(error)), std_dev = sd(as.numeric(error)), Type = Type)
}
#Error scatterplots
#1) to plot a single scenario, run desired section below
#2) to plot two scenarios on top of each other, run both scenarios and then the bottom most chunk benlow
#PLOT JUST ALL STRATA RESULTS
library(ggplot2)
allstrata_scatter <-ggplot(data=df_allstrata,
aes(x=iter,y=as.numeric(error))) +
geom_point(color="blue")+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean), data = means_sd_allstrata, color = "blue")
print(allstrata_scatter)
#PLOT JUST EXCLUDE STRATA RESULTS
library(ggplot2)
exclude_scatter <-ggplot(data=df_exclude,
aes(x=iter,y=as.numeric(error))) +
geom_point(color="blue")+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean), data = means_sd_exclude, color = "blue")
print(exclude_scatter)
#
# ggsave(filename = paste("Results/GB_error_plots/Individussssal_SRS_",scenario,".pdf",sep=""),
# plot = last_plot())
#
#PLOT BOTH SCATTERPLOTS TOGETHER
df_both <- rbind(df_allstrata,df_exclude)
means_sd_both <- rbind(means_sd_allstrata,means_sd_exclude)
library(ggplot2)
both_scat <-ggplot(data=df_both,
aes(x=iter,y=as.numeric(unlist(error)),color=Type)) +
geom_point()+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean, color = Type), data = means_sd_both)
print(both_scat)
#calculate change in mean value between all strata and exclude strata
#YTF
YTF_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="YTF") & (means_sd_allstrata$season=="spring")), 3 ])
YTF_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="YTF") & (means_sd_exclude$season=="spring")), 3 ])
YTF_mean_change_spring <- YTF_mean_spring_all - YTF_mean_spring_exclude
YTF_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="YTF") & (means_sd_allstrata$season=="fall")), 3 ])
YTF_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="YTF") & (means_sd_exclude$season=="fall")), 3 ])
YTF_mean_change_fall <- YTF_mean_fall_all - YTF_mean_fall_exclude
#Cod
Cod_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Cod") & (means_sd_allstrata$season=="spring")), 3 ])
Cod_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Cod") & (means_sd_exclude$season=="spring")), 3 ])
Cod_mean_change_spring <- Cod_mean_spring_all - Cod_mean_spring_exclude
Cod_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Cod") & (means_sd_allstrata$season=="fall")), 3 ])
Cod_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Cod") & (means_sd_exclude$season=="fall")), 3 ])
Cod_mean_change_fall <- Cod_mean_fall_all - Cod_mean_fall_exclude
#Had
Had_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Had") & (means_sd_allstrata$season=="spring")), 3 ])
Had_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Had") & (means_sd_exclude$season=="spring")), 3 ])
Had_mean_change_spring <- Had_mean_spring_all - Had_mean_spring_exclude
Had_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Had") & (means_sd_allstrata$season=="fall")), 3 ])
Had_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Had") & (means_sd_exclude$season=="fall")), 3 ])
Had_mean_change_fall <- Had_mean_fall_all - Had_mean_fall_exclude
#view mean and sd values
View(unique(means_sd_both))
remove(result)
Blevy2/READ-PDB-blevy2-MFS2 documentation built on Nov. 29, 2023, 11:48 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#THIS SCRIPT IS A PIECE OF RUN_SURVEY_BENS_NEW WHERE THE STRATIFIED MEAN CALCULATION IS MADE
#THIS SCRIPT ALLOWS US TO CALULATE THE STRATIFIED MEAN FOR A SMALLER NUMER OF NOISELESS SURVEYS
##################################################################################################
#THINGS WE NEED
##################################################################################################
scenario <- "DecPop_IncTemp"
n_spp <- 3
#survey results without noise
list_all <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\list_all_",scenario,".RDS",sep=""))
#simulation results
