#' @title
#' Plot index of abundance
#'
#' @description
#' \code{plot_biomass_index} plots an index proportion to population abundance for stream networks
#'
#' @param TmbData Formatted data inputs, from `VAST::Data_Fn(...)`
#' @param savedir Directory for saving plot and table
#' @param PlotName Name for plot
#' @param interval_width width for confidence intervals
#' @param strata_names names for spatial strata
#' @param category_names names for categories (if using package `VAST`)
#' @param use_biascorr Boolean, whether to use bias-corrected estimates if available
#' @param plot_legend Add legend for labelling colors
#' @param total_area_km2 Total area for calculating a design-based estimator using one design-stratum (only recommended for model exploration)
#' @param plot_log Boolean, whether to plot y-axis in log-scale
#' @param width plot width in inches
#' @param height plot height in inches
#' @param treat_missing_as_zero Boolean whether to treat years and species with no (or only NA) data as instances where the index should be zero
#' @param ... Other inputs to `par()`
#' @inheritParams plot_maps
#' @importFrom FishStatsUtils plot_lines
#'
#' @return Return Tagged list of output
#' \describe{
#' \item{Table}{table of index estimates by stratum and year, e.g., for including in an assessment model}
#' }
#'
#' @export
plot_biomass_index <-
function( TmbData, Sdreport, Year_Set=NULL, Years2Include=NULL, savedir=paste0(getwd(),"/"), PlotName="Index", interval_width=1,
strata_names=NULL, category_names=NULL, use_biascorr=TRUE, plot_legend=TRUE, total_area_km2=NULL, plot_log=FALSE, width=4, height=4,
treat_missing_as_zero=FALSE, create_covariance_table=FALSE, ... ){
# Informative errors
if(is.null(Sdreport)) stop("Sdreport is NULL; please provide Sdreport")
if(!is.null(category_names) && length(category_names)!=TmbData$n_c ) stop("`category_names` must have same length as `TmbData$n_c`")
if(!is.null(Year_Set) && length(Year_Set)!=TmbData$n_t ) stop("`Year_Set` must have same length as `TmbData$n_t`")
if(!is.null(strata_names) && length(strata_names)!=TmbData$n_l ) stop("`strata_names` must have same length as `TmbData$n_l`")
# Which parameters
if( "ln_Index_tl" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
# SpatialDeltaGLMM
ParName = "Index_tl"
TmbData[['n_c']] = 1
}
if( "ln_Index_ctl" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
# VAST Version < 2.0.0
ParName = "Index_ctl"
}
if( "ln_Index_cyl" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
# VAST Version >= 2.0.0
ParName = "Index_cyl"
TmbData[["n_t"]] = nrow(TmbData[["t_yz"]])
}
if( "Index_tp" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
# SpatialVAM
ParName = "Index_tp"
TmbData[["n_l"]] = 1
TmbData[["n_c"]] = TmbData[["n_p"]]
}
# Add t_iz if missing (e.g., from earlier version of VAST, or SpatialDeltaGLMM)
if( !("t_iz" %in% names(TmbData)) ){
TmbData$t_iz = matrix( TmbData$t_i, ncol=1 )
}
# Add in t_yz if missing (e.g., from earlier version of VAST, or SpatialDeltaGLMM)
if( !("t_yz" %in% names(TmbData)) ){
TmbData$t_yz = matrix(1:TmbData$n_t - 1, ncol=1)
}
# Fill in missing
if( is.null(Year_Set) ) Year_Set = 1:TmbData$n_t
if( is.null(Years2Include) ) Years2Include = 1:TmbData$n_t
if( is.null(strata_names) ) strata_names = 1:TmbData$n_l
if( is.null(category_names) ) category_names = 1:TmbData$n_c
# Logical check
if( "unbiased"%in%names(Sdreport) ){
if( all(is.na(Sdreport$unbiased$value)) ){
stop("You appear to be using bias-correction, but all values are NA. Please report problem to package author.")
