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load_results.R
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load_results.R
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## Source this data to load in the results and do any processing before
## making figures and tables.
## Load the results from section 1
Linf.df <- data.frame(species=factor(c('cod', 'flatfish', 'yellow-long')), Linf=c(132,47,62))
popbins.binwidth.scalars <- readRDS(file='results/popbins.binwidth.scalars.RData')
popbins.binwidth.ts <- readRDS(file='results/popbins.binwidth.ts.RData')
popbins.minsize <- readRDS(file='results/popbins.minsize.RData')
popbins.maxsize <- readRDS(file='results/popbins.maxsize.RData')
popbins.maxsize <- merge(Linf.df, popbins.maxsize, by='species')
popbins.maxsize$max.Linf.ratio <- with(popbins.maxsize, maxsize/Linf)
popbins.k.scalars <- readRDS(file='results/popbins.k.scalars.RData')
popbins.Linf.scalars <- readRDS(file='results/popbins.Linf.scalars.RData')
popbins.Lmin.scalars <- readRDS(file='results/popbins.Lmin.scalars.RData')
### ------------------------------------------------------------
### Step 1: Make some quick data.frames to rename ggplot factors
bin.cases.df <-
rbind(data.frame(species=c('cod'),
dbin=paste0("dbin=",c(1,2,5,10,20, 2,5,10,20)),
pbin=paste0("pbin=", c(1,1,1,1,1, 2,5,10,20)),
B=paste0("B",B.binning)),
data.frame(species=c('flatfish'),
dbin=paste0("dbin=",c(1,2,5,10,20, 2,5,10,20)),
pbin=paste0("pbin=", c(1,1,1,1,1, 2,5,10,20)),
B=paste0("B",B.binning)),
data.frame(species=c('yellow-long'),
dbin=paste0("dbin=",c(1,2,5,10,20, 2,5,10,20)),
pbin=paste0("pbin=", c(1,1,1,1,1, 2,5,10,20)),
B=paste0("B",B.binning)))
data.cases.df <-
data.frame(D=paste0("D",D.binning), data=c("Rich:A+L", "Rich:C+L", "Poor:A+L", "Poor:C+L"))
tcomp.cases.df <-
data.frame(species=rep(c('cod','flatfish','yellow-long'),each=4),
tvalue=paste0("tcomp=", c(-1, 1e-3, 0.01, 0.05)),
I=paste0("I",11:14))
robust.cases.df <-
data.frame(species=rep(c('cod','flatfish','yellow-long'),each=4),
rvalue=paste0("robust=", c(1e-10,1e-5,1e-3,0.01)),
I=paste0("I", 21:24))
## reorder for plotting
bin.cases.df$pbin <- factor(bin.cases.df$pbin, levels= c("pbin=1", "pbin=2", "pbin=4" , "pbin=5",
"pbin=10", "pbin=12", "pbin=20", "pbin=24"))
bin.cases.df$dbin <- factor(bin.cases.df$dbin, levels=c("dbin=1", "dbin=2", "dbin=4" , "dbin=5",
"dbin=10", "dbin=12", "dbin=20", "dbin=24"))
data.cases.df$data <- factor(data.cases.df$data, levels=c("Rich:A+L", "Rich:C+L", "Poor:A+L", "Poor:C+L"))
robust.cases.df$rvalue <- factor(robust.cases.df$rvalue,
levels=c("robust=1e-10", "robust=1e-05", "robust=0.001",
"robust=0.01"))
### End renaming data.frames
### ------------------------------------------------------------
### ------------------------------------------------------------
### Step 2: Load and prep the binning results
## binning section
binning <- readRDS("results/results_binning.sc.RData")
binning$log_max_grad <- log(binning$max_grad)
binning$converged <- ifelse(binning$max_grad<.1 & binning$params_on_bound_em==0, "yes", "no")
binning <- ddply(binning, "scenario", mutate, pct.converged=mean(converged=="yes"))
binning <- calculate_re(binning, add=TRUE)
binning$runtime <- (binning$RunTime)
binning <- merge(binning, bin.cases.df, by=c("species", "B"))
binning <- merge(binning, data.cases.df, by=c("D"))
binning$data.binwidth <- with(binning, as.numeric(gsub("dbin=", "", x=dbin)))
binning$binmatch <-
with(binning, ifelse(pbin=="pbin=1", "pbin=1cm", "pbin=dbin"))
## Drop fixed params (columns of zeroes)
binning$RecrDist_GP_1_re <- NULL
binning <- binning[,-which(apply(binning, 2, function(x) all(x==0)))]
binning.unfiltered <- binning # before subetting by convergence
binning <- droplevels(subset(binning, converged=="yes"))
