style code

DESCRIPTION

@@ -33,5 +33,5 @@ Remotes:
     poissonconsulting/embr
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr

R/analyse.R

@@ -1,30 +1,32 @@
 jmb_analyse_chain <- function(inits, loaded, data, monitor, niters, ngens, nthin, quiet) {
-
   capture_output <- if (quiet) function(x) suppressWarnings(capture.output(x)) else eval
 
   capture_output(
     jags_model <- rjags::jags.model(loaded, data, inits = inits, n.adapt = 0, quiet = quiet)
   )
 
   niters <- niters * nthin
-  adapted <- rjags::adapt(jags_model, n.iter = floor(niters / 2),
-                          progress.bar = "none", end.adaptation = TRUE)
+  adapted <- rjags::adapt(jags_model,
+    n.iter = floor(niters / 2),
+    progress.bar = "none", end.adaptation = TRUE
+  )
 
   if (!adapted) warning("incomplete adaptation")
 
   update(jags_model, n.iter = floor(niters / 2), progress.bar = "none")
 
   monitor <- monitor[monitor %in% stats::variable.names(jags_model)]
 
-  jags_samples <- rjags::jags.samples(model = jags_model, variable.names = monitor,
-                                      n.iter = niters, thin = nthin, progress.bar = "none")
+  jags_samples <- rjags::jags.samples(
+    model = jags_model, variable.names = monitor,
+    n.iter = niters, thin = nthin, progress.bar = "none"
+  )
 
   list(jags_model = jags_model, jags_samples = jags_samples)
 }
 
 #' @export
 analyse1.jmb_model <- function(model, data, loaded, nchains, niters, nthin, quiet, glance, parallel, ...) {
-
   timer <- timer::Timer$new()
   timer$start()
 
@@ -37,23 +39,27 @@ analyse1.jmb_model <- function(model, data, loaded, nchains, niters, nthin, quie
   monitor <- embr::monitor(model)
   monitor <- monitor[!monitor %in% names(data)]
 
-  jags_chains <- llply(inits, .fun = jmb_analyse_chain,
-                       .parallel = parallel,
-                       loaded = loaded,
-                       data = data,
-                       monitor = monitor,
-                       niters = niters,
-                       nthin = nthin,
-                       quiet = quiet)
+  jags_chains <- llply(inits,
+    .fun = jmb_analyse_chain,
+    .parallel = parallel,
+    loaded = loaded,
+    data = data,
+    monitor = monitor,
+    niters = niters,
+    nthin = nthin,
+    quiet = quiet
+  )
 
   mcmc <- llply(jags_chains, function(x) x$jags_samples)
   mcmc <- lapply(mcmc, function(x) mcmcr::as.mcmcr(lapply(x, mcmcr::as.mcmcarray)))
   mcmc <- Reduce(mcmcr::bind_chains, mcmc)
 
-  obj <- c(obj, inits = list(inits),
-             jags_chains = list(jags_chains),
-             mcmcr = list(mcmc),
-             nthin = nthin)
+  obj <- c(obj,
+    inits = list(inits),
+    jags_chains = list(jags_chains),
+    mcmcr = list(mcmc),
+    nthin = nthin
+  )
 
   obj$duration <- timer$elapsed()
   class(obj) <- c("jmb_analysis", "mb_analysis")
@@ -62,4 +68,3 @@ analyse1.jmb_model <- function(model, data, loaded, nchains, niters, nthin, quie
 
   obj
 }
-

R/inits.R

@@ -1,7 +1,9 @@
 inits <- function(data, gen_inits, nchains) {
   rngs <- rjags::parallel.seeds("base::BaseRNG", nchains)
 
-  if (identical(gen_inits(data), list())) return(rngs)
+  if (identical(gen_inits(data), list())) {
+    return(rngs)
+  }
 
   inits <- list()
   for (i in 1:nchains) {

R/sd-priors-by.R

@@ -1,16 +1,18 @@
 #' @export
 sd_priors_by.jmb_code <- function(
-  x, by = 10, distributions = c("normal", "lognormal", "t"), ...) {
+    x, by = 10, distributions = c("normal", "lognormal", "t"), ...) {
   chk_number(by)
   chk_range(by, c(0.001, 1000))
   chk_unused(...)
 
   chk_s3_class(distributions, "character")
   chk_unique(distributions)
-  chk_subset(distributions,  c("laplace", "logistic", "lognormal",
-                                "normal", "t", "nt"))
+  chk_subset(distributions, c(
+    "laplace", "logistic", "lognormal",
+    "normal", "t", "nt"
+  ))
 