result <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\result_",scenario,".RDS",sep=""))
#random survey locations
surv_random <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\surv_random_",scenario,".RDS",sep=""))
#spp1 spp2 spp3
short_names <- c("YT","Cod","Had")
#strata that each species occupies. Used to calculate stratified random mean of each
strata_species <- list()
strata_species[["YT"]] <- c(13,14,15,16,17,18,19,20,21)
strata_species[["Cod"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25)
strata_species[["Had"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25,29,30)
##################################################################################################
library(raster)
library(sp)
library(TMB)
library(VAST)
library(dplyr)
library(ggplot2)
#read in habitat matrix
hab <- readRDS(file="hab_GB_3species.RDS") #courser resolution
#read in GB strata
#haddock contains all stratas used
Had_ras <- readRDS(file="TestScripts/Habitat_plots/Haddock/Had_Weighted_AdaptFalse_RASTER_res2.RDS")
#plot(Had_ras)
#translate habitat matrix back into raster
hab_ras <-raster(hab$hab$spp3)
extent(hab_ras) <- extent(Had_ras)
#plot(hab_ras)
#ADD TRUE MODEL POPULATION VALUES TO SURVEY DATA TABLES
for(iter in seq(length(list_all))){
print(iter)
temp <- matrix(data=0,nrow=length(list_all[[iter]][,1]),ncol=n_spp)
lat <- vector()
lon <- vector()
for(samp in seq(length(list_all[[iter]][,1]))){
#ADDING TRUE POPULATION
x = as.numeric(list_all[[iter]][samp,2]) #x in second column
y = as.numeric(list_all[[iter]][samp,3]) #y in third column
wk = as.numeric(list_all[[iter]][samp,11]) #week in 11th column
yr = as.numeric(list_all[[iter]][samp,7]) #year in 7th column
temp[samp,1] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp1"]],na.rm=T) #YT is spp1
temp[samp,2] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp2"]],na.rm=T) #Cod is spp2
temp[samp,3] <- sum(result[[iter]]$pop_bios[[(wk+(52*(yr-1)))]][["spp3"]],na.rm=T) #Had is spp3
#ADDING LAT LON LOCATIONS
rw <- as.numeric(list_all[[iter]][samp,"x"]) #x in col 2
cl <- as.numeric(list_all[[iter]][samp,"y"]) #y in col 3
lon[samp] <- xFromCol(hab_ras, col = cl)
lat[samp] <- yFromRow(hab_ras, row = rw)
}
temp <- cbind(temp,lat,lon)
colnames(temp) <- c("YT","Cod","Had","Latitude","Longitude")
list_all[[iter]] <- cbind(list_all[[iter]],temp)
colnames(list_all[[iter]]) <- c("station_no","x","y","stratum","day","tow","year","YT_samp","Cod_samp","Had_samp","week","Season","YT_pop","Cod_pop","Had_pop","Lat","Lon")
}
#FIND MEAN VALUE BY SEASON USING ABOVE INFORMATION. USE MEAN OF TWO SURVEY WEEKS FOR EACH SEASON
season_wks <- list(c(13,14),c(37,38))
pop_by_season <- list()
for(iter in seq(length(list_all))){
for(s in short_names){
temp <- data.frame()
idx <- 1
for(yr in seq(3,22)){
for(season in seq(2)){
temp[idx,1] <- yr
temp[idx,2] <- season
#use values in given year for weeks in specified season. only use single strata because entire population summarized in each strata in above loop
temp[idx,3] <- mean(as.numeric(list_all[[iter]][((as.numeric(list_all[[iter]][,"year"]==yr)) & (as.numeric(list_all[[iter]][,"week"]) %in% season_wks[[season]]) & (as.numeric(list_all[[iter]][,"stratum"]==29)) ),paste(s,"_pop",sep="")]))
idx <- idx + 1
}
}
colnames(temp) <- c("year","season","biomass")
pop_by_season[[s]][[iter]] <- temp
}
}
#BELOW WILL TAKE A MINUTE
#choose some strata to exclude, if desired
exclude_strata <- TRUE
ifelse(exclude_strata,
#George's Bank Setup by species
#YT Cod Haddock
exclude <- list(c(13,14,15,17,18), c(23,24,25), c(23,24,25,29,30)),
#exclude none
exclude <- list(c(0),c(0),c(0)) #3 species
)
#NOW WE NEED TO CREATE A STRATIFIED MEAN FROM EACH OF THESE SAMPLES
#there are #strata * #iterations * #samp_per_iter total samples
#I AM COPYING FROM CALC_SRS_INDEX_SURVEY_BENS, which was adapted from Liz's code to create below
