Copepod Model Results: Covariate comparisons

We tried covariate models

Some worked, some didnt

BLUF: not using covariates for the small copepods, will retain DOY for the large copepods

# from each output folder in pyindex, 
outdir <- here::here("pyindex")
moddirs <- list.dirs(outdir) 
moddirs <- moddirs[-1]
# keep folder name
modnames <- list.dirs(outdir, full.names = FALSE)


# function to apply extracting info
getmodinfo <- function(d.name){
  # read settings
  modpath <- stringr::str_split(d.name, "/", simplify = TRUE)
  modname <- modpath[length(modpath)]
  
  settings <- read.table(file.path(d.name, "settings.txt"), comment.char = "",
    fill = TRUE, header = FALSE)
  
  n_x <- as.numeric(as.character(settings[(which(settings[,1]=="$n_x")+1),2]))
  grid_size_km <- as.numeric(as.character(settings[(which(settings[,1]=="$grid_size_km")+1),2]))
  max_cells <- as.numeric(as.character(settings[(which(settings[,1]=="$max_cells")+1),2]))
  use_anisotropy <- as.character(settings[(which(settings[,1]=="$use_anisotropy")+1),2])
  fine_scale <- as.character(settings[(which(settings[,1]=="$fine_scale")+1),2])
  bias.correct <- as.character(settings[(which(settings[,1]=="$bias.correct")+1),2])
  
  #FieldConfig
  if(settings[(which(settings[,1]=="$FieldConfig")+1),1]=="Component_1"){
    omega1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+2),2])
    omega2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),1])
    epsilon1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),2])
    epsilon2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+5),1])
    beta1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+6),2])
    beta2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+7),1])
  }
  
  if(settings[(which(settings[,1]=="$FieldConfig")+1),1]=="Omega1"){
    omega1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),1])
    omega2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),1])
    epsilon1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),2])
    epsilon2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),2])
    beta1 <- "IID"
    beta2 <- "IID"
  }
  
  
  #RhoConfig
  rho_beta1 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+3),1]))
  rho_beta2 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+3),2]))
  rho_epsilon1 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+4),1]))
  rho_epsilon2 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+4),2]))
  
  # read parameter estimates, object is called parameter_Estimates
  if(file.exists(file.path(d.name, "parameter_estimates.RData"))) {
    load(file.path(d.name, "parameter_estimates.RData"))
    
    AIC <- parameter_estimates$AIC[1]  
    converged <- parameter_estimates$Convergence_check[1]
    fixedcoeff <- unname(parameter_estimates$number_of_coefficients[2])
    randomcoeff <- unname(parameter_estimates$number_of_coefficients[3])
    
  }else if(file.exists(file.path(d.name, "parameter_estimates.txt"))){
    
    reptext <- readLines(file.path(d.name, "parameter_estimates.txt"))
    
    AIC <- as.double(reptext[grep(reptext, pattern = "AIC")+1])
    converged <- reptext[grep(reptext, pattern = "Convergence_check")+1]
    fixedcoeff <- as.integer(stringr::str_split(reptext[grep(reptext, pattern = "number_of_coefficients")+2], 
                                     boundary("word"))[[1]][2])
    randomcoeff <- as.integer(stringr::str_split(reptext[grep(reptext, pattern = "number_of_coefficients")+2], 
                                     boundary("word"))[[1]][3])
    
  }else{
    
    AIC <- NA_real_
    converged <- NA_character_
    fixedcoeff <- NA_integer_
    randomcoeff <- NA_integer_
  }
  
  
  #index <- read.csv(file.path(d.name, "Index.csv"))
  
  
  # return model attributes as a dataframe
  out <- data.frame(modname = modname,
                    n_x = n_x,
                    grid_size_km = grid_size_km,
                    max_cells = max_cells,
                    use_anisotropy = use_anisotropy,
                    fine_scale =  fine_scale,
                    bias.correct = bias.correct,
                    omega1 = omega1,
                    omega2 = omega2,
                    epsilon1 = epsilon1,
                    epsilon2 = epsilon2,
                    beta1 = beta1,
                    beta2 = beta2,
                    rho_epsilon1 = rho_epsilon1,
                    rho_epsilon2 = rho_epsilon2,
                    rho_beta1 = rho_beta1,
                    rho_beta2 = rho_beta2,
                    AIC = AIC,
                    converged = converged,
                    fixedcoeff = fixedcoeff,
                    randomcoeff = randomcoeff#,
                    #index = index
  )
    
    return(out)

