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SR_all_LVM.R
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SR_all_LVM.R
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#The purpose of this script is to fit models of transition between spawners and juvenile emigrants expressing different juvenile life histories
#libraries
library(here)
library(tidyverse)
library(viridis)
library(TMB)
library(TMBhelper)
library(readxl)
library(glmmTMB)
## Analysis of screw trap data to estimate daily juvenile emigrants ##
refit<-FALSE # whether to refit models rather that loading existing fitted model objects if they exist (takes about ~20 minutes; 5 for model fitting and 15 for parametric bootstrap)
#fit/load model
source(here("src","ST_all.R"))
ST_all<-ST_all_func(refit=refit)
# load functions to calculate geometric means of dialy emigrants and plot
source(here("src","ST_plotting_funcs.R"))
#plot average daily emigrants and LHP breaks
# png(file=here("results","plots","emigration_timing.png"),units="in",width = 6,height=4,res=300)
with(ST_all,ggplot_timing_func(all_emigrants_estimates,all_data_lists,across_stream_geomean,mix_geomean_dens,breaks))
# dev.off()
#plot average daily temperature and discharge
# png(file=here("results","plots","temp_dis.png"),units="in",width = 4.5,height=3,res=300)
with(ST_all,plot_dis_temp_func(all_data_lists))
# dev.off()
rm(list=ls()[which(ls()!="ST_all")])#remove all all the functions used for screw trap model
#----------------------------------------------------------------------------
## Analysis of screw trap data to estimate daily juvenile emigrants ##
## Load data ##
{
#read redd data from Hatchery program annual report (Hillman et al 2020)
redds<-read_csv(here("data","redd_counts.csv")) %>% pivot_longer(c("Chiwawa","Nason","White"),"stream",values_to="redds")
##emigrant abundance estimates (log means and standard deviations from screw trap model)
all_emigrants_estimates<-ST_all$all_emigrants_estimates; rm("ST_all")
#load stream flow covariate values (flow_covs)
source(here("src","covariates.r"))
# length of habitat surveyed for spawners in each tributary (based on motoring annual report, Hillman et al. 2020)
trib_lengths<-c(32.5,15.4,16.1)
names(trib_lengths)<-c("Chiwawa","Nason","White")
}
## Load functions ##
# source helper functions to make data object, make initial parameter values, and makes map (which fixes certain parameter), all to feed to the TMB model "Stock_recruit_LVM"
source(here("src","SR_helper_functions.R"))
# source plotting functions for visualizing results.
source(here("src","SR_plotting_funcs_factor.R"))
#---------------------------------------------------------------------
#---------------------------------------------------------------------
## Fit models ##
#load TMB model
setwd(here("src","TMB"))
TMB::compile("Stock_recruit_LVM.cpp")
dyn.load(dynlib("Stock_recruit_LVM"))
#---------------------------------------------------------------------
#sometimes have to try a number of times with different starting calues to get a model to converge (takes several minutes)
#there should ideally be multiple converged model with the same BIC = 612.9517
## if its not working, I recommend closing r and then trying again
set.seed(1234)
fit_mod_result<-fit_mod_iter(streams=0:2, #streams to include (Chiwawa, Nason, White)
LHs=1:4, #life histories to include (spring subs, sumemr subs, fall subs, spring yearlings)
n_f=1, # number of latent variable factors to include
fit_env =1, #whether to fit environmentel covariates (1=year, 0 = no)
fit_attempts=20, # numebr of times to attempt to fit model
additional_attempts = 0, #additional attempts after that until a model converges
log_J_max_prior=log(15000)) # prior mean on Jmax hyper-mean
#BIC value from each iteration running the model with different initial parameters
##There should ideally be multiple converged model with the same BIC = 612.9517
fit_mod_result$BIC_vec
# AIC of best fit
fit_mod_result$fit$AIC
save(fit_mod_result,file=here("results","fit_mod_result_12_21_21.Rdata"))
#list of final parameter values
par_out<-fit_mod_result$mod$env$parList(par=fit_mod_result$mod$env$last.par.best)
par_out$rate %>% exp() #penalty parameters
