From 82e54cfd708dd4ac43075cc6854812b44f1542d7 Mon Sep 17 00:00:00 2001 From: Eric Ward <5046884+ericward-noaa@users.noreply.github.com> Date: Mon, 19 Aug 2024 08:36:13 -0700 Subject: [PATCH 01/15] Multispecies / multiresponse vignette I talked to Lewis last week about this. Currently includes examples of: - main effects by species - spatial fields by species, with and without shared variance - spatiotemporal fields by species --- vignettes/web_only/multispecies.Rmd | 247 ++++++++++++++++++++++++++++ 1 file changed, 247 insertions(+) create mode 100644 vignettes/web_only/multispecies.Rmd diff --git a/vignettes/web_only/multispecies.Rmd b/vignettes/web_only/multispecies.Rmd new file mode 100644 index 000000000..10bf8c21e --- /dev/null +++ b/vignettes/web_only/multispecies.Rmd @@ -0,0 +1,247 @@ +--- +title: "Fitting multispecies models with sdmTMB" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Fitting multispecies models with sdmTMB} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +**If the code in this vignette has not been evaluated, a rendered version is available on the [documentation site](https://pbs-assess.github.io/sdmTMB/index.html) under 'Articles'.** + +```{r setup, include = FALSE, cache=FALSE} +dplyr_installed <- require("dplyr", quietly = TRUE) +ggplot_installed <- require("ggplot2", quietly = TRUE) +pkgs <- dplyr_installed && ggplot_installed +EVAL <- identical(Sys.getenv("NOT_CRAN"), "true") && pkgs +knitr::opts_chunk$set( + collapse = TRUE, + warning=FALSE, + message=FALSE, + comment = "#>", + fig.width = 7, + fig.asp = 0.618, + eval = EVAL, + purl = EVAL +) +``` + +```{r packages, message=FALSE, warning=TRUE} +library(ggplot2) +library(dplyr) +library(sdmTMB) +``` + +For some applications, we might be interested in fitting a model that includes multiple responses (such as 2+ species). The most important step in fitting these models is understanding which parameters are shared, and which parameters are species specific. + +Below, we illustrate a series of models that may be used to answer different questions. We'll start by simulating a 2-species dataset. Each species is allowed to have unique spatial ranges and variances, as well as different year effects. + +```{r sim_dat} + set.seed(123) + + # make fake predictor(s) (a1) and sampling locations: + predictor_dat <- data.frame( + X = runif(500), Y = runif(500), + year = rep(1:5, each = 100) + ) + predictor_dat$fyear <- as.factor(predictor_dat$year) + mesh <- make_mesh(predictor_dat, xy_cols = c("X", "Y"), cutoff = 0.1) + + sim_dat_A <- sdmTMB_simulate( + formula = ~ 0 + fyear, + data = predictor_dat, + time = "year", + mesh = mesh, + range = 0.5, + family = tweedie(), + seed = 42, + sigma_O = 0.2, + phi = 0.1, + sigma_E = 0.1, + B = runif(5, min = 5, max = 8) # 5 random year effects + ) + sim_dat_A$species <- "A" + + sim_dat_B <- sdmTMB_simulate( + formula = ~ 0 + fyear, + data = predictor_dat, + time = "year", + mesh = mesh, + range = 0.2, + family = gaussian(), + seed = 42, + sigma_O = 0.3, + phi = 0.1, + sigma_E = 0.1, + B = runif(5, min = 5, max = 8) # 5 random year effects + ) + sim_dat_B$species <- "B" + + sim_dat <- rbind(sim_dat_A, sim_dat_B) + sim_dat$fyear <- as.factor(sim_dat$year) +``` + +We'll start by making the mesh across the full dataset + +```{r mesh_fig, fig.asp=1} +spde <- make_mesh(sim_dat, c("X", "Y"), cutoff = 0.1) +plot(spde) +``` + +### Model 1: species specific intercepts + +As a first model, we can include species specific year effects. This can be done in a couple ways -- one option would be to estimate the `species*year` interaction, letting the year effects for each species be independent. All other parameters (range, spatial field, spatial variance, spatiotemporal fields, spatiotemporal variances) are shared. + +```{r} +fit <- sdmTMB( + formula = observed ~ fyear*species, + data = sim_dat, + time = "year", + spatiotemporal = "iid", + mesh = spde, family = gaussian()) +``` + +This model doesn't appear to converge, and the results from `sanity(fit)` indicate problems with the spatial and spatiotemporal variances. This is likely a sign that our assumptions in the estimation model are wrong for this particular dataset (not surprising, as our simulated data