* using log directory ‘/data/localhost/ripley/R/packages/tests-donttest/sdmTMB.Rcheck’
* using R Under development (unstable) (2026-08-25 r90447)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
    GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
* running under: Fedora Linux 44 (Server Edition)
* using session charset: UTF-8
* current time: 2026-08-26 00:47:38 UTC
* checking for file ‘sdmTMB/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘sdmTMB’ version ‘1.1.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘sdmTMB’ can be installed ... [5m/10m] OK
* used C++ compiler: ‘g++ (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)’
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [29s/61s] NOTE
Found calls to structure() using deprecated special names:
  sdmTMB/tests/testthat/test-1-fit-basic.R (.Dim: 1)
'.Dim' should be changed to 'dim'.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [45s/101s] OK
* checking examples with --run-donttest ... [117s/223s] ERROR
Running examples in ‘sdmTMB-Ex.R’ failed
The error most likely occurred in:

> ### Name: predict.sdmTMB
> ### Title: Predict from an sdmTMB model
> ### Aliases: predict.sdmTMB
> 
> ### ** Examples
> 
> ## Don't show: 
> if (ggplot2_installed()) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ 
+ d <- pcod_2011
+ mesh <- make_mesh(d, c("X", "Y"), cutoff = 30) # a coarse mesh for example speed
+ m <- sdmTMB(
+  data = d, formula = density ~ 0 + as.factor(year) + depth_scaled + depth_scaled2,
+  time = "year", mesh = mesh, family = tweedie(link = "log")
+ )
+ 
+ # Predictions at original data locations -------------------------------
+ 
+ predictions <- predict(m)
+ head(predictions)
+ 
+ predictions$resids <- residuals(m) # randomized quantile residuals
+ 
+ library(ggplot2)
+ ggplot(predictions, aes(X, Y, col = resids)) + scale_colour_gradient2() +
+   geom_point() + facet_wrap(~year)
+ hist(predictions$resids)
+ qqnorm(predictions$resids); abline(a = 0, b = 1)
+ 
+ # Predictions on new data ----------------------------------------------
+ 
+ qcs_grid_2011 <- replicate_df(qcs_grid, "year", unique(pcod_2011$year))
+ predictions <- predict(m, newdata = qcs_grid_2011)
+ 
+ ## No test: 
+ # A short function for plotting predictions:
+ plot_map <- function(dat, column = est) {
+   ggplot(dat, aes(X, Y, fill = {{ column }})) +
+     geom_raster() +
+     facet_wrap(~year) +
+     coord_fixed()
+ }
+ 
+ plot_map(predictions, exp(est)) +
+   scale_fill_viridis_c(trans = "sqrt") +
+   ggtitle("Prediction (fixed effects + all random effects)")
+ 
+ plot_map(predictions, exp(est_non_rf)) +
+   ggtitle("Prediction (fixed effects and any time-varying effects)") +
+   scale_fill_viridis_c(trans = "sqrt")
+ 
+ plot_map(predictions, est_rf) +
+   ggtitle("All random field estimates") +
+   scale_fill_gradient2()
+ 
+ plot_map(predictions, omega_s) +
+   ggtitle("Spatial random effects only") +
+   scale_fill_gradient2()
+ 
+ plot_map(predictions, epsilon_st) +
+   ggtitle("Spatiotemporal random effects only") +
+   scale_fill_gradient2()
+ 
+ # Visualizing a marginal effect ----------------------------------------
+ 
+ # See the visreg package or the ggeffects::ggeffect() or
+ # ggeffects::ggpredict() functions
+ # To do this manually:
+ 
+ nd <- data.frame(depth_scaled =
+   seq(min(d$depth_scaled), max(d$depth_scaled), length.out = 100))
+ nd$depth_scaled2 <- nd$depth_scaled^2
+ 
+ # Because this is a spatiotemporal model, you'll need at least one time
+ # value. For these population-level predictions, if time isn't also a fixed
+ # effect, it doesn't matter what you pick:
+ nd$year <- 2011L # L: integer to match original data
+ p <- predict(m, newdata = nd, se_fit = TRUE, re_form = NA)