}


modcompare <- purrr::map_dfr(moddirs, getmodinfo)

modselect <- modcompare |>
  dplyr::mutate(season = dplyr::case_when(stringr::str_detect(modname, "_fall_") ~ "Fall",
                            stringr::str_detect(modname, "spring") ~ "Spring",
                            stringr::str_detect(modname, "_all_") ~ "Annual",
                            TRUE ~ as.character(NA))) |>
  dplyr::mutate(converged2 = dplyr::case_when(stringr::str_detect(converged, "no evidence") ~ "likely",
                                stringr::str_detect(converged, "is likely not") ~ "unlikely",
                                TRUE ~ as.character(NA))) |>
  dplyr::mutate(copegroup = stringr::str_extract(modname, "[^_]+")) |>
  #dplyr::mutate(modname = str_extract(modname, '(?<=allagg_).*')) |>
  dplyr::group_by(copegroup, season) |>
  dplyr::mutate(deltaAIC = AIC-min(AIC)) |>
  dplyr::select(copegroup, modname, season, deltaAIC, fixedcoeff,
         randomcoeff, use_anisotropy, 
         omega1, omega2, epsilon1, epsilon2, 
         beta1, beta2, AIC, converged2) |>
  dplyr::arrange(copegroup, season, AIC)

# DT::datatable(modselect, rownames = FALSE, 
#               options= list(pageLength = 25, scrollX = TRUE),
#               caption = "Comparison of delta AIC values using Restricted Maxiumum Likelihood (REML) for alternative fixed and random effects model structures. See text for model descriptions.")

# flextable::flextable(modselect) %>%
#                        #dplyr::select(-c(use_anisotropy, 
#          #omega1, omega2, epsilon1, epsilon2, 
#          #beta1, beta2))
#          #) %>%
#   flextable::set_header_labels(modname = "Model name",
#                                season = "Season",
#                                #deltaAIC = "dAIC",
#                                fixedcoeff = "N fixed",
#                                randomcoeff = "N random",
#                                converged2 = "Convergence") |>
#   #flextable::set_caption("Comparison of delta AIC (dAIC) values using Restricted Maxiumum Likelihood (REML) for alternative fixed and random effects model structures, with number of fixed (N fixed) and random (N random) coefficients. See text for model descriptions associated with each model name.") %>%
#   flextable::fontsize(size = 9, part = "all") %>%
#   flextable::colformat_double(digits = 2) |>
#   flextable::set_table_properties(layout = "autofit", width = 1)

stratlook <- data.frame(Stratum = c("Stratum_1",
                                    "Stratum_2",
                                    "Stratum_3",
                                    "Stratum_4",
                                    "Stratum_5",
                                    "Stratum_6",
                                    "Stratum_7"),
                        Region  = c("AllEPU",
                                    "her_sp",
                                    "her_fa",
                                    "MAB",
                                    "GB",
                                    "GOM",
                                    "SS"))

# function to apply extracting info
getmodindex <- function(d.name){
  # read settings
  modpath <- stringr::str_split(d.name, "/", simplify = TRUE)
  modname <- modpath[length(modpath)]
  
  if(file.exists(file.path(d.name,"Index.csv"))){
    index <- read.csv(file.path(d.name, "Index.csv"))
  }else{
    stopifnot()
  }
  # return model indices as a dataframe
  out <- data.frame(modname = modname,
                    index
  )
  
  return(out)
}

modcompareindex <- purrr::map_dfr(moddirs, purrr::possibly(getmodindex, otherwise = NULL))

splitoutput <- modcompareindex %>%
  dplyr::mutate(Season = modname |> map(str_split, pattern = "_") |> map_chr(c(1,2))) %>%
  dplyr::mutate(Data = modname |> map(str_split, pattern = "_") |> map_chr(c(1,1))) %>%
  dplyr::mutate(Estimate = ifelse(Estimate == 0, NA, Estimate)) |>
  dplyr::left_join(stratlook) #%>%
  #dplyr::filter(Region %in% c(GOM", "GB", "MAB","SS", "AllEPU")) use all regions

zoomax <- max(splitoutput$Estimate, na.rm=T)


zootsmean <- splitoutput %>%
  dplyr::group_by(modname, Region) %>%
  dplyr::mutate(fmean = mean(Estimate, na.rm=T)) 

plot_zooindices <- function(splitoutput, plotdata, plotregions, plottitle){
  
  filterEPUs <- plotregions #c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU")
  
  seasons <- splitoutput |> dplyr::filter(Data==plotdata) |> dplyr::select(Season) |> dplyr::distinct()
  
  ncols <- dim(seasons)[1]
  