}
}
# Objects
SD = TMB::summary.sdreport(Sdreport)
SD_estimate = as.list( Sdreport, what="Estimate", report=TRUE )
SD_stderr = as.list( Sdreport, what="Std. Error", report=TRUE )
# Extract index (using bias-correctino if available and requested)
if( ParName %in% c("Index_tl","Index_ctl","Index_cyl")){
Index_ctl = log_Index_ctl = array( NA, dim=c(unlist(TmbData[c('n_c','n_t','n_l')]),2), dimnames=list(category_names,Year_Set,strata_names,c('Estimate','Std. Error')) )
# Index
if( use_biascorr==TRUE && "unbiased"%in%names(Sdreport) ){
Index_ctl[] = SD[which(rownames(SD)==ParName),c('Est. (bias.correct)','Std. Error')]
}
if( !any(is.na(Index_ctl)) ){
message("Using bias-corrected estimates for abundance index (natural-scale)...")
}else{
message("Not using bias-corrected estimates for abundance index (natural-scale)...")
Index_ctl[] = SD[which(rownames(SD)==ParName),c('Estimate','Std. Error')]
}
# Log-Index
if( use_biascorr==TRUE && "unbiased"%in%names(Sdreport) ){
log_Index_ctl[] = SD[which(rownames(SD)==paste0("ln_",ParName)),c('Est. (bias.correct)','Std. Error')]
}
if( !any(is.na(log_Index_ctl)) ){
message("Using bias-corrected estimates for abundance index (log-scale)...")
}else{
message("Not using bias-corrected estimates for abundance index (log-scale)...")
log_Index_ctl[] = SD[which(rownames(SD)==paste0("ln_",ParName)),c('Estimate','Std. Error')]
}
}
if( ParName %in% c("Index_tp")){
if( use_biascorr==TRUE && "unbiased"%in%names(Sdreport) ){
Index_ctl = aperm( array( c(Sdreport$unbiased$value[which(names(Sdreport$value)==ParName)],TMB::summary.sdreport(Sdreport)[which(rownames(TMB::summary.sdreport(Sdreport))==ParName),'Std. Error']), dim=c(unlist(TmbData[c('n_t','n_c','n_l')]),2), dimnames=list(NULL,NULL,NULL,c('Estimate','Std. Error')) ), perm=c(2,1,3))
if( "ln_Index_tp" %in% rownames(TMB::summary.sdreport(Sdreport))){
log_Index_ctl = aperm( array( c(Sdreport$unbiased$value[which(names(Sdreport$value)==paste0("ln_",ParName))],TMB::summary.sdreport(Sdreport)[which(rownames(TMB::summary.sdreport(Sdreport))==paste0("ln_",ParName)),'Std. Error']), dim=c(unlist(TmbData[c('n_t','n_c','n_l')]),2), dimnames=list(NULL,NULL,NULL,c('Estimate','Std. Error')) ), perm=c(2,1,3))
}else{
log_Index_ctl = log( Index_ctl )
log_Index_ctl[,,,'Std. Error'] = log_Index_ctl[,,,'Std. Error'] / log_Index_ctl[,,,'Estimate']
warning( "Using kludge for log-standard errors of index, to be replaced in later versions of 'MIST'" )
}
}else{
Index_ctl = aperm( array( TMB::summary.sdreport(Sdreport)[which(rownames(TMB::summary.sdreport(Sdreport))==ParName),], dim=c(unlist(TmbData[c('n_t','n_c','n_l')]),2), dimnames=list(NULL,NULL,NULL,c('Estimate','Std. Error')) ), perm=c(2,1,3,4))
if( "ln_Index_tp" %in% rownames(TMB::summary.sdreport(Sdreport))){
log_Index_ctl = aperm( array( TMB::summary.sdreport(Sdreport)[which(rownames(TMB::summary.sdreport(Sdreport))==paste0("ln_",ParName)),], dim=c(unlist(TmbData[c('n_t','n_c','n_l')]),2), dimnames=list(NULL,NULL,NULL,c('Estimate','Std. Error')) ), perm=c(2,1,3,4))
}else{
log_Index_ctl = log( Index_ctl )
log_Index_ctl[,,,'Std. Error'] = log_Index_ctl[,,,'Std. Error'] / log_Index_ctl[,,,'Estimate']
warning( "Using kludge for log-standard errors of index, to be replaced in later versions of 'MIST'" )
}
}
}
# Extract biomass ratio Bratio_cty if available (only available if >= V5.3.0 and using spatial Gompertz model features)
if( "Bratio_cyl" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
Bratio_ctl = array( NA, dim=c(unlist(TmbData[c('n_c','n_t','n_l')]),2), dimnames=list(category_names,Year_Set,strata_names,c('Estimate','Std. Error')) )
if( use_biascorr==TRUE && "unbiased"%in%names(Sdreport) ){
Bratio_ctl[] = SD[which(rownames(SD)=="Bratio_cyl"),c('Est. (bias.correct)','Std. Error')]
}
if( !any(is.na(Bratio_ctl)) ){
message("Using bias-corrected estimates for biomass ratio (natural-scale)...")
