Gets a copy of the BUGS code and writes it to the screen and (optionally) to a text file
Usage
get_bmaBUGScode(
option = "smooth",
print.screen = FALSE,
save.local = FALSE,
incl.2deriv = FALSE,
Y1perfect = FALSE,
seFromData = FALSE
)Arguments
- option
text string defining which particular variant is desired
- print.screen
Logical, should the code be printed to the console?
- save.local
Logical, should text file of the BUGS code be produced?
- incl.2deriv
Logical, Should there be a calculation of the indicator's second derivative?
- Y1perfect
Logical, should the first year for each species be assumed to be known without error (`TRUE`)
- seFromData
Logical, Should the standard errors be read in from data (`TRUE`) or estimated (`FALSE`)
Details
There are a number of model to choose from:
"smooth"The default option. Indicator defined by Growth rates, with Ruppert smoother, allowing for species to join late. Error on the first year of each species' time-series is assumed to be zero. The indicator is the expected value of the geometric mean across species (with missing species imputed). Includes three options: `seFromData` `Y1perfect` and `incl.2deriv`. See bayesian_meta_analysis for mode details. Using the default values `seFromData = FALSE` and `Y1perfect = TRUE` are the options used in Freeman et al. (2020)."smooth_det2"Equivalent to smooth with `seFromData = TRUE` and `Y1perfect = FALSE`. Retained for backwards compatability. Choosing this option will overwrite user-entered options for `seFromData` and `Y1perfect`."smooth_det_sigtheta"Equivalent to smooth with `seFromData = FALSE` and `Y1perfect = FALSE`. Retained for backwards compatability. Choosing this option will overwrite user-entered options for `seFromData` and `Y1perfect`."smooth_det"Specific variant of smooth_det2 - deprecated but retained here for backward compatibility
Examples
get_bmaBUGScode(option="smooth", print.screen=TRUE, save.local=FALSE)
#> model {
#> ################### Define priors ###########################
#>
#> # process errors
#> tau.spi <- pow(sigma.spi,-2)
#> sigma.spi ~ dunif(0.0001,30)
#>
#> # observation error is constant
#> theta ~ dunif(0.0001,10)
#>
#> ######################### Smoothing #######################
#>
#> beta[1] ~ dnorm(0, 0.000001)
#> beta[2] ~ dnorm(0, 0.000001)
#> taub ~ dgamma(0.000001, 0.000001)
#> for(k in 1:num.knots){b[k]~dnorm(0,taub)}
#>
#> for (t in 1:(nyears - 1)){
#> logLambda[t] <- m[t]
#> m[t] <- mfe[t] + mre[t]
#> mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]
#> for (k in 1:num.knots){
#> temp[t,k] <- b[k] * Z[t,k]
#> }
#> mre[t]<-sum(temp[t,1:num.knots])
#> }
#>
#> ################### Define likelihood ###########################
#>
#> M[1] <- 0
#>
#> for (t in 2:nyears){
#> M[t] <- M[t-1] + logLambda[t-1]
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears-1)){
#> spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)
#> }}
#>
#> for (s in 1:nsp){
#> for (t in 1:FY[s]){
#> spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]
#> }
#>
#> for (t in (FY[s]+1):(nyears)){
#> spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])
#> estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])
#> tau.obs[s,t] <- pow(theta, -2)
#> }}
#>
#> #################### geomean of expected values ######################
#>
#> for (t in 1:nyears){
#> Mprime[t] <- sum(spindex[,t])/nsp
#> }
#>
#> }