  currentMonth <- lubridate::month(Sys.Date())
  currentYear <- lubridate::year(Sys.Date())
  if (currentMonth > 4) {
    endShade <- currentYear
  } else {
    endShade <- currentYear - 1
  }
  shadedRegion <- c(endShade-9,endShade)
  
  shade.alpha <- 0.3
  shade.fill <- "lightgrey"
  lwd <- 1
  pcex <- 2
  trend.alpha <- 0.5
  trend.size <- 2
  hline.size <- 1
  line.size <- 2
  hline.alpha <- 0.35
  hline.lty <- "dashed"
  label.size <- 5
  hjust.label <- 1.5
  letter_size <- 4
  errorbar.width <- 0.25
  x.shade.min <- shadedRegion[1]
  x.shade.max <- shadedRegion[2]
  
  setup <- list(
    shade.alpha = shade.alpha,
    shade.fill =shade.fill,
    lwd = lwd,
    pcex = pcex,
    trend.alpha = trend.alpha,
    trend.size = trend.size,
    line.size = line.size,
    hline.size = hline.size,
    hline.alpha = hline.alpha,
    hline.lty = hline.lty,
    errorbar.width = errorbar.width,
    label.size = label.size,
    hjust.label = hjust.label,
    letter_size = letter_size,
    x.shade.min = x.shade.min,
    x.shade.max = x.shade.max
  )
  
  
  fix<- splitoutput |>
    dplyr::filter(Data %in% plotdata, #c("calfin"),
                  Region %in% filterEPUs) |>
    dplyr::group_by(Region, Season) |>
    dplyr::summarise(max = max(Estimate, na.rm=T))
  
  p <- splitoutput |>
    dplyr::filter(Data %in% plotdata, #c("calfin"),
                  Region %in% filterEPUs) |>
    dplyr::group_by(Region, Season) |>
    dplyr::left_join(fix) |>
    dplyr::mutate(#Value = Value/resca,
      Mean = as.numeric(Estimate),
      SE = Std..Error.for.Estimate,
      Mean = Mean/max,
      SE = SE/max,
      Upper = Mean + SE,
      Lower = Mean - SE) |>
    ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, linetype = modname, group = modname))+
    ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
                      xmin = setup$x.shade.min , xmax = setup$x.shade.max,
                      ymin = -Inf, ymax = Inf) +
    ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.5)+
    ggplot2::geom_point()+
    ggplot2::geom_line()+
    ggplot2::ggtitle(plottitle)+
    ggplot2::ylab(expression("Relative abundance"))+
    ggplot2::xlab(ggplot2::element_blank())+
    ggplot2::facet_wrap(Region~Season, ncol = ncols, 
                        labeller = label_wrap_gen(multi_line=FALSE))+
    ecodata::geom_gls()+
    ecodata::theme_ts()+
    ecodata::theme_facet()+
    ecodata::theme_title() +
    ggplot2::theme(legend.position = "bottom")
  
  return(p)
}

Comparison of small copepod models with and without covariates

Overview of small copepod model indices (no covariates), including the herring larval season model (Sep-Feb).

splitnocov <- splitoutput |> dplyr::filter(!str_detect(modname, "_doy"))

plot_zooindices(splitoutput = splitnocov, 
             plotdata = "smallcopeALL", 
             plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"), 
             plottitle = "Small copepods") 

Fall small copepod trends

Do we get the same trend from the day of year covariate models and those without covariates? Scale is different.

plot_indices_comp <- function(splitoutput, 
                                 plotdata, 
                                 plotregions, 
                                 plottitle,
                                 comptype){
  
  filterEPUs <- plotregions #c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU")
  
  seasons <- splitoutput |> dplyr::filter(Data==plotdata) |> dplyr::select(Season) |> dplyr::distinct()
  
  ncols <- dim(seasons)[1]
  
  currentMonth <- lubridate::month(Sys.Date())
  currentYear <- lubridate::year(Sys.Date())
  if (currentMonth > 4) {
    endShade <- currentYear
  } else {
    endShade <- currentYear - 1
  }
  shadedRegion <- c(endShade-9,endShade)
  
  shade.alpha <- 0.3
  shade.fill <- "lightgrey"
  lwd <- 1
  pcex <- 2
  trend.alpha <- 0.5
  trend.size <- 2
  hline.size <- 1
  line.size <- 2
  hline.alpha <- 0.35
  hline.lty <- "dashed"
  label.size <- 5
  hjust.label <- 1.5
  letter_size <- 4
  errorbar.width <- 0.25
  x.shade.min <- shadedRegion[1]
  x.shade.max <- shadedRegion[2]
  