library(tibble)
library(ggplot2)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(here)
#stop output from below using this option
options(dplyr.summarise.inform = FALSE)
#load file to calculate the stratified mean
source("TestScripts/Calc_strat_mean/fn_srs_survey_BENS.R")
#setup dimensions for each species- 1 for each strata
strat_mean_all <- vector("list",length(seq(n_spp)))
for(s in seq(n_spp)){
strat_mean_all[[s]] <- vector("list",length(list_all))
}
#go through each strata survey, iteration, sample
for(iter in seq(length(list_all))){
print(iter)
#if any NA columns make them zero
list_all[[iter]][is.na(list_all[[iter]])]=0
# calculate SRS estimates ====
for(s in seq(n_spp)){
#DEFINE INDIVIDUAL STRATUM AREAS
stratum <- sort(unique(surv_random$log.mat[,4]))
STRATUM_AREA <- na.omit(surv_random$cells_per_strata) # old way: rep(10000/nstrata,nstrata) #100x100 grid so each corner has area 2500
sv.area <- as_tibble(data.frame(stratum,STRATUM_AREA))
#remove stratum that species does not occupy
sv.area <- sv.area[(sv.area$stratum %in% strata_species[[short_names[s]]]),]#sv.area %>% slice(-exclude)
#remove strata to exclude from stratified mean calculation
sv.area <- sv.area[!(sv.area$stratum %in% exclude[[s]]),]#sv.area %>% slice(-exclude)
spp <- as_tibble(list_all[[iter]],header=T) #pull out entire survey matrix
##remove strata to exclude from stratified mean calculation
spp <- spp[(spp$stratum %in% strata_species[[short_names[s]]]),]
spp <- spp[!(spp$stratum %in% exclude[[s]]),]
spp$year <- as.numeric(spp$year)
# get total area of stock ====
spp.strata <- unique(spp$stratum)
spp.strata <- as.numeric(spp.strata)
spp.area <- sum(sv.area$STRATUM_AREA[sv.area$stratum %in% spp.strata]) #TOTAL AREA OF ALL STRATA
temp <- srs_survey(df=spp, sa=sv.area, str=NULL, ta=1, sppname = paste0(short_names[s],"_samp", sep="") ) # if strata=NULL, the function will use the unique strata set found in df
# View(temp)
strat_mean_all[[s]][[iter]] <- temp %>%
mutate(mean.yr.absolute=mean.yr*spp.area, sd.mean.yr.absolute=sd.mean.yr*spp.area,
CV.absolute=sd.mean.yr.absolute/mean.yr.absolute) # if strata=NULL, the function will use the unique strata set found in df
strat_mean_all[[s]][[iter]] <- data.matrix(strat_mean_all[[s]][[iter]])
}
colnames(strat_mean_all[[s]][[iter]]) <- c("year","mean.yr","var.mean.yr","sd.mean.yr","CV","season","mean.yr.absolute","sd.mean.yr.absolute","CV.absolute")
}
#initial scenario folder
dir.create( paste0(getwd(),"/VAST/",scenario)) #create folder to store upcoming subfolders
names(strat_mean_all) <- short_names
saveRDS(strat_mean_all,paste0(getwd(),"/VAST/",scenario,"/strat_mean_all_",scenario,".RDS"))
names(strat_mean_all) <- short_names
if(exclude_strata == FALSE){
strat_mean_all_allstrata <- strat_mean_all
}
if(exclude_strata == TRUE){
strat_mean_all_exclude <- strat_mean_all
}
##########################################################################################
# Preparing things to plot
##########################################################################################
#copying plot_pop_summary to summarize yearly population estimates
#assumes we have summarized version of simulations called res
results_df <- list()
for(iter in seq(length(list_all))){
res <- result[[iter]]
n_spp <- length(res[["pop_summary"]])
res_df <- lapply(seq_len(n_spp), function(x) {
res_spp <- lapply(names(res[["pop_summary"]][[x]]), function(x1) {
x1_res <- tidyr::gather(as.data.frame(t(res[["pop_summary"]][[x]][[x1]])), key = "year", factor_key = T)
if(x1 == "Bio.mat" | x1 == "Bio.mat.sd") { res_out <- data.frame("pop" = rep(short_names[[x]], length.out = nrow(x1_res)),
"metric" = x1,
"year" = as.numeric(x1_res$year),
"day" = rep(1:358, length.out = nrow(x1_res)),#changed 362 to 358
"julien_day" = seq_len(nrow(x1_res)),
"data" = x1_res$value)
return(res_out)
}