has different parameters by species). + +Our simulated dataset only has 5 years of data -- if we had a longer time series, an alternative formulation for our year effects could be to fit smooth terms, like in a GAM with `mgcv`. + +```{r eval=FALSE} +fit <- sdmTMB( + formula = observed ~ s(year,by="species"), + data = sim_dat, + time = "year", + spatiotemporal = "iid", + mesh = spde, family = gaussian()) +``` + +### Model 2: species specific fields + +We may be interested in fitting a model that lets the spatial patterning differ by species. These kinds of models can be expressed using spatially varying coefficients. Note that we use `spatial = off` because this represents a global spatial intercept -- turning this off is equivalent to using a `-1` of `0` in a main formula including a factor. + +```{r} +fit <- sdmTMB( + observed ~ 0 + fyear, # shared year effects + data = sim_dat, + mesh = spde, + family = gaussian(), + spatial = "off", + time = "year", + spatiotemporal = "iid", + spatial_varying = ~ 0 + as.factor(species) +) +``` + +This model also appears to be problematic. If we look at the estimated parameters, you'll notice that there are two row entries for `sigma_Z`. This means that our model is trying to estimate separate species specific variance terms for the species specific spatial fields. + +```{r} +fit$sd_report +``` + +If we wanted to estimate species specific spatial fields with a single shared variance (meaning the magnitude of the peaks and valleys in the field was similar), we could do that by specifying a custom map argument and passing it into `sdmTMBcontrol()` + +```{r} +map_list = list(ln_tau_Z = factor(c(1, 1))) + +fit <- sdmTMB( + observed ~ 0 + fyear, # shared year effects + data = sim_dat, + mesh = spde, + family = gaussian(), + spatial = "off", + time = "year", + spatiotemporal = "iid", + spatial_varying = ~ 0 + as.factor(species), + control = sdmTMBcontrol(map = map_list) +) +``` + +### Model 3: species specific spatiotemporal fields + +As a last example, we can set up a model that also includes spatiotemporal fields unique to each species. In all of the examples above, spatiotemporal fields are included -- but shared among species. + +One approach to including spatiotemporal fields is creating a new variable combining species and year. We can then include this spatiotemporal variation by changing the `time` argument to be `time = "species_year"` + +```{r} +# This needs to be a factor +sim_dat$species_year <- as.factor(paste(sim_dat$species, sim_dat$year)) + +map_list = list(ln_tau_Z = factor(c(1, 1))) + +fit <- sdmTMB( + observed ~ 0 + fyear, # shared year effects + data = sim_dat, + mesh = spde, + family = gaussian(), + spatial = "off", + time = "species_year", + spatiotemporal = "iid", + spatial_varying = ~ 0 + as.factor(species), + control = sdmTMBcontrol(map = map_list) +) + +``` + +This is getting a little better -- there is a gradient warning, but the Hessian appears to be ok. + +### Putting it all together + +Our spatiotemporal model above came close to passing all sanity() checks, and could be modified to also include the species-specific year effects that we started with + +```{r} +fit <- sdmTMB( + observed ~ 0 + fyear*species, # shared year effects + data = sim_dat, + mesh = spde, + family = gaussian(), + spatial = "off", + time = "species_year", + spatiotemporal = "iid", + spatial_varying = ~ 0 + as.factor(species), + control = sdmTMBcontrol(map = map_list) +) +``` + +These examples illustrate a number of ways that species - specific effects can be included in `sdmTMB` models, and can be extended to other types of groupings. A brief summary of these can be summarized as: + +```{r echo=FALSE} +desc <- data.frame("Form" = c("Main effects", "Spatial effects", "Spatial effects w/shared variance", "Spatiotemporal effects"), "Implementation" = c("Year by species interactions, or smooths", "Spatially varying coefficients", "Spatially varying coefficients + map argument", "Species-year factor as time variable")) +knitr::kable(desc) +``` + +### Extensions + +Including spatiotemporal effects like species-year combinations in the `time` argument is relatively straightforward, but these models could get more complicated if we wanted