}else{
message("Not using bias-corrected estimates for biomass ratio (natural-scale)...")
Bratio_ctl[] = SD[which(rownames(SD)=="Bratio_cyl"),c('Estimate','Std. Error')]
}
}else{
Bratio_ctl = NULL
}
if( "ln_Bratio_cyl" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
log_Bratio_ctl = array( NA, dim=c(unlist(TmbData[c('n_c','n_t','n_l')]),2), dimnames=list(category_names,Year_Set,strata_names,c('Estimate','Std. Error')) )
if( use_biascorr==TRUE && "unbiased"%in%names(Sdreport) ){
log_Bratio_ctl[] = SD[which(rownames(SD)=="ln_Bratio_cyl"),c('Est. (bias.correct)','Std. Error')]
}
if( !any(is.na(log_Bratio_ctl)) ){
message("Using bias-corrected estimates for biomass ratio (log-scale)...")
}else{
message("Not using bias-corrected estimates for biomass ratio (log-scale)...")
log_Bratio_ctl[] = SD[which(rownames(SD)=="ln_Bratio_cyl"),c('Estimate','Std. Error')]
}
}else{
log_Bratio_ctl = NULL
}
# Extract Fratio
if( "Fratio_ct" %in% rownames(TMB::summary.sdreport(Sdreport)) ){
Fratio_ct = array( NA, dim=c(unlist(TmbData[c('n_c','n_t')]),2), dimnames=list(category_names,Year_Set,c('Estimate','Std. Error')) )
Fratio_ct[] = SD[which(rownames(SD)=="Fratio_ct"),c('Estimate','Std. Error')]
#Fratio_ct = abind::abind( SD_estimate$Fratio, SD_stderr$Fratio, along=3 )
#dimnames(Fratio_ct) = list(category_names,Year_Set,c('Estimate','Std. Error'))
}else{
Fratio_ct = NULL
}
# Calculate design-based
if( !is.null(total_area_km2) & TmbData$n_c==1 ){
message( "Calculating naive design-based index -- do not use this, its intended only for comparison purposes" )
Calc_design = TRUE
Design_t = tapply( TmbData$b_i/TmbData$a_i, INDEX=TmbData$t_i, FUN=mean ) * total_area_km2 / 1000 # Convert to tonnes
Design_t = cbind( "Estimate"=Design_t, "Std. Error"=sqrt(tapply(TmbData$b_i/TmbData$a_i,INDEX=TmbData$t_i,FUN=var)/tapply(TmbData$b_i/TmbData$a_i,INDEX=TmbData$t_i,FUN=length))*total_area_km2/1000)
Design_t = cbind( Design_t, "CV"=Design_t[,'Std. Error'] / Design_t[,'Estimate'] )
}else{
Calc_design = FALSE
}
# Fix at zeros any years-category combinations with no data
if( treat_missing_as_zero==TRUE ){
# Determine year-category pairs with no data
Num_ct = tapply( TmbData$b_i, INDEX=list(factor(TmbData$c_i,levels=1:TmbData$n_c-1),factor(TmbData$t_i[,1],levels=1:TmbData$n_t-1)), FUN=function(vec){sum(!is.na(vec))} )
Num_ct = ifelse( is.na(Num_ct), 0, Num_ct )
# Replace values with 0 (estimate) and NA (standard error)
Index_ctl[,,,'Estimate'] = ifelse(Num_ct%o%rep(1,TmbData$n_l)==0, 0, Index_ctl[,,,'Estimate'])
Index_ctl[,,,'Std. Error'] = ifelse(Num_ct%o%rep(1,TmbData$n_l)==0, NA, Index_ctl[,,,'Std. Error'])