binning.counts <- ddply(binning, .(data, data.binwidth, binmatch,species), summarize,
replicates=length(scenario),
pct.converged=100*unique(pct.converged),
median.iterations=median(Niterations),
median.runtime=median(runtime))
binning.counts.normalized <- ddply(binning.counts, .(species, data), mutate,
runtime.normalized=median.runtime/median.runtime[which(data.binwidth==1)],
iterations.normalized=median.iterations/median.iterations[which(data.binwidth==1)])
binning.counts.normalized$match <- factor(binning.counts.normalized$binmatch)
levels(binning.counts.normalized$match) <- c("No", "Yes")
binning.runtime <- ddply(binning, .(data,B, species), summarize,
binmatch=binmatch[1],
dbin=dbin[1], pbin=pbin[1],
replicates=length(scenario),
mean.runtime=round(mean(runtime),2),
median.runtime=round(median(runtime),2),
sd.runtime=round(sd(runtime),2),
lower.runtime=quantile(runtime, probs=.25),
upper.runtime=quantile(runtime, probs=.75))
re.names <- names(binning)[grep("_re", names(binning))]
binning.long <-
melt(binning, measure.vars=re.names, id.vars=
c("species","replicate", "data", "B", "dbin", "binmatch",
"data.binwidth", "pct.converged",
"pbin", "log_max_grad", "params_on_bound_em",
"runtime"))
growth.names <- re.names[grep("GP_", re.names)]
binning.long.growth <- droplevels(subset(binning.long, variable %in% growth.names))
binning.long.growth$variable <- gsub("_Fem_GP_1_re|_re", "", binning.long.growth$variable)
selex.names <- re.names[grep("Sel_", re.names)]
binning.long.selex <- droplevels(subset(binning.long, variable %in% selex.names))
binning.long.selex$variable <- gsub("ery|ey|Size|_re", "", binning.long.selex$variable)
binning.long.selex$variable <- gsub("_", ".", binning.long.selex$variable)
management.names <- c("SSB_MSY_re", "depletion_re", "SSB_Unfished_re")
binning.long.management <- droplevels(subset(binning.long, variable %in% management.names))
binning.long.management$variable <- gsub("_re", "", binning.long.management$variable)
figure.names <- c(growth.names, "SSB_MSY_re", "depletion_re")
binning.long.figure <- droplevels(subset(binning.long, variable %in% figure.names))
binning.long.figure$variable <- gsub("_Fem_GP_1_re|_re", "", binning.long.figure$variable)
## ## This is my crazy way to get B0 on the x axis
temp <- dcast(binning.long.growth, value.var='value',
formula=species+data+variable+replicate~B)
binning.long.growth2 <-
melt(temp, measure.vars=levels(bin.cases.df$B)[-1],
variable.name="B" )
rm(temp)
## Now make running MARE calculations. Takes a while to run these calcs!!!
my.median <- function(x) {sapply(1:length(x), function(i) {median(x[1:i])})}
binning.long.growth.mares <-
group_by(binning.long.growth, species, data, B, variable) %>%
dplyr::mutate(
replicate2=1:length(replicate),
cMARE=my.median(abs(value))-median(abs(value)),
MARE=my.median(abs(value)))
binning.long.management.mares <-
group_by(binning.long.management, species, data, B, variable) %>%
dplyr::mutate(
replicate2=1:length(replicate),
cMARE=my.median(abs(value))-median(abs(value)),
MARE=my.median(abs(value)))
binning.long.selex.mares <-
group_by(binning.long.selex, species, data, B, variable) %>%
dplyr::mutate(
replicate2=1:length(replicate),
cMARE=my.median(abs(value))-median(abs(value)),
MARE=my.median(abs(value)))
## This is a crazy way to get which params get stuck for which scenarios
library("magrittr")
x.low <- paste(binning.unfiltered$params_stuck_low_em, collapse = ";") %>%
strsplit(";")
x.low <- unique(x.low[[1]])
x.low <- x.low[-which(x.low == "NA")]
stuck.low <- matrix(ncol = length(x.low), nrow = nrow(binning.unfiltered)) %>%
as.data.frame %>%
setNames(x.low)
for (i in seq_along(x.low)) {
stuck.low[,i] <- grepl(x.low[i], binning.unfiltered$params_stuck_low_em)}
binning.stuck.low <- cbind(binning.unfiltered[, c("species", "data.binwidth", "binmatch", "data")], stuck.low)
