🔥 fix 3 - Final for vignette rendering?

R/add_constraint.R

@@ -98,6 +98,8 @@ methods::setMethod(
   "add_constraint",
   methods::signature(mod = "BiodiversityScenario"),
   function(mod, method, ...) {
+    assertthat::assert_that(!missing(method),
+                            msg = "Set a method for the constraint!")
     assertthat::assert_that(
       inherits(mod, "BiodiversityScenario"),
       !is.Waiver(mod$get_predictors()),
@@ -725,7 +727,7 @@ methods::setMethod(
 #' @param value A [`numeric`] value describing the minimum amount of area of a
 #' given patch
 #' @param unit A [`character`] of the unit of area. Options available are
-#' \code{km2} (Default) and \code{ha}.
+#' \code{km2} (Default), \code{ha} and \code{pixel}.
 #' @param establishment_step A [`logical`] flag indicating whether a given patch
 #' is only to be removed if wasn't small in a previous time step (not yet
 #' implemented!)

R/project.R

@@ -176,7 +176,7 @@ methods::setMethod(
     # get predictor names of integrated model
     if (!is.Waiver(fit$.internals)) {
       int_pred_names <- lapply(fit$.internals, function(i) i$model$model$predictors_names)
-    } else (int_pred_names <- NULL)
+    } else { int_pred_names <- NULL }
 
     if (is.Waiver(fit$.internals)) {
       assertthat::assert_that(all(mod_pred_names %in% pred_names),
@@ -234,6 +234,10 @@ methods::setMethod(
       }
     }
 
+    if(("threshold" %in% names(scenario_constraints)) && is.Waiver(baseline_threshold)){
+      if(getOption('ibis.setupmessages', default = TRUE)) myLog('[Scenario]','yellow','Threshold constraint found but not threshold set? Apply threshold()!')
+    }
+
     if("connectivity" %in% names(scenario_constraints) && "dispersal" %notin% names(scenario_constraints)){
       if(getOption('ibis.setupmessages', default = TRUE)) myLog('[Scenario]','yellow','Connectivity contraints make most sense with a dispersal constraint.')
     }

R/utils-scenario.R

@@ -678,7 +678,7 @@ st_minsize <- function(obj, value, unit = "km2",  establishment_step = FALSE){
       new <- terra::mask(new, ori.obj)
     } else {
       # Now first label
-      labs <- terra::patches(obj)
+      labs <- terra::patches(obj,zeroAsNA = TRUE)
 
       # Then calculate area in km2
       ar <- terra::mask(terra::cellSize(labs, unit = "km"), labs)

R/utils.R

@@ -207,17 +207,20 @@ capitalize_text <- function(x) {
 #' @keywords utils
 #'
 #' @examples
-#' capitalize_text('presence')
-#' capitalize_text('ducks are the best birds')
+#' factor_to_numeric(factor("5"))
+#' factor_to_numeric(factor("test"))
 #'
 #' @noRd
 #'
 #' @keywords internal
 factor_to_numeric <- function(x) {
+  if(is.numeric(x)) return(x)
   assertthat::assert_that(is.factor(x) || is.character(x))
-  as.numeric(
-    as.character(
-      x
+  suppressWarnings(
+    as.numeric(
+      as.character(
+        x
+      )
     )
   )
 }

vignettes/articles/04_biodiversity_projections.Rmd

@@ -196,7 +196,7 @@ sc.fit2 <- sc |>
 
 # Also fit one projection a nichelimit has been added
 sc.fit3 <- sc |>   
-   add_constraint(method = "sdd_nex", value = 1e5) |> 
+   add_constraint(method = "sdd_nex", value = 1e5) |>
    add_constraint_adaptability(method = "nichelimit") |> 
    # Directly fit the object
    project(stabilize = F)

vignettes/articles/05_mechanistic_estimation.Rmd

@@ -86,7 +86,6 @@ op <- try({
                         quiet = TRUE,
                         build_vignettes = FALSE) # For steps
 },silent = TRUE)
-if(inherits(op, "try-error")) stop("steps could not be installed for this vignette!")
 ```
 
 ### Adding dispersal to scenarios with `KISSMig`
@@ -263,42 +262,45 @@ assumptions have been questioned before and should be interpreted with caution (
 __Users should always clearly understand the rationale behind parameter choices!__
 
 ```{r steps}
-require("steps")
-
-# Define some arbitrary vital rates for the transition for this purpose
-# Define vital rates
-vt <- matrix(c(0.0,0.52,0.75,
-              0.52,0.28,0.0,
-              0.0,0.52,0.75),
-              nrow = 3, ncol = 3, byrow = TRUE)
-colnames(vt) <- rownames(vt) <- c('juvenile','subadult','adult')
+if("steps" %in% installed.packages()[,1]){
+  require("steps")
+  
+  # Define some arbitrary vital rates for the transition for this purpose
+  # Define vital rates
+  vt <- matrix(c(0.0,0.52,0.75,
+                0.52,0.28,0.0,
+                0.0,0.52,0.75),
+                nrow = 3, ncol = 3, byrow = TRUE)
+  colnames(vt) <- rownames(vt) <- c('juvenile','subadult','adult')
+  
+  # We again specify a scenario as before using the fitted model
+  prj <- scenario(fit) |> 
+    # Apply the same variable transformations as above. 
+    add_predictors(pred_future, transform = 'scale') |> 
+    # Calculate thresholds at each time step. The threshold estimate is taken from 
+    # the fitted model object.
+    threshold() |> 
+    # We then specify that we we 
+    simulate_population_steps(vital_rates = vt)
+  
+  # Notice how we have added steps as additional simulation outcome
+  prj
+  # Now project
+  scenario1 <- project(prj)
+  plot(scenario1, "population")
+  
+  # Also see a different one where we add a dispersal constraint and density dependence
+  dispersal <- steps::fast_dispersal(dispersal_kernel = steps::exponential_dispersal_kernel(distance_decay = 1))
+  scenario2 <- project(prj |>
+                         simulate_population_steps(vt,
+                                                   dispersal = dispersal,
+                                                   density_dependence = steps::ceiling_density(3) ) )
+  
+  # We can see that the dispersal constraint and higher density dependence cleary
+  # results in a population abundance that tends to be concentrated in central Europe.
+  plot(scenario2, "population")
 
-# We again specify a scenario as before using the fitted model
-prj <- scenario(fit) |> 
-  # Apply the same variable transformations as above. 
-  add_predictors(pred_future, transform = 'scale') |> 
-  # Calculate thresholds at each time step. The threshold estimate is taken from 
-  # the fitted model object.
-  threshold() |> 
-  # We then specify that we we 
-  simulate_population_steps(vital_rates = vt)
-
-# Notice how we have added steps as additional simulation outcome
-prj
-# Now project
-scenario1 <- project(prj)
-plot(scenario1, "population")
-
-# Also see a different one where we add a dispersal constraint and density dependence
-dispersal <- steps::fast_dispersal(dispersal_kernel = steps::exponential_dispersal_kernel(distance_decay = 1))
-scenario2 <- project(prj |>
-                       simulate_population_steps(vt,
-                                                 dispersal = dispersal,
-                                                 density_dependence = steps::ceiling_density(3) ) )
-
-# We can see that the dispersal constraint and higher density dependence cleary
-# results in a population abundance that tends to be concentrated in central Europe.
-plot(scenario2, "population")
+}
 
 ```