if(x1 == "Rec.mat" | x1 == "Rec.mat.sd") { res_out <- data.frame("pop" = rep(short_names[[x]], length.out = nrow(x1_res)),
"metric" = x1 ,
"year" = as.numeric(seq_len(nrow(x1_res))),
"day" = rep(1, length.out = nrow(x1_res)),
"julien_day" = rep(1, length.out = nrow(x1_res)),
"data" = x1_res$value)
return(res_out)
}
})
return(do.call(rbind, res_spp))
})
results_df[[iter]] <- do.call(rbind, res_df)
}
# #ANNUAL POP BY SPECIES
annual_species <- list()
for(iter in seq(length(list_all))){
for(s in seq(length(short_names))){
annual_species[[short_names[s]]][[iter]] <- results_df[[iter]] %>% filter(metric == "Bio.mat", day == 1, pop == short_names[s]) %>%
group_by(pop,year) %>% summarise(data = sum(data))
}
}
pdf(file=paste("Results/GB_error_plots/Individual_SRS_",scenario,".pdf",sep=""))
nyears <- 20
#for error calculation
if(exclude_strata == TRUE){
SRS_error_spring_exclude <- list()
SRS_error_fall_exclude <- list()
SRS_spring_exclude <- list()
SRS_fall_exclude <- list()}
if(exclude_strata == FALSE){
SRS_error_spring_allstrata <- list()
SRS_error_fall_allstrata <- list()
SRS_spring_allstrata <- list()
SRS_fall_allstrata <- list()}
#plot stratified calculation and population estimate on same plot
#first make model output have 2 seasons to match the stratified mean calcs
for(iter in seq(length(list_all))){
print(iter)
for(s in seq(length(annual_species))){
annual_species[[short_names[s]]][[iter]] <- rbind(annual_species[[short_names[s]]][[iter]][3:22,],annual_species[[short_names[s]]][[iter]][3:22,])
annual_species[[short_names[s]]][[iter]]$season <- c(rep(1,nyears),rep(2,nyears)) #spring = season 1, fall = season 2
annual_species[[short_names[s]]][[iter]]$year <- as.numeric(rep(seq(3,22),2))
}
SRS_data <- list()
for(s in short_names){
#pull out strat mean calc
ifelse(exclude_strata == FALSE, SRS_data[[s]] <- strat_mean_all_allstrata[[s]][[iter]], SRS_data[[s]] <- strat_mean_all_exclude[[s]][[iter]])
#model value (old way)
#model[[s]][[iter]] <- annual_species[[s]][[iter]][annual_species[[s]][[iter]]$season==1,"data"]
#
# #SRS spring estimate
# SRS_spring[[s]][[iter]] <- strat_mean_all[[s]][[iter]][strat_mean_all[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#
# #SRS fall estimate
# SRS_fall[[s]][[iter]] <- strat_mean_all[[s]][[iter]][strat_mean_all[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#
# #calculate SPRING error from each iteration
# SRS_error_spring[[s]][[iter]] <- norm(model[[s]][[iter]] - SRS_spring[[s]][[iter]] , type="2") / norm(model[[s]][[iter]] , type ="2")
#
# #calculate FALL error from each iteration
# SRS_error_fall[[s]][[iter]] <- norm(model[[s]][[iter]] - SRS_fall[[s]][[iter]] , type="2") / norm(model[[s]][[iter]] , type ="2")
#
model_spring = pop_by_season[[s]][[iter]][pop_by_season[[s]][[iter]]$season==1,"biomass"]
model_fall = pop_by_season[[s]][[iter]][pop_by_season[[s]][[iter]]$season==2,"biomass"]
if(exclude_strata == FALSE){
#SRS spring estimate
SRS_spring_allstrata[[s]][[iter]] <- strat_mean_all_allstrata[[s]][[iter]][strat_mean_all_allstrata[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#SRS fall estimate
SRS_fall_allstrata[[s]][[iter]] <- strat_mean_all_allstrata[[s]][[iter]][strat_mean_all_allstrata[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#calculate SPRING error from each iteration
SRS_error_spring_allstrata[[s]][[iter]] <- norm(model_spring- SRS_spring_allstrata[[s]][[iter]] , type="2") / norm(model_spring , type ="2")
#calculate FALL error from each iteration
SRS_error_fall_allstrata[[s]][[iter]] <- norm(model_fall - SRS_fall_allstrata[[s]][[iter]] , type="2") / norm(model_fall , type ="2")
}
if(exclude_strata == TRUE){
#SRS spring estimate
SRS_spring_exclude[[s]][[iter]] <- strat_mean_all_exclude[[s]][[iter]][strat_mean_all_exclude[[s]][[iter]][,"season"]==1,"mean.yr.absolute"]
#SRS fall estimate
SRS_fall_exclude[[s]][[iter]] <- strat_mean_all_exclude[[s]][[iter]][strat_mean_all_exclude[[s]][[iter]][,"season"]==2,"mean.yr.absolute"]