#> [1] "model {\n ################### Define priors ###########################\n\n # process errors\n tau.spi <- pow(sigma.spi,-2)\n sigma.spi ~ dunif(0.0001,30)\n\n # observation error is constant\n theta ~ dunif(0.0001,10)\n\n ######################### Smoothing #######################\n\n beta[1] ~ dnorm(0, 0.000001)\n beta[2] ~ dnorm(0, 0.000001)\n taub ~ dgamma(0.000001, 0.000001)\n for(k in 1:num.knots){b[k]~dnorm(0,taub)}\n \n for (t in 1:(nyears - 1)){\n logLambda[t] <- m[t]\n m[t] <- mfe[t] + mre[t]\n mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]\n for (k in 1:num.knots){\n temp[t,k] <- b[k] * Z[t,k]\n }\n mre[t]<-sum(temp[t,1:num.knots])\n } \n \n ################### Define likelihood ###########################\n\n M[1] <- 0 \n \n for (t in 2:nyears){\n M[t] <- M[t-1] + logLambda[t-1]\n }\n\n for (s in 1:nsp){\n for (t in 1:(nyears-1)){\n spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)\n }}\n\n for (s in 1:nsp){\n for (t in 1:FY[s]){\n spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]\n }\n\n for (t in (FY[s]+1):(nyears)){\n spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])\n estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])\n tau.obs[s,t] <- pow(theta, -2)\n }}\n\n #################### geomean of expected values ######################\n \n for (t in 1:nyears){\n Mprime[t] <- sum(spindex[,t])/nsp\n }\n\n}"
get_bmaBUGScode(option="smooth_JABES", print.screen=TRUE, save.local=FALSE)
#> model {
#> ################### Define priors ###########################
#>
#> # process errors
#> tau.spi <- pow(sigma.spi,-2)
#> sigma.spi ~ dunif(0.0001,30)
#>
#> # observation error is constant
#> theta ~ dunif(0.0001,10)
#>
#> ######################### Smoothing #######################
#>
#> beta[1] ~ dnorm(0, 0.000001)
#> beta[2] ~ dnorm(0, 0.000001)
#> taub ~ dgamma(0.000001, 0.000001)
#> for(k in 1:num.knots){b[k]~dnorm(0,taub)}
#>
#> for (t in 1:(nyears - 1)){
#> logLambda[t] <- m[t]
#> m[t] <- mfe[t] + mre[t]
#> mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]
#> for (k in 1:num.knots){
#> temp[t,k] <- b[k] * Z[t,k]
#> }
#> mre[t]<-sum(temp[t,1:num.knots])
#> }
#>
#> ################### Define likelihood ###########################
#>
#> M[1] <- 0
#>
#> for (t in 2:nyears){
#> M[t] <- M[t-1] + logLambda[t-1]
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears-1)){
#> spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)
#> }}
#>
#> for (s in 1:nsp){
#> for (t in 1:(FY[s]-1)){
#> spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]
#> }
#>
#> spindex[s,FY[s]] <- estimate[s,FY[s]] # we assume the first year is known without error
#>
#> for (t in (FY[s]+1):(nyears)){
#> spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])
#> estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])
#> tau.obs[s,t] <- pow(theta, -2)
#> }}
#>
#> #################### geomean of expected values ######################
#>
#> for (t in 1:nyears){
#> Mprime[t] <- sum(spindex[,t])/nsp
#> }
#>
#> }
