From d7736b92edf2b4dba485a4d2ce1812f289878a8c Mon Sep 17 00:00:00 2001 From: seananderson Date: Mon, 26 Aug 2024 19:38:23 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20pbs-asse?= =?UTF-8?q?ss/sdmTMB@075440b2d31bf82aadac67770d428f43e4821dea=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/basic-intro.html | 52 +++--------------- .../figure-html/plot-cv-1.png | Bin 47613 -> 46363 bytes .../figure-html/residuals-mcmc-1.png | Bin 44612 -> 44067 bytes .../figure-html/tv-depth-eff-1.png | Bin 155994 -> 160606 bytes articles/bayesian.html | 2 +- .../figure-html/unnamed-chunk-9-1.png | Bin 244050 -> 248409 bytes articles/delta-models.html | 6 +- .../delta-models_files/figure-html/cv-1.png | Bin 49037 -> 48768 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 29374 -> 28534 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 52963 -> 52328 bytes articles/residual-checking.html | 20 +++---- .../figure-html/unnamed-chunk-13-1.png | Bin 57575 -> 57652 bytes index.html | 2 +- news/index.html | 2 +- pkgdown.yml | 2 +- reference/dharma_residuals.html | 2 +- reference/get_index_sims-1.png | Bin 69831 -> 70874 bytes reference/predict.sdmTMB.html | 2 +- reference/sdmTMB.html | 4 +- search.json | 2 +- 20 files changed, 30 insertions(+), 66 deletions(-) diff --git a/articles/basic-intro.html b/articles/basic-intro.html index b43a2f4d7..04b8db6bf 100644 --- a/articles/basic-intro.html +++ b/articles/basic-intro.html @@ -391,8 +391,8 @@

Model diagnostics#> #> SAMPLING FOR MODEL 'tmb_generic' NOW (CHAIN 1). #> Chain 1: -#> Chain 1: Gradient evaluation took 0.01007 seconds -#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 100.7 seconds. +#> Chain 1: Gradient evaluation took 0.012596 seconds +#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 125.96 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: @@ -417,9 +417,9 @@

Model diagnostics#> Chain 1: Iteration: 100 / 101 [ 99%] (Warmup) #> Chain 1: Iteration: 101 / 101 [100%] (Sampling) #> Chain 1: -#> Chain 1: Elapsed Time: 26.202 seconds (Warm-up) -#> Chain 1: 0.198 seconds (Sampling) -#> Chain 1: 26.4 seconds (Total) +#> Chain 1: Elapsed Time: 27.083 seconds (Warm-up) +#> Chain 1: 0.197 seconds (Sampling) +#> Chain 1: 27.28 seconds (Total) #> Chain 1: r <- residuals(m3, "mle-mcmc", mcmc_samples = samps) qqnorm(r) @@ -586,9 +586,6 @@

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)` -#> Warning in sqrt(diag(cov)): NaNs produced -#> Warning: The model may not have converged: non-positive-definite Hessian -#> matrix. m4 #> Spatiotemporal model fit by ML ['sdmTMB'] #> Formula: density ~ 0 + as.factor(year) @@ -596,40 +593,6 @@

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 @@ -668,13 +631,13 @@

Time-varying effects#> Matérn range: 13.58 #> Spatial SD: 1.63 #> Spatiotemporal IID SD: 1.66 -#> ML criterion at convergence: 6251.146 +#> ML criterion at convergence: 6251.245 #> #> See ?tidy.sdmTMB to extract these values as a data frame. #> #> **Possible issues detected! Check output of sanity().** AIC(m4) -#> [1] 12534.29 +#> [1] 12534.49

To plot these, we make a data frame that contains all combinations of the time-varying covariate and time. This is easily created using expand.grid() or tidyr::expand_grid().

@@ -689,6 +652,7 @@

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binomial(link = "logit")) and then a model for the positive catches only with a lognormal observation distribution and a log link (i.e., lognormal(link = "log")

-
  • Poisson-link delta-Gamma or delta-lognormal. See the Poisson-link +

  • Poisson-link delta-Gamma or delta-lognormal. See the Poisson-link delta model vignette.