binning.stuck.low <- melt(binning.stuck.low, id.vars=c("species", "data.binwidth","binmatch", "data"))
binning.stuck.low <- ddply(binning.stuck.low, .(species, data, binmatch, data.binwidth, variable), summarize, pct.stuck=mean(value))
binning.stuck.low$which.bound <- "low"
x.high <- paste(binning.unfiltered$params_stuck_high_em, collapse = ";") %>%
strsplit(";")
x.high <- unique(x.high[[1]])
x.high <- x.high[-which(x.high == "NA")]
stuck.high <- matrix(ncol = length(x.high), nrow = nrow(binning.unfiltered)) %>%
as.data.frame %>%
setNames(x.high)
for (i in seq_along(x.high)) {
stuck.high[,i] <- grepl(x.high[i], binning.unfiltered$params_stuck_high_em)}
binning.stuck.high <- cbind(binning.unfiltered[, c("species", "data.binwidth", "binmatch", "data")], stuck.high)
binning.stuck.high <- melt(binning.stuck.high, id.vars=c("species", "data.binwidth","binmatch", "data"))
binning.stuck.high <- ddply(binning.stuck.high, .(species, data, binmatch, data.binwidth, variable), summarize, pct.stuck=mean(value))
binning.stuck.high$which.bound <- "high"
binning.stuck <- subset(rbind(binning.stuck.low, binning.stuck.high), pct.stuck>0)
binning.stuck$variable <- gsub("Size|_Fem_GP_1", "", binning.stuck$variable)
## ## Read in the bias adjustment parameters by iteration
## get_results_bias <- function(scenarios, directory=getwd()){
## temp <- list()
## for(sc in scenarios){
## bias.temp <- read.table(paste0(sc,"/bias/AdjustBias.DAT"), header=FALSE, sep=" ")
## names(bias.temp) <- c("iteration", paste0("bias", 1:5))
## bias.temp$scenario <- sc
## temp[[sc]] <- bias.temp
## }
## bias.all <- do.call(rbind, temp)
## row.names(bias.all) <- NULL
## bias.all$converged <- !is.na(bias.all$bias1)
## bias.all.long <- melt(bias.all, id.vars=c("iteration", "scenario", "converged"))
## return(bias.all.long)
## }
## bias.all.long <- get_results_bias(scen.all)
## bias.all.long$data <- do.call(rbind, strsplit(bias.all.long$scenario, split='-'))[,2]
## bias.all.long$B <- do.call(rbind, strsplit(bias.all.long$scenario, split='-'))[,1]
## bias.all.long$species <- do.call(rbind, strsplit(bias.all.long$scenario, split='-'))[,6]
## scenarios.converged <- ddply(bias.all.long, .(species, data, B), summarize,
## pct.converged=mean(converged))
## ## read in the time series data
## binning.ts <- readRDS("results/results_binning.ts.RData")
## binning.ts <- merge(binning.ts, subset(binning.unfiltered, select=c("ID",'converged')), by="ID")
## binning.ts <- merge(binning.ts, bin.cases.df, by=c("species", "B"))
## binning.ts <- merge(binning.ts, data.cases.df, by=c("D"))
## binning.ts <- subset(binning.ts, converged=="yes")
## binning.ts <- calculate_re(binning.ts, add=TRUE)
## End of binning results
### ------------------------------------------------------------
## ### ------------------------------------------------------------
## ### Step 3: Load and prep the tail compression and robustification data
## ## tail compression
## tcomp <- readRDS("results/results_tcomp.sc.RData")
## ## process the main results
## tcomp$params_on_bound_om <- NULL
## tcomp$log_max_grad <- log(tcomp$max_grad)
## tcomp$converged <- ifelse(tcomp$max_grad<.1 & tcomp$params_on_bound_em==0, "yes", "no")
## tcomp <- calculate_re(tcomp, add=TRUE)
## tcomp$runtime <- tcomp$RunTime
## tcomp <- merge(tcomp, tcomp.cases.df, by=c("species", "I"))
## tcomp <- merge(tcomp, data.cases.df, by=c("D"))
## ## Drop fixed params (columns of zeroes)
## tcomp$RecrDist_GP_1_re <- NULL
## tcomp <- tcomp[,-which(apply(tcomp, 2, function(x) all(x==0)))]
## tcomp <- ddply(tcomp, "scenario", mutate, pct.converged=mean(converged=="yes"))
## tcomp.unfiltered <- tcomp
## tcomp <- droplevels(subset(tcomp, converged=="yes"))
## tcomp.counts <- ddply(tcomp, .