to also include other grouping factors (such as cohort effects for each species). There may be some identifiability issues that would have to be resolved, but even if the `time` argument is being used, it's possible to add other factors through the `spatial_varying` formula. As an example, if our dataframe included a cohort variable for each species, we could first create a new combination + +```{r eval=FALSE} +sim_dat$species_cohort <- as.factor(paste(sim_dat$species, sim_dat$cohort)) +``` + +If there were ~ 10 levels of `species_cohort`, and we were including our original spatial effects in `spatial_varying`, we could create a new map list that would indicate we wanted all species-cohort fields to have the same spatial variance, + +```{r eval=FALSE} +sim_dat$species_year <- as.factor(paste(sim_dat$species, sim_dat$year)) + +# The first 2 elements correspond to species, remaining 10 to species-cohort +map_list = list(ln_tau_Z = factor(c(1, 1, rep(2, 10)))) + +fit <- sdmTMB( + observed ~ 0 + fyear*species, + data = sim_dat, + mesh = spde, + family = gaussian(), + spatial = "off", + time = "species_year", + spatiotemporal = "iid", + spatial_varying = ~ 0 + as.factor(species) + species_cohort, + control = sdmTMBcontrol(map = map_list) +) +``` + + + + + + + + From a677156a7f491e7407fdfe6916c10f25b8142c73 Mon Sep 17 00:00:00 2001 From: Lewis Barnett Date: Fri, 22 Nov 2024 12:46:29 -0800 Subject: [PATCH 02/15] edit and show more of output --- vignettes/web_only/multispecies.Rmd | 82 +++++++++++++++++------------ 1 file changed, 48 insertions(+), 34 deletions(-) diff --git a/vignettes/web_only/multispecies.Rmd b/vignettes/web_only/multispecies.Rmd index 10bf8c21e..7da13f0cd 100644 --- a/vignettes/web_only/multispecies.Rmd +++ b/vignettes/web_only/multispecies.Rmd @@ -10,7 +10,7 @@ vignette: > **If the code in this vignette has not been evaluated, a rendered version is available on the [documentation site](https://pbs-assess.github.io/sdmTMB/index.html) under 'Articles'.** -```{r setup, include = FALSE, cache=FALSE} +```{r setup, include = FALSE, cache = FALSE} dplyr_installed <- require("dplyr", quietly = TRUE) ggplot_installed <- require("ggplot2", quietly = TRUE) pkgs <- dplyr_installed && ggplot_installed @@ -27,15 +27,15 @@ knitr::opts_chunk$set( ) ``` -```{r packages, message=FALSE, warning=TRUE} +```{r packages, message = FALSE, warning = TRUE} library(ggplot2) library(dplyr) library(sdmTMB) ``` -For some applications, we might be interested in fitting a model that includes multiple responses (such as 2+ species). The most important step in fitting these models is understanding which parameters are shared, and which parameters are species specific. +For some applications, we might be interested in fitting a model that includes multiple responses (such as 2+ species, or multiple size or age classes within a species). The most important step in fitting these models is understanding which parameters are shared, and which parameters are species-specific. -Below, we illustrate a series of models that may be used to answer different questions. We'll start by simulating a 2-species dataset. Each species is allowed to have unique spatial ranges and variances, as well as different year effects. +Below, we illustrate a series of models that may be used to answer a few different common questions. We'll start by simulating a 2-species dataset. Each species is allowed to have unique spatial ranges and variances, as well as different year effects. ```{r sim_dat} set.seed(123) @@ -82,42 +82,50 @@ Below, we illustrate a series of models that may be used to answer different que sim_dat$fyear <- as.factor(sim_dat$year) ``` -We'll start by making the mesh across the full dataset +We'll start by making the mesh across the full dataset. ```{r mesh_fig, fig.asp=1} spde <- make_mesh(sim_dat, c("X", "Y"), cutoff = 0.1) plot(spde) ``` -### Model 1: species specific intercepts +### Model 1: species-specific intercepts -As a first model, we can include species specific year effects. This can be done in a couple ways -- one option would be to estimate the `species*year` interaction, letting the year effects for each species be independent. All other parameters (range, spatial