+ ggplot(p, aes(depth_scaled, exp(est),
+   ymin = exp(est - 1.96 * est_se), ymax = exp(est + 1.96 * est_se))) +
+   geom_line() + geom_ribbon(alpha = 0.4)
+ 
+ # Plotting marginal effect of a spline ---------------------------------
+ 
+ m_gam <- sdmTMB(
+  data = d, formula = density ~ 0 + as.factor(year) + s(depth_scaled, k = 5),
+  time = "year", mesh = mesh, family = tweedie(link = "log")
+ )
+ if (require("visreg", quietly = TRUE)) {
+   visreg::visreg(m_gam, "depth_scaled")
+ }
+ 
+ # or manually:
+ nd <- data.frame(depth_scaled =
+   seq(min(d$depth_scaled), max(d$depth_scaled), length.out = 100))
+ nd$year <- 2011L
+ p <- predict(m_gam, newdata = nd, se_fit = TRUE, re_form = NA)
+ ggplot(p, aes(depth_scaled, exp(est),
+   ymin = exp(est - 1.96 * est_se), ymax = exp(est + 1.96 * est_se))) +
+   geom_line() + geom_ribbon(alpha = 0.4)
+ 
+ # Forecasting ----------------------------------------------------------
+ mesh <- make_mesh(d, c("X", "Y"), cutoff = 15)
+ 
+ unique(d$year)
+ m <- sdmTMB(
+   data = d, formula = density ~ 1,
+   spatiotemporal = "AR1", # using AR(1) to have something to forecast with
+   extra_time = 2019L, # `L` for integer to match our data
+   spatial = "off",
+   time = "year", mesh = mesh, family = tweedie(link = "log")
+ )
+ 
+ # Add a year to our grid:
+ grid2019 <- qcs_grid_2011[qcs_grid_2011$year == max(qcs_grid_2011$year), ]
+ grid2019$year <- 2019L # `L` because `year` is an integer in the data
+ qcsgrid_forecast <- rbind(qcs_grid_2011, grid2019)
+ 
+ predictions <- predict(m, newdata = qcsgrid_forecast)
+ plot_map(predictions, exp(est)) +
+   scale_fill_viridis_c(trans = "log10")
+ plot_map(predictions, epsilon_st) +
+   scale_fill_gradient2()
+ 
+ # Estimating local trends ----------------------------------------------
+ 
+ d <- pcod
+ d$year_scaled <- as.numeric(scale(d$year))
+ mesh <- make_mesh(pcod, c("X", "Y"), cutoff = 25)
+ m <- sdmTMB(data = d, formula = density ~ depth_scaled + depth_scaled2,
+   mesh = mesh, family = tweedie(link = "log"),
+   spatial_varying = ~ 0 + year_scaled, time = "year", spatiotemporal = "off")
+ nd <- replicate_df(qcs_grid, "year", unique(pcod$year))
+ nd$year_scaled <- (nd$year - mean(d$year)) / sd(d$year)
+ p <- predict(m, newdata = nd)
+ 
+ plot_map(subset(p, year == 2003), zeta_s_year_scaled) + # pick any year
+   ggtitle("Spatial slopes") +
+   scale_fill_gradient2()
+ 
+ plot_map(p, est_rf) +
+   ggtitle("Random field estimates") +
+   scale_fill_gradient2()
+ 
+ plot_map(p, exp(est_non_rf)) +
+   ggtitle("Prediction (fixed effects only)") +
+   scale_fill_viridis_c(trans = "sqrt")
+ 
+ plot_map(p, exp(est)) +
+   ggtitle("Prediction (fixed effects + all random effects)") +
+   scale_fill_viridis_c(trans = "sqrt")
+ ## End(No test)
+ ## Don't show: 
+ }) # examplesIf
> d <- pcod_2011
> mesh <- make_mesh(d, c("X", "Y"), cutoff = 30)
> m <- sdmTMB(data = d, formula = density ~ 0 + as.factor(year) + depth_scaled + 
+     depth_scaled2, time = "year", mesh = mesh, family = tweedie(link = "log"))
> predictions <- predict(m)
> head(predictions)
# A tibble: 6 × 17
   year     X     Y depth density present   lat   lon depth_mean depth_sd
  <int> <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl> <dbl>      <dbl>    <dbl>
1  2011  435. 5718.   241    245.       1  51.6 -130.       5.16    0.445
2  2011  487. 5719.    52      0        0  51.6 -129.       5.16    0.445
3  2011  490. 5717.    47      0        0  51.6 -129.       5.16    0.445
4  2011  545. 5717.   157      0        0  51.6 -128.       5.16    0.445
5  2011  404. 5720.   398      0        0  51.6 -130.       5.16    0.445
6  2011  420. 5721.   486      0        0  51.6 -130.       5.16    0.445