#> [1] "model {\n ################### Define priors ###########################\n\n # process errors\n tau.spi <- pow(sigma.spi,-2)\n sigma.spi ~ dunif(0.0001,30)\n\n # observation error is constant\n theta ~ dunif(0.0001,10)\n\n ######################### Smoothing #######################\n\n beta[1] ~ dnorm(0, 0.000001)\n beta[2] ~ dnorm(0, 0.000001)\n taub ~ dgamma(0.000001, 0.000001)\n for(k in 1:num.knots){b[k]~dnorm(0,taub)}\n \n for (t in 1:(nyears - 1)){\n logLambda[t] <- m[t]\n m[t] <- mfe[t] + mre[t]\n mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]\n for (k in 1:num.knots){\n temp[t,k] <- b[k] * Z[t,k]\n }\n mre[t]<-sum(temp[t,1:num.knots])\n } \n \n ################### Define likelihood ###########################\n\n M[1] <- 0 \n \n for (t in 2:nyears){\n M[t] <- M[t-1] + logLambda[t-1]\n }\n\n for (s in 1:nsp){\n for (t in 1:(nyears-1)){\n spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)\n }}\n\n for (s in 1:nsp){\n for (t in 1:(FY[s]-1)){\n spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]\n }\n\n spindex[s,FY[s]] <- estimate[s,FY[s]] # we assume the first year is known without error\n\n for (t in (FY[s]+1):(nyears)){\n spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])\n estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])\n tau.obs[s,t] <- pow(theta, -2)\n }}\n\n #################### geomean of expected values ######################\n \n for (t in 1:nyears){\n Mprime[t] <- sum(spindex[,t])/nsp\n }\n\n}"
get_bmaBUGScode(option="smooth_det", print.screen=TRUE, save.local=FALSE)
#> model {
#>
#> ################### Define priors
#> # process errors
#> tau.spi <- pow(sigma.spi,-2)
#> sigma.spi ~ dunif(0,30)
#> tau.sg <- pow(sigma.sg,-2)
#> sigma.sg ~ dunif(0,1000)
#>
#> # observation errors
#> # one value per site-species
#> for (s in 1:nsp){
#> for (t in 1:nyears){
#> sigma.obs[s,t] ~ dunif(0, max_se) # for the missing values
#> }}
#>
#> for (s in 1:nsp){
#> spindex[s,1]~dnorm(Mprime.raw[1],tau.spi)
#> }
#>
#> ################### Define likelihood #######################
#>
#> for (t in 1:(nyears-1)){
#> logLambda[t] <- Mprime.raw[t+1] - Mprime.raw[t]
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears-1)){
#> spgrowth[s,t]~dnorm(logLambda[t],tau.sg)
#> }
#> }
#>
#> for (s in 1:nsp){
#> for (t in 2:(nyears)){
#> spindex[s,t]<-spindex[s,1] + sum(spgrowth[s,1:(t-1)])
#> }
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears)){
#> estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])
#> tau.obs[s,t] <- pow(sigma.obs[s,t], -2)
#> }
#> }
#>
#> M <- m
#>
#> ######################### end likelihood ###########################
#>
#>
#> ######################### Smoothing #######################
#>
#> beta[1] ~ dnorm(0, 0.000001)
#> beta[2] ~ dnorm(0, 0.000001)
#> taub ~ dgamma(0.000001, 0.000001)
#> for(k in 1:num.knots){b[k]~dnorm(0,taub)}
#>
#> for (t in 1:(nyears - 1)){
#> logLambda[t] <- m[t]
#> m[t] <- mfe[t] + mre[t]
#> mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]
#> for (k in 1:num.knots){
#> temp[t,k] <- b[k] * Z[t,k]
#> }
#> mre[t]<-sum(temp[t,1:num.knots])
#> }
#>
#> }
#> [1] "model {\n\n ################### Define priors\n # process errors\n tau.spi <- pow(sigma.spi,-2)\n sigma.spi ~ dunif(0,30)\n tau.sg <- pow(sigma.sg,-2)\n sigma.sg ~ dunif(0,1000)\n\n # observation errors\n # one value per site-species\n for (s in 1:nsp){ \n for (t in 1:nyears){\n sigma.obs[s,t] ~ dunif(0, max_se) # for the missing values\n }}\n \n for (s in 1:nsp){\n spindex[s,1]~dnorm(Mprime.raw[1],tau.spi)\n }\n \n ################### Define likelihood #######################\n \n for (t in 1:(nyears-1)){\n logLambda[t] <- Mprime.raw[t+1] - Mprime.raw[t]\n }\n \n for (s in 1:nsp){\n for (t in 1:(nyears-1)){\n spgrowth[s,t]~dnorm(logLambda[t],tau.sg)\n }\n }\n \n for (s in 1:nsp){\n