r <- residuals(m3, "mle-mcmc", mcmc_samples = samps) @@ -604,7 +604,10 @@ Time-varying effects) #> Detected irregular time spacing with an AR(1) or random walk process. #> Consider filling in the missing time slices with `extra_time`. -#> `extra_time = c(2006, 2008, 2010, 2012, 2014, 2016)`
m4 #> Spatiotemporal model fit by ML ['sdmTMB'] @@ -613,6 +616,40 @@ Time-varying effects#> Time column: year #> Data: pcod #> Family: tweedie(link = 'log') +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced +#> Warning in sqrt(diag(object$cov.fixed)): NaNs produced #> #> coef.est coef.se #> as.factor(year)2003 3.36 0.30 @@ -653,7 +690,9 @@ Time-varying effects#> Spatiotemporal IID SD: 1.66 #> ML criterion at convergence: 6251.146 #> -#> See ?tidy.sdmTMB to extract these values as a data frame.
AIC(m4) #> [1] 12534.29
mcmc_res <- residuals(fit_nb2, type = "mle-mcmc", mcmc_samples = samps) @@ -507,8 +507,8 @@ The need for one-sample residuals#> #> SAMPLING FOR MODEL 'tmb_generic' NOW (CHAIN 1). #> Chain 1: -#> Chain 1: Gradient evaluation took 0.00212 seconds -#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 21.2 seconds. +#> Chain 1: Gradient evaluation took 0.002356 seconds +#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.56 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: @@ -525,9 +525,9 @@ The need for one-sample residuals#> Chain 1: Iteration: 360 / 400 [ 90%] (Sampling) #> Chain 1: Iteration: 400 / 400 [100%] (Sampling) #> Chain 1: -#> Chain 1: Elapsed Time: 30.746 seconds (Warm-up) -#> Chain 1: 30.135 seconds (Sampling) -#> Chain 1: 60.881 seconds (Total) +#> Chain 1: Elapsed Time: 30.581 seconds (Warm-up) +#> Chain 1: 29.989 seconds (Sampling) +#> Chain 1: 60.57 seconds (Total) #> Chain 1: #> Warning: The largest R-hat is 1.08, indicating chains have not mixed. #> Running the chains for more iterations may help. See diff --git a/articles/web_only/spatial-trend-models.html b/articles/web_only/spatial-trend-models.html index 3aad73812..8a94e0f67 100644 --- a/articles/web_only/spatial-trend-models.html +++ b/articles/web_only/spatial-trend-models.html @@ -26,7 +26,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 diff --git a/articles/web_only/threshold-models.html b/articles/web_only/threshold-models.html index 30b775a1b..fbfaa122b 100644 --- a/articles/web_only/threshold-models.html +++ b/articles/web_only/threshold-models.html @@ -26,7 +26,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 diff --git a/articles/web_only/visreg.html b/articles/web_only/visreg.html index 3480051eb..384b98166 100644 --- a/articles/web_only/visreg.html +++ b/articles/web_only/visreg.html @@ -26,7 +26,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 diff --git a/authors.html b/authors.html index 5af37a322..5d81628c8 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 diff --git a/header.html b/header.html index dc6bfed7e..334688e20 100644 --- a/header.html +++ b/header.html @@ -10,7 +10,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 diff --git a/index.html b/index.html index ae11ebfa4..3b4032fad 100644 --- a/index.html +++ b/index.html @@ -26,7 +26,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 @@ -269,7 +269,7 @@ Basic useggeffects::ggpredict(fit, "depth [50:400, by=2]") |> plot()
If the depth effect was parametric and not a penalized smoother, we could have alternatively used ggeffects::ggeffect() for a fast marginal effect plot.
ggeffects::ggeffect()
Predict on new data:
Next, we can predict on new data. We will use a data frame qcs_grid from the package, which contains all the locations (and covariates) at which we wish to predict. Here, these newdata are a grid, or raster, covering our survey.
qcs_grid
newdata
p <- predict(fit, newdata = qcs_grid)
diff --git a/news/index.html b/news/index.html index fe6e4724f..fde678870 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ sdmTMB - 0.6.0 + 0.6.0.9000 @@ -62,6 +62,9 @@ Source: NEWS.md
NEWS.md
CRAN release: 2024-05-29
Pass several arguments to DHARMa::plotQQunif().
DHARMa::plotQQunif()