# ℹ 7 more variables: depth_scaled <dbl>, depth_scaled2 <dbl>, est <dbl>,
#   est_non_rf <dbl>, est_rf <dbl>, omega_s <dbl>, epsilon_st <dbl>
> predictions$resids <- residuals(m)
> library(ggplot2)
> ggplot(predictions, aes(X, Y, col = resids)) + scale_colour_gradient2() + 
+     geom_point() + facet_wrap(~year)
> hist(predictions$resids)
> qqnorm(predictions$resids)
> abline(a = 0, b = 1)
> qcs_grid_2011 <- replicate_df(qcs_grid, "year", unique(pcod_2011$year))
> predictions <- predict(m, newdata = qcs_grid_2011)
> plot_map <- function(dat, column = est) {
+     ggplot(dat, aes(X, Y, fill = {
+         {
+             column
+         }
+     })) + geom_raster() + facet_wrap(~year) + coord_fixed()
+ }
> plot_map(predictions, exp(est)) + scale_fill_viridis_c(trans = "sqrt") + 
+     ggtitle("Prediction (fixed effects + all random effects)")
> plot_map(predictions, exp(est_non_rf)) + ggtitle("Prediction (fixed effects and any time-varying effects)") + 
+     scale_fill_viridis_c(trans = "sqrt")
> plot_map(predictions, est_rf) + ggtitle("All random field estimates") + 
+     scale_fill_gradient2()
> plot_map(predictions, omega_s) + ggtitle("Spatial random effects only") + 
+     scale_fill_gradient2()
> plot_map(predictions, epsilon_st) + ggtitle("Spatiotemporal random effects only") + 
+     scale_fill_gradient2()
> nd <- data.frame(depth_scaled = seq(min(d$depth_scaled), max(d$depth_scaled), 
+     length.out = 100))
> nd$depth_scaled2 <- nd$depth_scaled^2
> nd$year <- 2011L
> p <- predict(m, newdata = nd, se_fit = TRUE, re_form = NA)
> ggplot(p, aes(depth_scaled, exp(est), ymin = exp(est - 1.96 * est_se), 
+     ymax = exp(est + 1.96 * est_se))) + geom_line() + geom_ribbon(alpha = 0.4)
> m_gam <- sdmTMB(data = d, formula = density ~ 0 + as.factor(year) + s(depth_scaled, 
+     k = 5), time = "year", mesh = mesh, family = tweedie(link = "log"))
> if (require("visreg", quietly = TRUE)) {
+     visreg::visreg(m_gam, "depth_scaled")
+ }
visreg 3.0 includes breaking changes. For migration details, see:
https://pbreheny.github.io/visreg/articles/migrating-to-3-0.html
Error in `predict()`:
! `xy_cols` (the column names for the x and y coordinates) are not in
  `newdata`.
Did you miss specifying the argument `xy_cols` to match your data?
The newer `make_mesh()` (vs. `make_spde()`) takes care of this for you.
Backtrace:
     ▆
  1. ├─base::withAutoprint(...)
  2. │ └─base::source(...)
  3. │   ├─base::withVisible(eval(ei, envir))
  4. │   └─base::eval(ei, envir)
  5. │     └─base::eval(ei, envir)
  6. └─visreg::visreg(m_gam, "depth_scaled")
  7.   └─visreg:::build_visreg(...)
  8.     └─visreg:::get_xy(...)
  9.       └─visreg:::compute_response(fit, x, trans, alpha, predict)
 10.         └─visreg:::visreg_pred(fit, x$frame_res, predict = predict)
 11.           └─visreg (local) call_fn(fit, args, se_fit)
 12.             ├─base::suppressWarnings(do.call("predict", args))
 13.             │ └─base::withCallingHandlers(...)
 14.             ├─base::do.call("predict", args)
 15.             ├─stats::predict(object = `<sdmTMB>`, newdata = `<df[,3]>`)
 16.             └─sdmTMB:::predict.sdmTMB(object = `<sdmTMB>`, newdata = `<df[,3]>`)
 17.               └─cli::cli_abort(...)
 18.                 └─rlang::abort(...)
Execution halted
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’ [125s/108s]
 [125s/109s] OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [8s/12s] OK
* checking PDF version of manual ... [6s/13s] OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* checking for new files in some other directories ... OK
* DONE

Status: 1 ERROR, 1 NOTE
See
  ‘/data/localhost/ripley/R/packages/tests-donttest/sdmTMB.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 22:07.61, 671.53 + 48.29