formatting

R/bounded_niche.R

@@ -11,7 +11,7 @@ bounded_niche = function(g_min, g_max){
   #' @returns a function describing the niche for usage with `apply_niche`. The function returns 1 if the taxon is within its niche (the gradient is between `g_min` and `g_max`), and 0 otherwise
   #'
   #' @description
-    #' Defines a simple niche model where the defined is given by a lower limit (`g_min`) and an upper limie (`g_max`) of a gradient the taxon can tolerate
+    #' Defines a simple niche model where the niche defined is given by a lower limit (`g_min`) and an upper limie (`g_max`) of a gradient the taxon can tolerate
     #'
   #' @examples
     #' \dontrun{

R/snd_niche.R

@@ -7,7 +7,7 @@ snd_niche = function(opt, tol, prob_modifier = 1, cutoff_val = NULL){
   #' @param opt optimum value, gradient value where collection probability is highest
   #' @param tol tolerance to changes in gradient. For large values, collection probability drops off slower away from `opt`
   #' @param prob_modifier collection probability modifier, collection probability at `opt`.
-  #' @param cutoff_val NULL of a number. If a number, all collection probabilities at gradient values below `cutoff_value` are set to 0. This can for example be used to model exclusively marine species when the gradient is water depth (see examples).
+  #' @param cutoff_val NULL or a number. If a number, all collection probabilities at gradient values below `cutoff_value` are set to 0. This can for example be used to model exclusively marine species when the gradient is water depth (see examples).
   #'
   #' @returns a function for usage with `apply_niche`.
   #'
@@ -30,7 +30,7 @@ snd_niche = function(opt, tol, prob_modifier = 1, cutoff_val = NULL){
   #'
   #' @seealso [apply_niche()] for usage of the returned function, [bounded_niche()] for another niche model
   #' @description
-    #' Defines niche model based in the "Probability of collection" model by Holland and Patzkowsky (2002).
+    #' Defines niche model based in the "Probability of collection" model by Holland and Patzkowsky (1999).
     #' The collection probability follows the shape of a bell curve across a gradient, where `opt` determines the peak (mean) of the bell curve, and `tol` the standard deviation. "snd" stands for "scaled normal distribution", as the collection probability has the shape of the probability density of the normal distribution.
   #' @references * Holland, Steven M. and Patzkowsky, Mark E. 1999. "Models for simulating the fossil record." Geology. https://doi.org/10.1130/0091-7613(1999)027%3C0491:MFSTFR%3E2.3.CO;2
 

vignettes/StratPal.Rmd

@@ -145,7 +145,7 @@ The `StratPal` package comes with some example data for age-depth models stored
 
 ### Defining age-depth models
 
-Let's start with defining an age-depth model. This can be done with `tp_to_adm` (tie points to age-depth model):
+Let's start with defining the age-depth model 2 km from shore in scenario A. This can be done with `tp_to_adm` (tie points to age-depth model):
 
 ```{r}
 t = scenarioA$t_myr       # extract time tie points

vignettes/event_data.Rmd

@@ -244,14 +244,14 @@ plot(t, gc(t),
      main = "Water depth 2 km offshore")
 ```
 
-Next, we define the niche. This is done using a function that takes water depth (our gradient) as input, and returns numbers between 0 and 1. A 1 corresponds to the taxon's environmental optimum, and a 0 reflects that it is completely outside of its niche. Values between 0 and 1 are are interpreted as observation or collection probabilities. They determine the probability to actually observe a specimen that is present at a given time at the specified gradient.
+Next, we define the niche. This is done using a function that takes water depth (our gradient) as input, and returns numbers between 0 and 1. A 1 corresponds to the taxon's environmental optimum, and a 0 reflects that it is completely outside of its niche. Values between 0 and 1 are are interpreted as observation or collection probabilities. If a specimen is present at a given time, the collection probability is the probability to observe it at the specified gradient.
 
-For our example, we use the helper function `snd_niche` (snd standing for scaled normal distribution) to define a niche. The collection probability in this model follows the shape of a bell curve with a peak at the environmental optimum, and a width specified by the `tol` parameter. This is identical to the *probability of collection* model by [Holland and Patzkowsky (1999)](#References). See the "advanced functionality" vignette for details how to construct other niche definitions, or `?bounded_niche` for another template niche model.
+For our example, we use the helper function `snd_niche` (*snd* standing for "scaled normal distribution") to define a niche. The collection probability in this model follows the shape of a bell curve with a peak at the environmental optimum, and a width specified by the `tol` parameter. This is identical to the *probability of collection* model by [Holland and Patzkowsky (1999)](#References). See the "advanced functionality" vignette for details how to construct other niche definitions, or `?bounded_niche` for another template niche model.
 
 ```{r}
 my_niche = snd_niche(opt = 10,       # preferred water depth 
                      tol = 5,        # tolerance to water depth fluctuations
-                     cutoff_val = 0)  # cut off negative values - the taxon does not survive on land
+                     cutoff_val = 0) # cut off negative values - the taxon does not survive on land
 x = seq(-2, 30, by = 0.1)
 plot(x, my_niche(x),
      type = "l",
@@ -266,7 +266,7 @@ With the niche and the change in gradient defined, we can use `apply_niche` for
 
 ```{r}
 p3(rate = 300, from = min_time(adm_2km), to = max_time(adm_2km)) |> # model occurrences of taxon based on a constant rate
-  apply_niche(niche_def = my_niche, gc = gc) |>                # apply the niche preference of the taxon as defined above
+  apply_niche(niche_def = my_niche, gc = gc) |>                     # apply the niche model
   hist(xlab = "Time [Myr]",
        main = "Fossil abundance",
        ylab = "# Fossils",
@@ -279,7 +279,7 @@ With the basic niche modeling done, we can incorporate it into our modeling pipe
 
 ```{r}
 p3(rate = 300, from = min_time(adm_2km), to = max_time(adm_2km)) |> # model occurrences based on constant rate
-  apply_niche(niche_def = my_niche, gc = gc) |>                # apply niche preference
+  apply_niche(niche_def = my_niche, gc = gc) |>                     # apply niche model
   time_to_strat(adm_2km, destructive = TRUE) |>                     # transform into strat. domain, destroy fossils that coincide with hiatuses 
   hist(xlab = "Stratigraphic height [m]",                           # plot results
        main = "Fossil abundance 2 km from shore",