log_Index_ctl[,,,'Estimate'] = ifelse(Num_ct%o%rep(1,TmbData$n_l)==0, -Inf, log_Index_ctl[,,,'Estimate'])
log_Index_ctl[,,,'Std. Error'] = ifelse(Num_ct%o%rep(1,TmbData$n_l)==0, NA, log_Index_ctl[,,,'Std. Error'])
}
# Plot biomass and Bratio
Plot_suffix = "Biomass"
if( !is.null(Bratio_ctl) ) Plot_suffix = c( Plot_suffix, "Bratio" )
for( plotI in 1:length(Plot_suffix) ){
Par = list( mar=c(2,2,1,0), mgp=c(2,0.5,0), tck=-0.02, yaxs="i", oma=c(1,2,0,0), mfrow=c(ceiling(sqrt(TmbData$n_c)),ceiling(TmbData$n_c/ceiling(sqrt(TmbData$n_c)))), ... )
if(!is.null(savedir)) png( file=paste0(savedir,"/",PlotName,"-",Plot_suffix[plotI],".png"), width=width, height=height, res=200, units="in")
par( Par )
for( cI in 1:TmbData$n_c ){
if( Plot_suffix[plotI]=="Biomass" ){ Array_ctl = Index_ctl; log_Array_ctl = log_Index_ctl }
if( Plot_suffix[plotI]=="Bratio" ){ Array_ctl = Bratio_ctl; log_Array_ctl = log_Bratio_ctl }
# Calculate y-axis limits
Ylim = c(0, max(Array_ctl[cI,Years2Include,,'Estimate']%o%c(1,1) * exp(log_Array_ctl[cI,Years2Include,,'Std. Error']%o%c(-interval_width,interval_width)),na.rm=TRUE) )
if( plot_log==TRUE ) Ylim[1] = min(Array_ctl[cI,Years2Include,,'Estimate']%o%c(1,1) * exp(log_Array_ctl[cI,Years2Include,,'Std. Error']%o%c(-interval_width,interval_width)),na.rm=TRUE)
# Plot stuff
plot(1, type="n", xlim=range(Year_Set), ylim=ifelse(plot_legend==TRUE,1.25,1.05)*Ylim, xlab="", ylab="", main=ifelse(TmbData$n_c>1,category_names[cI],""), log=ifelse(plot_log==TRUE,"y","") )
for(l in 1:TmbData$n_l){
plot_lines( y=Array_ctl[cI,Years2Include,l,'Estimate'], x=Year_Set[Years2Include]+seq(-0.1,0.1,length=TmbData$n_l)[l], ybounds=(Array_ctl[cI,Years2Include,l,'Estimate']%o%c(1,1))*exp(log_Array_ctl[cI,Years2Include,l,'Std. Error']%o%c(-interval_width,interval_width)), type="b", col=rainbow(TmbData[['n_l']])[l], col_bounds=rainbow(TmbData[['n_l']])[l], ylim=Ylim)
}
if(plot_legend==TRUE & cI==TmbData$n_c) legend( "top", bty="n", fill=c(na.omit(ifelse(Calc_design==TRUE,"black",NA)),rainbow(TmbData[['n_l']])), legend=c(na.omit(ifelse(Calc_design==TRUE,"Design-based",NA)),as.character(strata_names)), ncol=2 )
}
mtext( side=1:2, text=c("Year",switch(Plot_suffix[plotI], "Biomass"="Abundance", "Bratio"="Biomass ratio")), outer=TRUE, line=c(0,0) )
if(!is.null(savedir)) dev.off()
}
# Plot
if( !is.null(Fratio_ct) ){
Par = list( mar=c(2,2,1,0), mgp=c(2,0.5,0), tck=-0.02, yaxs="i", oma=c(1,2,0,0), mfrow=c(ceiling(sqrt(TmbData$n_c)),ceiling(TmbData$n_c/ceiling(sqrt(TmbData$n_c)))), ... )