gc() #garbage clean
#parametric bootstrap from posterior of fitted model
sim_post<-rmvnorm_prec(fit_mod_result$mod$env$last.par.best,
fit_mod_result$fit$SD$jointPrecision, 50000, 623 )
#reporting
##calculate and store functional form parameters (alpha, gamma, Jmax) for each posterior samples
FFparams<-array(dim=c(12,50000,3))
for (i in 1:50000){
out<-fit_mod_result$mod$report(par=sim_post[,i])
FFparams[,i,1]<-out$alpha
FFparams[,i,2]<-out$gamma
FFparams[,i,3]<-out$Jmax
}
##calculate quantiles of FF parameters
quant_FF<-apply(FFparams,c(1,3),quantile,probs=c(0.025,.5,0.975))
## table of functional relationship fit parameters
FF_params<- tibble(stream=rep(c("Chiwawa","Nason","White"),each=4),
LH=factor(rep(c("Spr-0","Sum-0","Fall-0", "Spr-1"),times=3),levels=c("Spr-0","Sum-0","Fall-0", "Spr-1")), #alpha parameters
alpha=fit_mod_result$report$alpha, #mean
alpha_lcl=quant_FF[1,,1], #lower 95% confidence limit
alpha_ucl=quant_FF[3,,1], #upper 95% confidence limie
#gamma
gamma=fit_mod_result$report$gamma,
gamma_lcl=quant_FF[1,,2],
gamma_ucl=quant_FF[3,,2],
#Jmax
Jmax=fit_mod_result$report$Jmax,
Jmax_lcl=quant_FF[1,,3],
Jmax_ucl=quant_FF[3,,3],) %>%
arrange(LH,stream) %>%
mutate(across(3:11,round,2))
View(FF_params) # View table
write.csv(FF_params,here("results","FF_params.csv")) #write table CSV file
#hyper means of parameters
##gamma
exp(par_out$beta_gamma)
exp(
sim_post[
which(names(fit_mod_result$mod$env$last.par.best)=="beta_gamma"),]) %>%
apply(1,quantile,probs=c(.025,.5,.975))
##Jmax
exp(par_out$beta_Jmax)
exp(
sim_post[
which(names(fit_mod_result$mod$env$last.par.best)=="beta_Jmax"),]) %>%
apply(1,quantile,probs=c(.025,.5,.975))
# plot of latent and expected juveniles vs spawners
##png(file=here("results","plots","spawn_em_11112021.png"),units="in",height=5,width=6.5,res=300)
spawn_em<-ggplot_spawner_juveniles(mod_fit = fit_mod_result$fit,mod_dat =fit_mod_result$dat,mod_rep=fit_mod_result$report)
##dev.off()
#spawners and juvenile by year
# png(here("results","plots","spaw_em.png"),units="in",res=300,height=5,width=7)
plot_spawn_em_year(mod_dat=fit_mod_result$dat,sum_out = spawn_em$sum_out)
# dev.off()
##expected emigrants vs. spawners
# png(here("results","plots","juv_LH.png"),units="in",res=300,height=4,width=5)
expec_spawn_em_ploft_func(preds=spawn_em$preds)
# dev.off()
# ggsave(here("results","plots","juv_LH.png"))
# Probability environmental covariate coefficients are not equal to zero
pnorm(0,abs(fit_mod_result$fit$SD$par.fixed),sqrt(diag(fit_mod_result$fit$SD$cov.fixed)),lower.tail = TRUE)[names(fit_mod_result$fit$SD$par.fixed)=="beta_e"]*2
# plot environmental covariate coefficients
# png(here("results","plots","coef_plot_all.png"),units="in",res=300,height=4,width=5)
bootstrap_env_cov(fit_mod_result$dat,last_best=fit_mod_result$fit$par , precis=fit_mod_result$fit$SD$cov.fixed )
# dev.off()
# quantiles of paramateric bootstrap distribution of environmental covariates
fit_mod_result$mod$env$last.par.best[which(names(fit_mod_result$mod$env$last.par.best) =="beta_e")]
sim_post[which(names(fit_mod_result$mod$env$last.par.best) =="beta_e"),] %>% apply(1,quantile,probs=c(.025,.5,.975))
#Refit model with no environmental covariates to look at process erros covariance (&correlation) based on the loadings onto the latent variable
#sometimes have to run this a few times to get a model to converge
fit_mod_result_no_env<-fit_mod_iter(streams=0:2,LHs=1:4,n_f=1,fit_env =0, fit_attempts=20,additional_attempts = 0,log_J_max_prior=log(15000))
#BIC value from each iteration running the model with different initial parameters
##There should ideally be multiple converged model with the same BIC = 606.946
fit_mod_result_no_env$BIC_vec
# AIC of best fit
fit_mod_result_no_env$fit$AIC
save(fit_mod_result_no_env,file=here("results","fit_mod_result_no_env_12_21_21.Rdata")) #save fitted model object
# plot process error correlation (takes a minute for bootstrapping p-values)
## png(here("results","plots","correlation.png"),units="in",res=300,height=10,width=10)
corr_plot<-plot_cor(mod_rep = fit_mod_result_no_env$report, mod_fit = fit_mod_result_no_env$fit, mod_dat = fit_mod_result_no_env$dat)
## dev.off()
#end of analysis