  setup <- list(
    shade.alpha = shade.alpha,
    shade.fill =shade.fill,
    lwd = lwd,
    pcex = pcex,
    trend.alpha = trend.alpha,
    trend.size = trend.size,
    line.size = line.size,
    hline.size = hline.size,
    hline.alpha = hline.alpha,
    hline.lty = hline.lty,
    errorbar.width = errorbar.width,
    label.size = label.size,
    hjust.label = hjust.label,
    letter_size = letter_size,
    x.shade.min = x.shade.min,
    x.shade.max = x.shade.max
  )
  
  if(comptype=="trend"){
    
    tsmean <- splitoutput |>
      dplyr::filter(Data %in% plotdata,
                  Region %in% filterEPUs) |>
      dplyr::group_by(modname, Region) |>
      dplyr::mutate(fmean = mean(Estimate, na.rm=TRUE))
    
    p <-ggplot(tsmean, aes(x=Time, y=((Estimate-fmean)/fmean), colour=modname)) +
      #geom_errorbar(aes(ymin=(Estimate+Std..Error.for.Estimate - fmean)/fmean,
      #                  ymax=(Estimate-Std..Error.for.Estimate - fmean)/fmean))+
      geom_point(aes(shape=modname))+
      geom_line()+
      #acet_wrap(~fct_relevel(Type, "Ecoregion", "Bluefish"), scales = "free_y") +
      facet_wrap(~Region, scales = "free_y", ncol = 1) +
      #scale_y_continuous(labels=function(x)round(x/foragemax, digits = 2))+
      ylab("Relative index scaled to time series mean")  +
      ggtitle(paste(plottitle, "Trend comparison")) +
      theme(#legend.position = c(1, 0),
        #legend.justification = c(1, 0),
        legend.position = "bottom",
        legend.text = element_text(size=rel(0.5)),
        legend.key.size = unit(0.5, 'lines'),
        legend.title = element_text(size=rel(0.5)))
  }
    
  if(comptype=="scale"){
    
    scalecomp <- splitoutput |>
      dplyr::filter(Data %in% plotdata,
                    Region %in% filterEPUs) 
    
    indexmax <-  max(scalecomp$Estimate, na.rm=TRUE)
    
    p <-ggplot(scalecomp, aes(x=Time, y=Estimate, colour=modname)) +
      #geom_errorbar(aes(ymin=Estimate+Std..Error.for.Estimate, ymax=Estimate-Std..Error.for.Estimate))+
      geom_ribbon(aes(ymin=Estimate+Std..Error.for.Estimate, ymax=Estimate-Std..Error.for.Estimate, fill=modname), linetype = 0, alpha = 0.15)+
      geom_point(aes(shape=modname))+
      geom_line()+
      #acet_wrap(~fct_relevel(Type, "Ecoregion", "Bluefish"), scales = "free_y") +
      facet_wrap(~Region, scales = "free_y", ncol = 1) +
      scale_y_continuous(labels=function(x)round(x/indexmax, digits = 2))+
      ylab("Relative index scaled to maximum")  +
      ggtitle(paste(plottitle, "Scale comparison"))+
      theme(#legend.position = c(1, 0),
        #legend.justification = c(1, 0),
        legend.position = "bottom",
        legend.text = element_text(size=rel(0.5)),
        legend.key.size = unit(0.5, 'lines'),
        legend.title = element_text(size=rel(0.5)))
    
  }  
  
  return(p)
}

fallonly <- splitoutput |> dplyr::filter(Season=="fall")

plot_indices_comp(splitoutput = fallonly, 
             plotdata = "smallcopeALL", 
             plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"), 
             plottitle = "Small copepods (All)",
             comptype = "trend")

fallonly <- splitoutput |> dplyr::filter(Season=="fall")

plot_indices_comp(splitoutput = fallonly, 
             plotdata = "smallcopeALL", 
             plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"), 
             plottitle = "Small copepods (All)",
             comptype = "scale")

Spring large copepod models

The Day of Year covariate model did converge in spring for large copepods.

fallonly <- splitoutput |> dplyr::filter(Season=="spring")

plot_indices_comp(splitoutput = fallonly, 
             plotdata = "lgcopeALL", 
             plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"), 
             plottitle = "Large copepods (All)",
             comptype = "trend")

fallonly <- splitoutput |> dplyr::filter(Season=="spring")

plot_indices_comp(splitoutput = fallonly, 
             plotdata = "lgcopeALL", 
             plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"), 
             plottitle = "Large copepods (All)",
             comptype = "scale")