if(!is.null(savedir)) png( file=paste0(savedir,"/",PlotName,"-Fratio.png"), width=width, height=height, res=200, units="in")
par( Par )
Array_ct = Fratio_ct
Array_ct = ifelse( Array_ct==0, NA, Array_ct )
for( cI in 1:TmbData$n_c ){
# Calculate y-axis limits
Ylim = c(0, max(Array_ct[cI,Years2Include,'Estimate']%o%c(1,1) + Array_ct[cI,Years2Include,'Std. Error']%o%c(-interval_width,interval_width),na.rm=TRUE) )
if( plot_log==TRUE ) Ylim[1] = min(Array_ct[cI,Years2Include,'Estimate']%o%c(1,1) + Array_ct[cI,Years2Include,'Std. Error']%o%c(-interval_width,interval_width),na.rm=TRUE)
# Plot stuff
plot(1, type="n", xlim=range(Year_Set), ylim=ifelse(plot_legend==TRUE,1.25,1.05)*Ylim, xlab="", ylab="", main=ifelse(TmbData$n_c>1,category_names[cI],""), log=ifelse(plot_log==TRUE,"y","") )
plot_lines( y=Array_ct[cI,Years2Include,'Estimate'], x=Year_Set[Years2Include], ybounds=(Array_ct[cI,Years2Include,'Estimate']%o%c(1,1))+Array_ct[cI,Years2Include,'Std. Error']%o%c(-interval_width,interval_width), type="b", col="black", col_bounds="black", ylim=Ylim)
}
mtext( side=1:2, text=c("Year","Fishing ratio"), outer=TRUE, line=c(0,0) )
if(!is.null(savedir)) dev.off()
}
# Plot stock status
if( !is.null(Bratio_ctl) & !is.null(Fratio_ct) ){
Par = list( mar=c(2,2,1,0), mgp=c(2,0.5,0), tck=-0.02, yaxs="i", oma=c(1,2,0,0), mfrow=c(ceiling(sqrt(TmbData$n_c)),ceiling(TmbData$n_c/ceiling(sqrt(TmbData$n_c)))), ... )
Col = colorRampPalette(colors=c("blue","purple","red"))
if(!is.null(savedir)) png( file=paste0(savedir,"/",PlotName,"-Status.png"), width=width, height=height, res=200, units="in")
par( Par )
Array1_ct = Bratio_ctl[,,1,]
Array1_ct = ifelse( Array1_ct==0, NA, Array1_ct )
Array2_ct = Fratio_ct
Array2_ct = ifelse( Array2_ct==0, NA, Array2_ct )
for( cI in 1:TmbData$n_c ){
# Calculate y-axis limits
Xlim = c(0, max(1, Array1_ct[cI,Years2Include,'Estimate']%o%c(1,1) + Array1_ct[cI,Years2Include,'Std. Error']%o%c(-interval_width,interval_width),na.rm=TRUE) )
Ylim = c(0, max(2, Array2_ct[cI,Years2Include,'Estimate']%o%c(1,1) + Array2_ct[cI,Years2Include,'Std. Error']%o%c(-interval_width,interval_width),na.rm=TRUE) )
# Plot stuff
plot(1, type="n", xlim=Xlim, ylim=Ylim, xlab="", ylab="", main=ifelse(TmbData$n_c>1,category_names[cI],"") )
points( x=Array1_ct[cI,Years2Include,'Estimate'], y=Array2_ct[cI,Years2Include,'Estimate'], col=Col(length(Year_Set))[Years2Include] )
for( tI in Years2Include ){
lines( x=rep(Array1_ct[cI,tI,'Estimate'],2), y=Array2_ct[cI,tI,'Estimate']+Array2_ct[cI,tI,'Std. Error']*c(-interval_width,interval_width), col=Col(length(Year_Set))[tI] )