for (t in 2:(nyears)){\n spindex[s,t]<-spindex[s,1] + sum(spgrowth[s,1:(t-1)])\n }\n }\n \n for (s in 1:nsp){\n for (t in 1:(nyears)){\n estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])\n tau.obs[s,t] <- pow(sigma.obs[s,t], -2)\n }\n }\n \n M <- m\n \n ######################### end likelihood ###########################\n \n\n ######################### Smoothing #######################\n\n beta[1] ~ dnorm(0, 0.000001)\n beta[2] ~ dnorm(0, 0.000001)\n taub ~ dgamma(0.000001, 0.000001)\n for(k in 1:num.knots){b[k]~dnorm(0,taub)}\n \n for (t in 1:(nyears - 1)){\n logLambda[t] <- m[t]\n m[t] <- mfe[t] + mre[t]\n mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]\n for (k in 1:num.knots){\n temp[t,k] <- b[k] * Z[t,k]\n }\n mre[t]<-sum(temp[t,1:num.knots])\n } \n \n}"
get_bmaBUGScode(option="smooth_det2", print.screen=TRUE, save.local=FALSE)
#> model {
#> ################### Define priors ###########################
#>
#> # process errors
#> tau.spi <- pow(sigma.spi,-2)
#> sigma.spi ~ dunif(0.0001,30)
#>
#> # observation errors: one value per site-species
#> for (s in 1:nsp){
#> for (t in 1:nyears){
#> sigma.obs[s,t] ~ dunif(0.0001, max_se) # for the missing values
#> }}
#>
#>
#> ######################### Smoothing #######################
#>
#> beta[1] ~ dnorm(0, 0.000001)
#> beta[2] ~ dnorm(0, 0.000001)
#> taub ~ dgamma(0.000001, 0.000001)
#> for(k in 1:num.knots){b[k]~dnorm(0,taub)}
#>
#> for (t in 1:(nyears - 1)){
#> logLambda[t] <- m[t]
#> m[t] <- mfe[t] + mre[t]
#> mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]
#> for (k in 1:num.knots){
#> temp[t,k] <- b[k] * Z[t,k]
#> }
#> mre[t]<-sum(temp[t,1:num.knots])
#> }
#>
#> ################### Define likelihood ###########################
#>
#> M[1] <- 0
#>
#> for (t in 2:nyears){
#> M[t] <- M[t-1] + logLambda[t-1]
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears-1)){
#> spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)
#> }}
#>
#> for (s in 1:nsp){
#> for (t in 1:FY[s]){
#> spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]
#> }
#>
#> for (t in (FY[s]+1):(nyears)){
#> spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])
#> estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])
#> tau.obs[s,t] <- pow(sigma.obs[s,t], -2)
#> }}
#>
#> #################### geomean of expected values ######################
#>
#> for (t in 1:nyears){
#> Mprime[t] <- sum(spindex[,t])/nsp
#> }
#>
#> }
#> [1] "model {\n ################### Define priors ###########################\n\n # process errors\n tau.spi <- pow(sigma.spi,-2)\n sigma.spi ~ dunif(0.0001,30)\n\n # observation errors: one value per site-species\n for (s in 1:nsp){\n for (t in 1:nyears){\n sigma.obs[s,t] ~ dunif(0.0001, max_se) # for the missing values\n }}\n \n\n ######################### Smoothing #######################\n\n beta[1] ~ dnorm(0, 0.000001)\n beta[2] ~ dnorm(0, 0.000001)\n taub ~ dgamma(0.000001, 0.000001)\n for(k in 1:num.knots){b[k]~dnorm(0,taub)}\n \n for (t in 1:(nyears - 1)){\n logLambda[t] <- m[t]\n m[t] <- mfe[t] + mre[t]\n mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]\n for (k in 1:num.knots){\n temp[t,k] <- b[k] * Z[t,k]\n }\n mre[t]<-sum(temp[t,1:num.knots])\n } \n \n ################### Define likelihood ###########################\n\n M[1] <- 0 \n \n for (t in 2:nyears){\n M[t] <- M[t-1] + logLambda[t-1]\n }\n\n for (s in 1:nsp){\n for (t in 