(tvalue, B, species, data), summarize,
## replicates=length(scenario),
## pct.converged=100*unique(pct.converged),
## median.iterations=median(Niterations),
## median.runtime=median(runtime))
## re.names <- names(tcomp)[grep("_re", names(tcomp))]
## tcomp.long <-
## melt(tcomp, measure.vars=re.names, id.vars=
## c("species","replicate", "data", "B", "pct.converged",
## "tvalue", "log_max_grad", "params_on_bound_em",
## "runtime"))
## growth.names <- re.names[grep("GP_", re.names)]
## tcomp.long.growth <- droplevels(subset(tcomp.long, variable %in% growth.names))
## tcomp.long.growth$variable <- gsub("_Fem_GP_1_re|_re", "", tcomp.long.growth$variable)
## selex.names <- re.names[grep("Sel_", re.names)]
## tcomp.long.selex <- droplevels(subset(tcomp.long, variable %in% selex.names))
## tcomp.long.selex$variable <- gsub("ery|ey|Size|_re", "", tcomp.long.selex$variable)
## tcomp.long.selex$variable <- gsub("_", ".", tcomp.long.selex$variable)
## management.names <- c("SSB_MSY_re", "depletion_re", "SSB_Unfished_re")
## tcomp.long.management <- droplevels(subset(tcomp.long, variable %in% management.names))
## tcomp.long.management$variable <- gsub("_re", "", tcomp.long.management$variable)
## figure.names <- c(growth.names, "SSB_MSY_re", "depletion_re")
## tcomp.long.figure <- droplevels(subset(tcomp.long, variable %in% figure.names))
## tcomp.long.figure$variable <- gsub("_Fem_GP_1_re|_re", "", tcomp.long.figure$variable)
## ## This is a crazy way to get which params get stuck for which scenarios
## library("magrittr")
## x.low <- paste(tcomp.unfiltered$params_stuck_low_em, collapse = ";") %>%
## strsplit(";")
## x.low <- unique(x.low[[1]])
## x.low <- x.low[-which(x.low == "NA")]
## stuck.low <- matrix(ncol = length(x.low), nrow = nrow(tcomp.unfiltered)) %>%
## as.data.frame %>%
## setNames(x.low)
## for (i in seq_along(x.low)) {
## stuck.low[,i] <- grepl(x.low[i], tcomp.unfiltered$params_stuck_low_em)}
## tcomp.stuck.low <- cbind(tcomp.unfiltered[, c("species", "D", "I")], stuck.low)
## tcomp.stuck.low <- melt(tcomp.stuck.low, id.vars=c("species", "D","I"))
## tcomp.stuck.low <- ddply(tcomp.stuck.low, .(species, D, I, variable), summarize, pct.stuck=mean(value))
## tcomp.stuck.low$which.bound <- "low"
## x.high <- paste(tcomp.unfiltered$params_stuck_high_em, collapse = ";") %>%
## strsplit(";")
## x.high <- unique(x.high[[1]])
## x.high <- x.high[-which(x.high == "NA")]
## stuck.high <- matrix(ncol = length(x.high), nrow = nrow(tcomp.unfiltered)) %>%
## as.data.frame %>%
## setNames(x.high)
## for (i in seq_along(x.high)) {
## stuck.high[,i] <- grepl(x.high[i], tcomp.unfiltered$params_stuck_high_em)}
## tcomp.stuck.high <- cbind(tcomp.unfiltered[, c("species", "D", "I")], stuck.high)
## tcomp.stuck.high <- melt(tcomp.stuck.high, id.vars=c("species", "D","I"))
## tcomp.stuck.high <- ddply(tcomp.stuck.high, .(species, D, I, variable), summarize, pct.stuck=mean(value))
## tcomp.stuck.high$which.bound <- "high"
## tcomp.stuck <- subset(rbind(tcomp.stuck.low, tcomp.stuck.high), pct.stuck>0)
## tcomp.stuck$variable <- gsub("Size|_Fem_GP_1", "", tcomp.stuck$variable)
## robust <- readRDS("results/results_robust.sc.RData")
## ## process the main results
## robust$params_on_bound_om <- NULL
## robust$log_max_grad <- log(robust$max_grad)
## robust$converged <- ifelse(robust$max_grad<.1 & robust$params_on_bound_em==0, "yes", "no")
## robust <- calculate_re(robust, add=TRUE)
## robust$runtime <- robust$RunTime
## robust <- merge(robust, robust.cases.df, by=c("species", "I"))
## robust <- merge(robust, data.cases.df, by=c("D"))
## ## Drop fixed params (columns of zeroes)
## robust$RecrDist_GP_1_re <- NULL
## robust <- robust[,-which(apply(robust, 2, function(x) all(x==0)))]
## robust <- ddply(robust, "scenario", mutate, pct.converged=mean(converged=="yes"))
## robust.unfiltered <- robust
## robust <- droplevels(subset(robust, converged=="yes"))
## robust.counts <- ddply(robust, .