lines( x=Array1_ct[cI,tI,'Estimate']+Array1_ct[cI,tI,'Std. Error']*c(-interval_width,interval_width), y=rep(Array2_ct[cI,tI,'Estimate'],2), col=Col(length(Year_Set))[tI] )
}
abline( v=0.4, lty="dotted" )
abline( h=1, lty="dotted" )
}
legend( "topright", bty="n", fill=c(Col(length(Year_Set))[Years2Include[1]],Col(length(Year_Set))[rev(Years2Include)[1]]), legend=c(Year_Set[Years2Include[1]],Year_Set[rev(Years2Include)[1]]) )
mtext( side=1:2, text=c("Biomass relative to unfished","Fishing relative to F_40%"), outer=TRUE, line=c(0,0) )
if(!is.null(savedir)) dev.off()
}
# Write to file
Table = NULL
for( cI in 1:TmbData$n_c ){
Tmp = data.frame( "Year"=Year_Set, "Unit"=1, "Fleet"=rep(strata_names,each=TmbData$n_t), "Estimate_metric_tons"=as.vector(Index_ctl[cI,,,'Estimate']), "SD_log"=as.vector(log_Index_ctl[cI,,,'Std. Error']), "SD_mt"=as.vector(Index_ctl[cI,,,'Std. Error']) )
if( TmbData$n_c>1 ) Tmp = cbind( "Category"=category_names[cI], Tmp)
Table = rbind( Table, Tmp )
}
if(!is.null(total_area_km2)) Table = cbind(Table, "Naive_design-based_index"=Design_t)
if(!is.null(savedir)) write.csv( Table, file=paste0(savedir,"/Table_for_SS3.csv"), row.names=FALSE)
# Return stuff
Return = list( "Table"=Table, "log_Index_ctl"=log_Index_ctl, "Index_ctl"=Index_ctl, "Ylim"=Ylim )
# Extract and save covariance
if( "cov"%in%names(Sdreport) & create_covariance_table==TRUE ){
DF = expand.grid( "Category"=1:TmbData$n_c, "Year"=1:TmbData$n_t, "Stratum"=1:TmbData$n_l )
Which = which( names(Sdreport$value)==ParName )
Cov = Sdreport$cov[Which,Which]
Corr = cov2cor(Cov) - diag(nrow(Cov))
rowcolDF = cbind( "RowNum"=row(Corr)[lower.tri(Corr,diag=TRUE)], "ColNum"=col(Corr)[lower.tri(Corr,diag=TRUE)] )
Table = cbind( DF[rowcolDF[,'ColNum'],], DF[rowcolDF[,'RowNum'],] )
colnames(Table) = paste0(colnames(Table), rep(c(1,2),each=3))
Table = cbind( Table, "Correlation"=cov2cor(Cov)[lower.tri(Corr,diag=TRUE)], "Covariance"=Cov[lower.tri(Corr,diag=TRUE)] )
Table = cbind( Table, "Index1"=Index_ctl[as.matrix(cbind(DF[rowcolDF[,'ColNum'],],1))], "Index2"=Index_ctl[as.matrix(cbind(DF[rowcolDF[,'RowNum'],],1))] )
WhichZero = which( (Table[,'Index1']*Table[,'Index2']) == 0 )
Table[WhichZero,c('Correlation','Covariance')] = 0
Return = c( Return, "Table_of_estimted_covariance"=Table )
}
if( !is.null(Bratio_ctl)) Return = c( Return, list("Bratio_ctl"=Bratio_ctl) )
if( !is.null(log_Bratio_ctl)) Return = c( Return, list("log_Bratio_ctl"=log_Bratio_ctl) )
if( !is.null(Fratio_ct)) Return = c( Return, list("Fratio_ct"=Fratio_ct) )
return( invisible(Return) )
}
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