  • Delta-truncated-negative-binomial: family = delta_truncated_nbinom1() or @@ -502,8 +502,8 @@

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zTdf9cbLgRi(w@sF8?bIP$?tg5yK;9w0dgS#E3B>aDR7mRh^CTMQhz|%?vdq+DhNhE z@CEBKh6vh067-~5hycCIXI+N``dQ?`2faY;cG8Es0C9E^v~X?{7Xe{*9MGjV2TCC~ zI%viq#vx5}@2yUsa(*ue-8RC^`8e^wZW0)$Qnmuc$o3rP&h3_0p_NRGjFA6c2V|z% zBsDq)40s0y;$MSzjh&X;E6?-;#0(pWbW%0io{w|^i*ZfgSAYUWQZ0!2e&~sRcK? zi^fnDhaLb}UL(w+AAk{@@mX>8Nd*RW zmO#pKeLk{60JA>rT9E-Y3?|H>E%(vGDur*OdpMz1E>92?eXU?WyND^Bt7co8tzzQ5 zy%XYEc1{2NeFCB!IH>E9J=YG-KuvYVL9W?_N4s4wC zip@NCclQvt#;&d?=#DK#ouTdriMA!k=?gw?jDzLM=$?vx@Lh-6z+=5v4pzrq{R0*K z&G6*MkN3#V*K!ee?O7pP4^>M}=!Vy-Ap+KwU3qlSlT1WF;?=ES@v|l`uTSQ50=qOJ z%sL>EV<9UwxpTV_ruq{UE2}>PLD{hsuyw=(=M1RA4fcZ!u$^XT^4-8fwR+&YtO0r; z5Q}yZq#s7UzYhGg#0tKxd<`^AyK9Z(NJ^}23s?;iEWLv_XFS`yaAB z@3=wQ2Zrp@4-Dk}9|C2@%0KFf`adLDx{m(_z4OCMkgMCMC-based randomized-quantile #> #> SAMPLING FOR MODEL 'tmb_generic' NOW (CHAIN 1). #> Chain 1: -#> Chain 1: Gradient evaluation took 0.000981 seconds -#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.81 seconds. +#> Chain 1: Gradient evaluation took 0.000976 seconds +#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.76 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: @@ -232,9 +232,9 @@

    MCMC-based randomized-quantile #> Chain 1: Iteration: 720 / 800 [ 90%] (Sampling) #> Chain 1: Iteration: 800 / 800 [100%] (Sampling) #> Chain 1: -#> Chain 1: Elapsed Time: 6.685 seconds (Warm-up) -#> Chain 1: 4.754 seconds (Sampling) -#> Chain 1: 11.439 seconds (Total) +#> Chain 1: Elapsed Time: 6.704 seconds (Warm-up) +#> Chain 1: 4.757 seconds (Sampling) +#> Chain 1: 11.461 seconds (Total) #> Chain 1: mcmc_res <- residuals(fit_nb2, type = "mle-mcmc", mcmc_samples = samps) qqnorm(mcmc_res) @@ -488,8 +488,8 @@

    The need for one-sample residuals#> #> SAMPLING FOR MODEL 'tmb_generic' NOW (CHAIN 1). #> Chain 1: -#> Chain 1: Gradient evaluation took 0.002089 seconds -#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 20.89 seconds. +#> Chain 1: Gradient evaluation took 0.002304 seconds +#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.04 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: @@ -506,9 +506,9 @@

    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: 31.437 seconds (Warm-up) -#> Chain 1: 30.836 seconds (Sampling) -#> Chain 1: 62.273 seconds (Total) +#> Chain 1: Elapsed Time: 30.809 seconds (Warm-up) +#> Chain 1: 30.196 seconds (Sampling) +#> Chain 1: 61.005 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. 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    Priors

    Bayesian MCMC sampling with Stan

    -

    The fitted model can be passed to the tmbstan package to sample from the posterior with Stan. See the Bayesian vignette.

    +

    The fitted model can be passed to the tmbstan package to sample from the posterior with Stan. See the Bayesian vignette.

    Turning off random fields diff --git a/news/index.html b/news/index.html index cb8403905..20a8ca157 100644 --- a/news/index.html +++ b/news/index.html @@ -77,7 +77,7 @@

    sdmTMB 0.6.0

    sdmTMB 0.5.0

    CRAN release: 2024-04-03

    -