-  if(!length(distributions)) {
+  if (!length(distributions)) {
     wrn("No prior distributions included.")
     return(x)
   }
@@ -20,18 +22,24 @@ sd_priors_by.jmb_code <- function(
   pattern2 <- "\\s*[(][^,)]+,\\s*)((\\d+[.]{0,1}\\d*)|(\\d*[.]{0,1}\\d+))(\\s*\\^\\s*-\\s*2\\s*)([)])"
   pattern3 <- "\\s*[(][^,)]+,\\s*)((\\d+[.]{0,1}\\d*)|(\\d*[.]{0,1}\\d+))(\\s*\\^\\s*-\\s*2\\s*)(,[^,)]+[)])"
   replacement <- paste0("\\1(\\2 * ", by, ")^-2\\6")
-  if("laplace" %in% distributions)
-      x <- gsub(paste0("(~\\s*ddexp", pattern2), replacement, x)
-  if("logistic" %in% distributions)
-      x <- gsub(paste0("(~\\s*dlogis", pattern2), replacement, x)
-  if("lognormal" %in% distributions)
-      x <- gsub(paste0("(~\\s*dlnorm", pattern2), replacement, x)
-  if("normal" %in% distributions)
-      x <- gsub(paste0("(~\\s*dnorm", pattern2), replacement, x)
-  if("t" %in% distributions)
-      x <- gsub(paste0("(~\\s*dt", pattern3), replacement, x)
-  if("nt" %in% distributions)
-      x <- gsub(paste0("(~\\s*dnt", pattern3), replacement, x)
+  if ("laplace" %in% distributions) {
+    x <- gsub(paste0("(~\\s*ddexp", pattern2), replacement, x)
+  }
+  if ("logistic" %in% distributions) {
+    x <- gsub(paste0("(~\\s*dlogis", pattern2), replacement, x)
+  }
+  if ("lognormal" %in% distributions) {
+    x <- gsub(paste0("(~\\s*dlnorm", pattern2), replacement, x)
+  }
+  if ("normal" %in% distributions) {
+    x <- gsub(paste0("(~\\s*dnorm", pattern2), replacement, x)
+  }
+  if ("t" %in% distributions) {
+    x <- gsub(paste0("(~\\s*dt", pattern3), replacement, x)
+  }
+  if ("nt" %in% distributions) {
+    x <- gsub(paste0("(~\\s*dnt", pattern3), replacement, x)
+  }
 
   mb_code(x)
 }

README.Rmd

@@ -62,10 +62,11 @@ for (i in 1:length(Pairs)) {
 }")
 
 # define data types and center year
-model <- update_model(model, 
+model <- update_model(model,
   select_data = list("Pairs" = integer(), "Year*" = integer(), Annual = factor()),
   derived = "sAnnual",
-  random_effects = list(bAnnual = "Annual"))
+  random_effects = list(bAnnual = "Annual")
+)
 
 data <- bauw::peregrine
 data$Annual <- factor(data$Year)
@@ -107,7 +108,7 @@ devtools::install_github("poissonconsulting/jmbr")
 ## Citation
 
 ```{r, comment="", echo=FALSE}
-citation(package = 'jmbr')
+citation(package = "jmbr")
 ```
 