field, spatial variance, spatiotemporal fields, spatiotemporal variances) are shared. +As a first model, we can include species-specific year effects. This can be done in a couple ways -- one option would be to estimate the `species*year` interaction, letting the year effects for each species be independent. All other parameters (range, spatial field, spatial variance, spatiotemporal fields, spatiotemporal variances) are shared. ```{r} fit <- sdmTMB( - formula = observed ~ fyear*species, + formula = observed ~ fyear * species, data = sim_dat, time = "year", spatiotemporal = "iid", - mesh = spde, family = gaussian()) + mesh = spde, + family = gaussian() + ) + +fit + +print(sanity(fit)) ``` This model doesn't appear to converge, and the results from `sanity(fit)` indicate problems with the spatial and spatiotemporal variances. This is likely a sign that our assumptions in the estimation model are wrong for this particular dataset (not surprising, as our simulated data has different parameters by species). -Our simulated dataset only has 5 years of data -- if we had a longer time series, an alternative formulation for our year effects could be to fit smooth terms, like in a GAM with `mgcv`. +Our simulated dataset only has 5 years of data -- if we had a longer time series, an alternative formulation for our year effects could be to fit smooth terms, like in a GAM with `mgcv`. ```{r eval=FALSE} fit <- sdmTMB( - formula = observed ~ s(year,by="species"), + formula = observed ~ s(year, by = "species"), data = sim_dat, time = "year", spatiotemporal = "iid", - mesh = spde, family = gaussian()) + mesh = spde, + family = gaussian() + ) ``` -### Model 2: species specific fields +### Model 2: species-specific fields -We may be interested in fitting a model that lets the spatial patterning differ by species. These kinds of models can be expressed using spatially varying coefficients. Note that we use `spatial = off` because this represents a global spatial intercept -- turning this off is equivalent to using a `-1` of `0` in a main formula including a factor. +We may be interested in fitting a model that lets the spatial patterning differ by species. These kinds of models can be expressed using spatially varying coefficients. Note that we use `spatial = off` because this represents a global spatial intercept -- turning this off is akin to using a `-1` of `0` in a main formula including a factor. ```{r} fit <- sdmTMB( @@ -130,15 +138,17 @@ fit <- sdmTMB( spatiotemporal = "iid", spatial_varying = ~ 0 + as.factor(species) ) + +fit ``` -This model also appears to be problematic. If we look at the estimated parameters, you'll notice that there are two row entries for `sigma_Z`. This means that our model is trying to estimate separate species specific variance terms for the species specific spatial fields. +This model also appears to be problematic. If we look at the estimated parameters, you'll notice that there are two rows of entries for `sigma_Z`. This means that our model is trying to estimate separate species-specific variance terms for the species-specific spatial fields (say *that* 10 times fast!). ```{r} fit$sd_report ``` -If we wanted to estimate species specific spatial fields with a single shared variance (meaning the magnitude of the peaks and valleys in the field was similar), we could do that by specifying a custom map argument and passing it into `sdmTMBcontrol()` +If we wanted to estimate species-specific spatial fields with a single shared variance (meaning the net magnitude of the peaks and valleys in the fields were similar), we could do that by specifying a custom map argument and passing it into `sdmTMBcontrol()` ```{r} map_list = list(ln_tau_Z = factor(c(1, 1))) @@ -154,13 +164,15 @@ fit <- sdmTMB( spatial_varying = ~ 0 + as.factor(species), control = sdmTMBcontrol(map = map_list) ) + +fit ``` -### Model 3: species specific spatiotemporal fields +### Model 3: species-specific spatiotemporal fields -As a last example, we can set up a model that also includes spatiotemporal fields unique to each species. In all of the examples above, spatiotemporal fields are included -- but shared among species. +In all of the examples above, spatiotemporal fields are included -- but shared among species. As a last example, we can extend the above approaches to set up a model that includes spatiotemporal fields unique to each species. -One approach to including spatiotemporal fields is creating a new variable combining species and year. We can then include this spatiotemporal variation by changing the `time` argument to be `time = "species_year"` +One approach to including separate spatiotemporal fields by species is creating a new variable that is a concatenation of species and year (or any given time step factor). For example, we can then implement this form of species-specific spatiotemporal variation by changing the `time` argument to be `time = "species_year"` ```{r} # This needs to be a factor @@ -180,9 +192,12 @@ fit <- sdmTMB( control = sdmTMBcontrol(map = map_list) ) +fit + +print(sanity(fit)) ``` -This is getting a little better -- there is a gradient warning, but the Hessian appears to be ok. +This is getting a little better -- there is a gradient warning, but the Hessian appears to be invertible (`hessian_ok == TRUE)`, which is critical to convergence. ### Putting it all together @@ -200,33 +215,40 @@ fit <- sdmTMB( spatial_varying = ~ 0 + as.factor(species), control = sdmTMBcontrol(map = map_list) ) + + +fit + +print(sanity(fit)) + +fit$sd_report ``` -These examples illustrate a number of ways that species - specific effects can be included in `sdmTMB` models, and can be extended to other types of groupings. A brief summary of these can be summarized as: +These examples illustrate a number of ways that species-specific effects can be included in `sdmTMB` models, and can be extended to other categories/groups/cohorts within a species for which one wants to control the amount of information shared between groups (e.g., age-, size-, or stage-specific estimates). A brief summary of these approaches can be summarized as: ```{r echo=FALSE} desc <- data.frame("Form" = c("Main effects", "Spatial effects", "Spatial effects w/shared variance", "Spatiotemporal effects"), "Implementation" = c("Year by species interactions, or smooths", "Spatially varying coefficients", "Spatially varying coefficients + map argument", "Species-year factor as time variable")) knitr::kable(desc) ``` -### Extensions +### Further extensions -Including spatiotemporal effects like species-year combinations in the `time` argument is relatively straightforward, but these models could get more complicated if we wanted to also include other grouping factors (such as cohort effects for each species). There may be some identifiability issues that would have to be resolved, but even if the `time` argument is being used, it's possible to add other factors through the `spatial_varying` formula. As an example, if our dataframe included a cohort variable for each species, we could first create a new combination +Including spatiotemporal effects like species-year combinations in the `time` argument is relatively straightforward, but these models could get more complicated if we wanted to also include other grouping factors (such as cohort effects for each species). There may be some identifiability issues that would have to be resolved, but even if the `time` argument is being used, it's possible to add other factors through the `spatial_varying` formula. As an example, if our dataframe included a cohort variable for each species, we could first create a new factor that is a concatenation of species and cohort ```{r eval=FALSE} sim_dat$species_cohort <- as.factor(paste(sim_dat$species, sim_dat$cohort)) ``` -If there were ~ 10 levels of `species_cohort`, and we were including our original spatial effects in `spatial_varying`, we could create a new map list that would indicate we wanted all species-cohort fields to have the same spatial variance, +If there were \~ 10 levels of `species_cohort`, and we were including our original spatial effects in the `spatial_varying` term, we could create a new map list that would indicate we wanted all species-cohort fields to have the same spatial variance as below. ```{r eval=FALSE} sim_dat$species_year <- as.factor(paste(sim_dat$species, sim_dat$year)) -# The first 2 elements correspond to species, remaining 10 to species-cohort +# The first 2 elements correspond to species, the remaining 10 to species-cohort map_list = list(ln_tau_Z = factor(c(1, 1, rep(2, 10)))) fit <- sdmTMB( - observed ~ 0 + fyear*species, + observed ~ 0 + fyear * species, data = sim_dat, mesh = spde, family = gaussian(), @@ -237,11 +259,3 @@ fit <- sdmTMB( control = sdmTMBcontrol(map = map_list) ) ``` - - - - - - - - From 90822e45ae59b0e01d9d99cf2f74d2b24a568855 Mon Sep 17 00:00:00 2001 From: Sean Anderson Date: Fri, 29 Nov 2024 10:22:29 -0800 Subject: [PATCH 03/15] Fix -s95 par reporting in tidy() --- R/tidy.R | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/R/tidy.R b/R/tidy.R index 71fe69e06..257905caf 100644 --- a/R/tidy.R +++ b/R/tidy.R @@ -135,14 +135,18 @@ tidy.sdmTMB <- function(x, effects = c("fixed", "ran_pars", "ran_vals"), model = if (x$tmb_data$threshold_func > 0) { if (x$threshold_function == 1L) { par_name <- paste0(x$threshold_parameter, c("-slope", "-breakpt")) + estimates <- est$b_threshold[,model,drop=TRUE] + ses <- se$b_threshold[,model,drop=TRUE] } else { par_name <- paste0(x$threshold_parameter, c("-s50", "-s95", "-smax")) + estimates <- c(est$s50[model], est$s95[model], est$s_max[model]) + ses <- c(se$s50[model], se$s95[model], se$s_max[model]) } out <- rbind( out, data.frame( - term = par_name, estimate = est$b_threshold[,model,drop=TRUE], - std.error = se$b_threshold[,model,drop=TRUE], stringsAsFactors = FALSE + term = par_name, estimate = estimates, + std.error = ses, stringsAsFactors = FALSE ) ) } From 884e5a394d49b560a628c8a540740f3465d7489c Mon Sep 17 00:00:00 2001 From: Sean Anderson Date: Fri, 29 Nov 2024 10:25:51 -0800 Subject: [PATCH 04/15] Bump version and news --- DESCRIPTION | 2 +- NEWS.md | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5a78d950d..90d4fef72 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: sdmTMB Title: Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB' -Version: 0.6.0.9013 +Version: 0.6.0.9014 Authors@R: c( person(c("Sean", "C."), "Anderson", , "sean@seananderson.ca", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index 27f53b2a9..85f44f7ee 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,10 @@ # sdmTMB (development version) +* Fix bug in `s95` param reporting from the `tidy()` method. `s95` is present + in the logistic threshold models. The model itself was fine but the `s95` + parameter was supposed to be reported by `tidy()` as a combination of two + other parameters. This also affected the output in `print()`/`summary()`. + * Add `return_tmb_report` to `simulate.sdmTMB()`. * Add `newdata` argument to `simulate.sdmTMB()`. This enables simulating on From d60107d11270b558dbf7d53f988d144d0c34c257 Mon Sep 17 00:00:00 2001 From: Sean Anderson Date: Fri, 29 Nov 2024 10:58:14 -0800 Subject: [PATCH 05/15] Fix threshold model tidy() unit test --- tests/testthat/test-threshold-models.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-threshold-models.R b/tests/testthat/test-threshold-models.R index db79cef50..15dfcb5d3 100644 --- a/tests/testthat/test-threshold-models.R +++ b/tests/testthat/test-threshold-models.R @@ -10,7 +10,7 @@ test_that("A logistic threshold model fits", { expect_true("depth_scaled-s50" %in% tidy(m)$term) expect_true("depth_scaled-s95" %in% tidy(m)$term) expect_true("depth_scaled-smax" %in% tidy(m)$term) - expect_equal(tidy(m)[,"estimate",drop=TRUE], c(1.555 , 1.655 , 1.718 , 1.138, -0.979, -3.173 , 1.760), tolerance = 1e-3) + expect_equal(tidy(m)[,"estimate",drop=TRUE], c(1.555 , 1.655 , 1.718 , 1.138, -0.979, -0.937 , 1.760), tolerance = 1e-3) }) test_that("A linear threshold model fits", { @@ -114,3 +114,4 @@ test_that("A linear threshold *delta* model fits", { expect_equal(t1$std.error, td1$std.error, tolerance = 1e-5) expect_equal(t2$std.error, td2$std.error, tolerance = 1e-5) }) + From e1ad91ca50912a2c1fc380d0fab009be36ccbb3a Mon Sep 17 00:00:00 2001 From: Sean Anderson Date: Fri, 29 Nov 2024 12:53:23 -0800 Subject: [PATCH 06/15] Update ggeffects vignette: add.data->show_data --- vignettes/articles/ggeffects.