(rvalue, B, species, data), summarize,
## replicates=length(scenario),
## pct.converged=100*unique(pct.converged),
## median.iterations=median(Niterations),
## median.runtime=median(runtime))
## re.names <- names(robust)[grep("_re", names(robust))]
## robust.long <-
## melt(robust, measure.vars=re.names, id.vars=
## c("species","replicate", "data", "B", "pct.converged",
## "rvalue", "log_max_grad", "params_on_bound_em",
## "runtime"))
## growth.names <- re.names[grep("GP_", re.names)]
## robust.long.growth <- droplevels(subset(robust.long, variable %in% growth.names))
## robust.long.growth$variable <- gsub("_Fem_GP_1_re|_re", "", robust.long.growth$variable)
## selex.names <- re.names[grep("Sel_", re.names)]
## robust.long.selex <- droplevels(subset(robust.long, variable %in% selex.names))
## robust.long.selex$variable <- gsub("ery|ey|Size|_re", "", robust.long.selex$variable)
## robust.long.selex$variable <- gsub("_", ".", robust.long.selex$variable)
## management.names <- c("SSB_MSY_re", "depletion_re", "SSB_Unfished_re")
## robust.long.management <- droplevels(subset(robust.long, variable %in% management.names))
## robust.long.management$variable <- gsub("_re", "", robust.long.management$variable)
## figure.names <- c(growth.names, "SSB_MSY_re", "depletion_re")
## robust.long.figure <- droplevels(subset(robust.long, variable %in% figure.names))
## robust.long.figure$variable <- gsub("_Fem_GP_1_re|_re", "", robust.long.figure$variable)
## ## This is a crazy way to get which params get stuck for which scenarios
## library("magrittr")
## x.low <- paste(robust.unfiltered$params_stuck_low_em, collapse = ";") %>%
## strsplit(";")
## x.low <- unique(x.low[[1]])
## x.low <- x.low[-which(x.low == "NA")]
## stuck.low <- matrix(ncol = length(x.low), nrow = nrow(robust.unfiltered)) %>%
## as.data.frame %>%
## setNames(x.low)
## for (i in seq_along(x.low)) {
## stuck.low[,i] <- grepl(x.low[i], robust.unfiltered$params_stuck_low_em)}
## robust.stuck.low <- cbind(robust.unfiltered[, c("species", "D", "I")], stuck.low)
## robust.stuck.low <- melt(robust.stuck.low, id.vars=c("species", "D","I"))
## robust.stuck.low <- ddply(robust.stuck.low, .(species, D, I, variable), summarize, pct.stuck=mean(value))
## robust.stuck.low$which.bound <- "low"
## x.high <- paste(robust.unfiltered$params_stuck_high_em, collapse = ";") %>%
## strsplit(";")
## x.high <- unique(x.high[[1]])
## x.high <- x.high[-which(x.high == "NA")]
## stuck.high <- matrix(ncol = length(x.high), nrow = nrow(robust.unfiltered)) %>%
## as.data.frame %>%
## setNames(x.high)
## for (i in seq_along(x.high)) {
## stuck.high[,i] <- grepl(x.high[i], robust.unfiltered$params_stuck_high_em)}
## robust.stuck.high <- cbind(robust.unfiltered[, c("species", "D", "I")], stuck.high)
## robust.stuck.high <- melt(robust.stuck.high, id.vars=c("species", "D","I"))
## robust.stuck.high <- ddply(robust.stuck.high, .(species, D, I, variable), summarize, pct.stuck=mean(value))
## robust.stuck.high$which.bound <- "high"
## robust.stuck <- subset(rbind(robust.stuck.low, robust.stuck.high), pct.stuck>0)
## robust.stuck$variable <- gsub("Size|_Fem_GP_1", "", robust.stuck$variable)
## ## End tcomp and robust
## ### ------------------------------------------------------------
## ## Verify that these particular replicates are the same since they should
## ## be
## print("these should be the same:")
## print(rbind(binning[binning$scenario=='B0-D2-F1-I0-cod' & binning$replicate == 1, c("depletion_em", "depletion_om")],
## tcomp[tcomp$scenario=='B0-D2-F1-I11-cod' & tcomp$replicate == 1, c("depletion_em", "depletion_om")],
## robust[robust$scenario=='B0-D2-F1-I21-cod' & robust$replicate == 1, c("depletion_em", "depletion_om")]))