1:(nyears-1)){\n spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)\n }}\n\n for (s in 1:nsp){\n for (t in 1:FY[s]){\n spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]\n }\n\n for (t in (FY[s]+1):(nyears)){\n spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])\n estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])\n tau.obs[s,t] <- pow(sigma.obs[s,t], -2)\n }}\n\n #################### geomean of expected values ######################\n \n for (t in 1:nyears){\n Mprime[t] <- sum(spindex[,t])/nsp\n }\n\n}"
get_bmaBUGScode(option="smooth_det_sigtheta", print.screen=TRUE, save.local=FALSE)
#> model {
#> ################### Define priors ###########################
#>
#> # process errors
#> tau.spi <- pow(sigma.spi,-2)
#> sigma.spi ~ dunif(0.0001,30)
#>
#> # observation error is constant
#> theta ~ dunif(0.0001,10)
#>
#> ######################### Smoothing #######################
#>
#> beta[1] ~ dnorm(0, 0.000001)
#> beta[2] ~ dnorm(0, 0.000001)
#> taub ~ dgamma(0.000001, 0.000001)
#> for(k in 1:num.knots){b[k]~dnorm(0,taub)}
#>
#> for (t in 1:(nyears - 1)){
#> logLambda[t] <- m[t]
#> m[t] <- mfe[t] + mre[t]
#> mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]
#> for (k in 1:num.knots){
#> temp[t,k] <- b[k] * Z[t,k]
#> }
#> mre[t]<-sum(temp[t,1:num.knots])
#> }
#>
#> ################### Define likelihood ###########################
#>
#> M[1] <- 0
#>
#> for (t in 2:nyears){
#> M[t] <- M[t-1] + logLambda[t-1]
#> }
#>
#> for (s in 1:nsp){
#> for (t in 1:(nyears-1)){
#> spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)
#> }}
#>
#> for (s in 1:nsp){
#> for (t in 1:FY[s]){
#> spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]
#> }
#>
#> for (t in (FY[s]+1):(nyears)){
#> spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])
#> estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])
#> tau.obs[s,t] <- pow(theta, -2)
#> }}
#>
#> #################### geomean of expected values ######################
#>
#> for (t in 1:nyears){
#> Mprime[t] <- sum(spindex[,t])/nsp
#> }
#>
#> }
#> [1] "model {\n ################### Define priors ###########################\n\n # process errors\n tau.spi <- pow(sigma.spi,-2)\n sigma.spi ~ dunif(0.0001,30)\n\n # observation error is constant\n theta ~ dunif(0.0001,10)\n\n ######################### Smoothing #######################\n\n beta[1] ~ dnorm(0, 0.000001)\n beta[2] ~ dnorm(0, 0.000001)\n taub ~ dgamma(0.000001, 0.000001)\n for(k in 1:num.knots){b[k]~dnorm(0,taub)}\n \n for (t in 1:(nyears - 1)){\n logLambda[t] <- m[t]\n m[t] <- mfe[t] + mre[t]\n mfe[t] <- beta[1] * X[t,1] + beta[2] * X[t,2]\n for (k in 1:num.knots){\n temp[t,k] <- b[k] * Z[t,k]\n }\n mre[t]<-sum(temp[t,1:num.knots])\n } \n \n ################### Define likelihood ###########################\n\n M[1] <- 0 \n \n for (t in 2:nyears){\n M[t] <- M[t-1] + logLambda[t-1]\n }\n\n for (s in 1:nsp){\n for (t in 1:(nyears-1)){\n spgrowth[s,t] ~ dnorm(logLambda[t], tau.spi)\n }}\n\n for (s in 1:nsp){\n for (t in 1:FY[s]){\n spindex[s,t] <- spindex[s,t+1] - spgrowth[s,t]\n }\n\n for (t in (FY[s]+1):(nyears)){\n spindex[s,t] <- estimate[s,FY[s]] + sum(spgrowth[s,FY[s]:(t-1)])\n estimate[s,t] ~ dnorm(spindex[s,t], tau.obs[s,t])\n tau.obs[s,t] <- pow(theta, -2)\n }}\n\n #################### geomean of expected values ######################\n \n for (t in 1:nyears){\n Mprime[t] <- sum(spindex[,t])/nsp\n }\n\n}"