#calculate SPRING error from each iteration
SRS_error_spring_exclude[[s]][[iter]] <- norm(model_spring- SRS_spring_exclude[[s]][[iter]] , type="2") / norm(model_spring , type ="2")
#calculate FALL error from each iteration
SRS_error_fall_exclude[[s]][[iter]] <- norm(model_fall - SRS_fall_exclude[[s]][[iter]] , type="2") / norm(model_fall , type ="2")
}
}
long_names <- c("Yellowtail Flounder","Cod","Haddock")
#par(mfrow = c(1,3), mar = c(1, 1, 1, 1))
#for(s in short_names){
#
# #OLD WAY
# #initiate ggplot
# p<- ggplot() +
# #plot stratified calculation data
# geom_errorbar(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
# geom_point(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
# geom_line(data=as.data.frame(SRS_data[[s]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
# #plot model data
# geom_point(data = as.data.frame(annual_species[[s]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
# geom_line(data = as.data.frame(annual_species[[s]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#
# facet_wrap(~ season) +
# labs(x="year",y="Biomass", title = long_names[idx], color ="" )
# idx<-idx+1
#
# print(p)
#}
#NEW WAY PLOTTING 3 TOGETHER ON SAME PAGE
#YTF
p1<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[1]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[1]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[1]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[1]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[1]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[1], color ="" )
#COD
p2<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[2]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[2]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[2]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[2]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[2]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[2], color ="" )
#HAD
p3<- ggplot() +
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
geom_point(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[3]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
#plot model data
#this way plots annual data
#geom_point(data = as.data.frame(annual_species[[3]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#geom_line(data = as.data.frame(annual_species[[3]][[iter]]), aes(x=as.numeric(year),y=data, group =season, color = "Model")) +
#this way plots data by season
geom_point(data = as.data.frame(pop_by_season[[3]][[iter]]), aes(x=as.numeric(year),y=biomass, group = season, color = "Model")) +
geom_line(data = as.data.frame(pop_by_season[[3]][[iter]]), aes(x=as.numeric(year),y=biomass, group =season, color = "Model")) +
facet_wrap(~ season) +
labs(x="year",y="Biomass", title = long_names[3], color ="" )
gridExtra::grid.arrange(p1,p2,p3,nrow=3)
}
dev.off()
if(exclude_strata == FALSE){
df_spring_allstrata <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring_allstrata[[1]], SRS_error_spring_allstrata[[2]], SRS_error_spring_allstrata[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Type = rep(rep("All Strata",length(list_all)),n_spp)
)
df_fall_allstrata <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall_allstrata[[1]], SRS_error_fall_allstrata[[2]], SRS_error_fall_allstrata[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Type = rep(rep("All Strata",length(list_all)),n_spp)
)
}
if(exclude_strata == TRUE){
df_spring_exclude <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring_exclude[[1]], SRS_error_spring_exclude[[2]], SRS_error_spring_exclude[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Type = rep(rep("Exclude Strata",length(list_all)),n_spp)
)
df_fall_exclude <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall_exclude[[1]], SRS_error_fall_exclude[[2]], SRS_error_fall_exclude[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Type = rep(rep("Exclude Strata",length(list_all)),n_spp)