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/articles/ggeffects.Rmd b/vignettes/articles/ggeffects.Rmd index 72ee35eec..dc634117a 100644 --- a/vignettes/articles/ggeffects.Rmd +++ b/vignettes/articles/ggeffects.Rmd @@ -81,7 +81,7 @@ plot(g2) We can add in data points ```{r} -plot(g, add.data = TRUE) +plot(g, show_data = TRUE) ``` We can also use `ggeffect` to plot multiple variables by listing them in `terms = c()`, with the first term listed indicating the variable to be plotted on the x-axis, and the remaining listed terms (up to four total) indicating the groups. @@ -120,7 +120,7 @@ g2 <- ggeffect(fit2, "depth_scaled [-3:2.7 by=0.05]") plot(g2) # note the high density values dwarf the fitted curve here -plot(g2, add.dat = TRUE) +plot(g2, show_data = TRUE) ``` We can fit a model with an interaction of two continuous variables: From d92ebd840b567f8e604a2cf0459618bb5004ead0 Mon Sep 17 00:00:00 2001 From: olivroy Date: Fri, 29 Nov 2024 16:49:06 -0500 Subject: [PATCH 07/15] remove clisymbols dependency (for cli) --- DESCRIPTION | 1 - R/mesh.R | 6 +++--- scratch/binary-search.R | 6 +++--- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 90d4fef72..4378896d7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -64,7 +64,6 @@ Depends: Imports: assertthat, abind, - clisymbols, cli, fmesher, fishMod, diff --git a/R/mesh.R b/R/mesh.R index 0a071ed1b..4b6086ebb 100644 --- a/R/mesh.R +++ b/R/mesh.R @@ -207,12 +207,12 @@ binary_search_knots <- function(loc_xy, cat("cutoff =", pretty_cutoff, "| knots =", realized_knots) if (realized_knots > n_knots) { L <- m + 1 - cat(" |", clisymbols::symbol$arrow_down, "\n") + cat(" |", cli::symbol$arrow_down, "\n") } else if (realized_knots < n_knots) { R <- m - 1 - cat(" |", clisymbols::symbol$arrow_up, "\n") + cat(" |", cli::symbol$arrow_up, "\n") } else { - cat(" |", clisymbols::symbol$tick, "\n") + cat(" |", cli::symbol$tick, "\n") return(mesh) } } diff --git a/scratch/binary-search.R b/scratch/binary-search.R index f53ed20d8..64ebf6787 100644 --- a/scratch/binary-search.R +++ b/scratch/binary-search.R @@ -47,12 +47,12 @@ binary_search_knots <- function(x, y, cat("cutoff =", pretty_cutoff, "| knots =", realized_knots) if (realized_knots > n_knots) { L <- m + 1 - cat(" |", clisymbols::symbol$arrow_down, "\n") + cat(" |", cli::symbol$arrow_down, "\n") } else if (realized_knots < n_knots) { R <- m - 1 - cat(" |", clisymbols::symbol$arrow_up, "\n") + cat(" |", cli::symbol$arrow_up, "\n") } else { - cat(" |", clisymbols::symbol$tick, "\n") + cat(" |", cli::symbol$tick, "\n") return(mesh) } } From 8a44480403da5128d40c264966a59bdffc5df02b Mon Sep 17 00:00:00 2001 From: olivroy Date: Fri, 29 Nov 2024 16:53:14 -0500 Subject: [PATCH 08/15] add favicons + logo to pkgdown site in every page --- README.md | 2 +- header.md | 2 +- man/figures/{logo-sdmTMB.png => logo.png} | Bin pkgdown/favicon/apple-touch-icon.png | Bin 0 -> 52392 bytes pkgdown/favicon/favicon-96x96.png | Bin 0 -> 17506 bytes pkgdown/favicon/favicon.ico | Bin 0 -> 15086 bytes pkgdown/favicon/favicon.svg | 3 +++ pkgdown/favicon/site.webmanifest | 21 +++++++++++++++++++ pkgdown/favicon/web-app-manifest-192x192.png | Bin 0 -> 58865 bytes pkgdown/favicon/web-app-manifest-512x512.png | Bin 0 -> 338424 bytes 10 files changed, 26 insertions(+), 2 deletions(-) rename man/figures/{logo-sdmTMB.png => logo.png} (100%) create mode 100644 pkgdown/favicon/apple-touch-icon.png create mode 100644 pkgdown/favicon/favicon-96x96.png create mode 100644 pkgdown/favicon/favicon.ico create mode 100644 pkgdown/favicon/favicon.svg create mode 100644 pkgdown/favicon/site.webmanifest create mode 100644 pkgdown/favicon/web-app-manifest-192x192.png create mode 100644 pkgdown/favicon/web-app-manifest-512x512.png diff --git a/README.md b/README.md index 7147a42e9..8e893844f 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ -# sdmTMB +# sdmTMB > Spatial and spatiotemporal GLMMs with TMB diff --git a/header.md b/header.md index a24995118..d67664607 100644 --- a/header.md +++ b/header.md @@ -1,6 +1,6 @@ -# sdmTMB +# sdmTMB > Spatial and spatiotemporal GLMMs with TMB diff --git a/man/figures/logo-sdmTMB.png b/man/figures/logo.png similarity index 100% rename from man/figures/logo-sdmTMB.png rename to man/figures/logo.png diff --git a/pkgdown/favicon/apple-touch-icon.png b/pkgdown/favicon/apple-touch-icon.png new file mode 100644 index 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