 ## Contribution

tests/testthat/test-code.R

@@ -1,5 +1,4 @@
 test_that("analyse", {
-
   template <- "model{
 
   bIntercept ~ dlogis(0, 5^-2)
@@ -42,12 +41,16 @@ test_that("analyse", {
   code10 <- sd_priors_by(code, 10, distributions = c("logistic", "normal", "lognormal", "t"))
   expect_true(is.jmb_code(code10))
   expect_identical(pars(code10), pars(code))
-  expect_identical(as.character(code10),
-                   "model{\n\n  bIntercept ~ dlogis(0, (5 * 10)^-2)\n  bYear ~ dnorm(0, (.5 * 10)^-2)\n\n  bHabitatQuality[1] <- 0\n  for(i in 2:nHabitatQuality) {\n    bHabitatQuality[i] ~ dnorm(0, (5. * 10)^-2) T(0,)\n  }\n\n  log_sSiteYear ~ dlnorm(0, (5 * 10)^-2)\n  log_sDensity ~ dt(0, (5 * 10)^-2, 4.5)\n\n  log(sSiteYear) <- log_sSiteYear\n  log(sDensity) <- log_sDensity\n\n  for(i in 1:nSite) {\n    for(j in 1:nYearFactor) {\n      bSiteYear[i,j] ~ dnorm(0, sSiteYear^-2)\n    }\n  }\n\n  for(i in 1:length(Density)) {\n    eDensity[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]\n    Density[i] ~ dlnorm(eDensity[i], sDensity^-2)\n  }\n}")
+  expect_identical(
+    as.character(code10),
+    "model{\n\n  bIntercept ~ dlogis(0, (5 * 10)^-2)\n  bYear ~ dnorm(0, (.5 * 10)^-2)\n\n  bHabitatQuality[1] <- 0\n  for(i in 2:nHabitatQuality) {\n    bHabitatQuality[i] ~ dnorm(0, (5. * 10)^-2) T(0,)\n  }\n\n  log_sSiteYear ~ dlnorm(0, (5 * 10)^-2)\n  log_sDensity ~ dt(0, (5 * 10)^-2, 4.5)\n\n  log(sSiteYear) <- log_sSiteYear\n  log(sDensity) <- log_sDensity\n\n  for(i in 1:nSite) {\n    for(j in 1:nYearFactor) {\n      bSiteYear[i,j] ~ dnorm(0, sSiteYear^-2)\n    }\n  }\n\n  for(i in 1:length(Density)) {\n    eDensity[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]\n    Density[i] ~ dlnorm(eDensity[i], sDensity^-2)\n  }\n}"
+  )
 
   coder <- sd_priors_by(code, 10, distributions = "normal")
   expect_true(is.jmb_code(coder))
   expect_identical(pars(coder), pars(code))
-  expect_identical(as.character(coder),
-                   "model{\n\n  bIntercept ~ dlogis(0, 5^-2)\n  bYear ~ dnorm(0, (.5 * 10)^-2)\n\n  bHabitatQuality[1] <- 0\n  for(i in 2:nHabitatQuality) {\n    bHabitatQuality[i] ~ dnorm(0, (5. * 10)^-2) T(0,)\n  }\n\n  log_sSiteYear ~ dlnorm(0, 5^-2)\n  log_sDensity ~ dt(0, 5^-2, 4.5)\n\n  log(sSiteYear) <- log_sSiteYear\n  log(sDensity) <- log_sDensity\n\n  for(i in 1:nSite) {\n    for(j in 1:nYearFactor) {\n      bSiteYear[i,j] ~ dnorm(0, sSiteYear^-2)\n    }\n  }\n\n  for(i in 1:length(Density)) {\n    eDensity[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]\n    Density[i] ~ dlnorm(eDensity[i], sDensity^-2)\n  }\n}")
+  expect_identical(
+    as.character(coder),
+    "model{\n\n  bIntercept ~ dlogis(0, 5^-2)\n  bYear ~ dnorm(0, (.5 * 10)^-2)\n\n  bHabitatQuality[1] <- 0\n  for(i in 2:nHabitatQuality) {\n    bHabitatQuality[i] ~ dnorm(0, (5. * 10)^-2) T(0,)\n  }\n\n  log_sSiteYear ~ dlnorm(0, 5^-2)\n  log_sDensity ~ dt(0, 5^-2, 4.5)\n\n  log(sSiteYear) <- log_sSiteYear\n  log(sDensity) <- log_sDensity\n\n  for(i in 1:nSite) {\n    for(j in 1:nYearFactor) {\n      bSiteYear[i,j] ~ dnorm(0, sSiteYear^-2)\n    }\n  }\n\n  for(i in 1:length(Density)) {\n    eDensity[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]\n    Density[i] ~ dlnorm(eDensity[i], sDensity^-2)\n  }\n}"
+  )
 })

tests/testthat/test-model.R

@@ -32,13 +32,17 @@ test_that("analyse", {
     prediction[i] <- exp(bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]])
 } "
 
-  model <- model(code = template,
-                 select_data = list("Year+" = numeric(), YearFactor = factor(),
-                                    Site = factor(), Density = numeric(),
-                                    HabitatQuality = factor()),
-                 fixed = "^(b|l)", derived = "eDensity",
-                 random_effects = list(bSiteYear = c("Site", "YearFactor")),
-                 new_expr = new_expr)
+  model <- model(
+    code = template,
+    select_data = list(
+      "Year+" = numeric(), YearFactor = factor(),
+      Site = factor(), Density = numeric(),
+      HabitatQuality = factor()
+    ),
+    fixed = "^(b|l)", derived = "eDensity",
+    random_effects = list(bSiteYear = c("Site", "YearFactor")),
+    new_expr = new_expr
+  )
 
   expect_identical(class(model), c("jmb_model", "mb_model"))
   expect_true(is.jmb_model(model))