)
}
ifelse(exclude_strata == FALSE, df_allstrata <- rbind(df_fall_allstrata,df_spring_allstrata), df_exclude <- rbind(df_fall_exclude,df_spring_exclude))
#create data frame containing mean values for each group
if(exclude_strata == FALSE){
means_sd_allstrata <- ddply(df_allstrata, .(species,season), summarise, mean = mean(as.numeric(error)), std_dev = sd(as.numeric(error)), Type = Type)
}
if(exclude_strata == TRUE){
means_sd_exclude <- ddply(df_exclude, .(species,season), summarise, mean = mean(as.numeric(error)), std_dev = sd(as.numeric(error)), Type = Type)
}
#Error scatterplots
#1) to plot a single scenario, run desired section below
#2) to plot two scenarios on top of each other, run both scenarios and then the bottom most chunk benlow
#PLOT JUST ALL STRATA RESULTS
library(ggplot2)
allstrata_scatter <-ggplot(data=df_allstrata,
aes(x=iter,y=as.numeric(error))) +
geom_point(color="blue")+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean), data = means_sd_allstrata, color = "blue")
print(allstrata_scatter)
#PLOT JUST EXCLUDE STRATA RESULTS
library(ggplot2)
exclude_scatter <-ggplot(data=df_exclude,
aes(x=iter,y=as.numeric(error))) +
geom_point(color="blue")+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean), data = means_sd_exclude, color = "blue")
print(exclude_scatter)
#
# ggsave(filename = paste("Results/GB_error_plots/Individussssal_SRS_",scenario,".pdf",sep=""),
# plot = last_plot())
#
#PLOT BOTH SCATTERPLOTS TOGETHER
df_both <- rbind(df_allstrata,df_exclude)
means_sd_both <- rbind(means_sd_allstrata,means_sd_exclude)
library(ggplot2)
both_scat <-ggplot(data=df_both,
aes(x=iter,y=as.numeric(unlist(error)),color=Type)) +
geom_point()+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean, color = Type), data = means_sd_both)
print(both_scat)
#calculate change in mean value between all strata and exclude strata
#YTF
YTF_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="YTF") & (means_sd_allstrata$season=="spring")), 3 ])
YTF_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="YTF") & (means_sd_exclude$season=="spring")), 3 ])
YTF_mean_change_spring <- YTF_mean_spring_all - YTF_mean_spring_exclude
YTF_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="YTF") & (means_sd_allstrata$season=="fall")), 3 ])
YTF_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="YTF") & (means_sd_exclude$season=="fall")), 3 ])
YTF_mean_change_fall <- YTF_mean_fall_all - YTF_mean_fall_exclude
#Cod
Cod_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Cod") & (means_sd_allstrata$season=="spring")), 3 ])
Cod_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Cod") & (means_sd_exclude$season=="spring")), 3 ])
Cod_mean_change_spring <- Cod_mean_spring_all - Cod_mean_spring_exclude
Cod_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Cod") & (means_sd_allstrata$season=="fall")), 3 ])
Cod_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Cod") & (means_sd_exclude$season=="fall")), 3 ])
Cod_mean_change_fall <- Cod_mean_fall_all - Cod_mean_fall_exclude
#Had
Had_mean_spring_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Had") & (means_sd_allstrata$season=="spring")), 3 ])
Had_mean_spring_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Had") & (means_sd_exclude$season=="spring")), 3 ])
Had_mean_change_spring <- Had_mean_spring_all - Had_mean_spring_exclude
Had_mean_fall_all <- unique(means_sd_allstrata[((means_sd_allstrata$species=="Had") & (means_sd_allstrata$season=="fall")), 3 ])
Had_mean_fall_exclude <- unique(means_sd_exclude[((means_sd_exclude$species=="Had") & (means_sd_exclude$season=="fall")), 3 ])
Had_mean_change_fall <- Had_mean_fall_all - Had_mean_fall_exclude
#view mean and sd values
View(unique(means_sd_both))
remove(result)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.