Merge pull request #25 from r-spatialecology/dev

Dev

.github/workflows/Deploy-pkgdown.yaml

@@ -3,7 +3,7 @@
 
 on:
   push:
-    branches: [main, development]
+    branches: [main, dev]
     tags: ['*']
   workflow_dispatch:
 

.github/workflows/R-CMD-check.yaml

@@ -3,7 +3,7 @@
 
 on:
   push:
-    branches: [main, development]
+    branches: [main, dev]
   pull_request:
     branches: [main]
   workflow_dispatch:

.github/workflows/Render-README.yaml

@@ -3,7 +3,7 @@
 
 on:
   push:
-    branches: [main, development]
+    branches: [main, dev]
     paths: ["README.Rmd"]
   workflow_dispatch:
 

.github/workflows/Test-coverage.yaml

@@ -3,7 +3,7 @@
 
 on:
   push:
-    branches: [main, development]
+    branches: [main, dev]
   pull_request:
     branches: [main]
   workflow_dispatch:

.gitignore

@@ -4,3 +4,4 @@
 .Ruserdata
 docs
 CRAN-SUBMISSION
+.DS_Store

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: onpoint
 Title: Helper Functions for Point Pattern Analysis
-Version: 1.0.6
+Version: 1.1
 Authors@R: 
   person("Maximilian H.K.", "Hesselbarth", 
   email = "[email protected]",

NAMESPACE

@@ -2,12 +2,12 @@
 
 S3method(plot,env_summarized)
 S3method(print,env_summarized)
+export(Oest)
 export(balance_points)
-export(center_l_function)
-export(estimate_o_ring)
-export(estimate_pcf_fast)
+export(center_Lest)
+export(pcf_fast)
 export(plot_quantums)
+export(rheteroppp)
 export(rlabel_local)
-export(simulate_antecedent_conditions)
-export(simulate_heterogenous_pattern)
+export(simulate_antecedent)
 export(summarize_envelope)

NEWS.md

@@ -1,3 +1,6 @@
+# onpoint 1.1
+* Improved function names
+
 # onpoint 1.0.6
 * Order of stable `i` and randomized pattern `j` changed in `simulate_antecedent_conditions`
 * Fix anchors in documentation

R/estimate_o_ring.R → R/Oest.R

@@ -1,4 +1,4 @@
-#' estimate_o_ring
+#' Oest
 #'
 #' @description O-ring function
 #'
@@ -26,7 +26,7 @@
 #'
 #' @examples
 #' input_pattern <- spatstat.random::runifpoint(n = 100)
-#' estimate_o_ring(input_pattern)
+#' Oest(input_pattern)
 #'
 #' @references
 #' Wiegand, T., Moloney, K.A., 2004. Rings, circles, and null models for point pattern
@@ -36,7 +36,7 @@
 #' ecology. Chapman and Hall/CRC Press, Boca Raton, USA. <isbn:978-1-4200-8254-8>
 #'
 #' @export
-estimate_o_ring <- function(x, ...) {
+Oest <- function(x, ...) {
 
   if (!spatstat.geom::is.ppp(x)) {
     stop("Please provide ppp.")

R/balance_points.R → R/balance-points.R

@@ -8,8 +8,8 @@
 #'
 #' @details
 #' The function balances out the number of points in the input pattern to either
-#' the provided number of points as integer or the same number of points if a \code{ppp}
-#' object is provided.
+#' the provided number of points as integer or the same number of points if a
+#' \code{ppp} object is provided.
 #'
 #' @return ppp
 #'

R/center_l_function.R → R/center-Lest.R

@@ -1,4 +1,4 @@
-#' center_l_function
+#' center_Lest
 #'
 #' @description Centered L-function
 #'
@@ -18,10 +18,10 @@
 #'
 #' @examples
 #' input_pattern <- spatstat.random::runifpoint(n = 100)
-#' center_l_function(input_pattern, correction = "Ripley")
+#' center_Lest(input_pattern, correction = "Ripley")
 #'
 #' lest <- spatstat.explore::Lest(input_pattern)
-#' center_l_function(lest)
+#' center_Lest(lest)
 #'
 #' @references
 #' Besag, J.E., 1977. Discussion on Dr. Ripley’s paper. Journal of the Royal Statistical
@@ -36,7 +36,7 @@
 #' <https://doi.org/10.2307/3236356>
 #'
 #' @export
-center_l_function <- function(x, ...) {
+center_Lest <- function(x, ...) {
 
   if (!spatstat.geom::is.ppp(x) && !spatstat.geom::is.fv(x)) {
     stop("Please provide either ppp or fv object.")

R/estimate_pcf_fast.R → R/pcf-fast.R

@@ -1,4 +1,4 @@
-#' estimate_pcf_fast
+#' pcf_fast
 #'
 #' @description Fast estimation of the pair correlation function
 #'
@@ -22,7 +22,7 @@
 #' @examples
 #' set.seed(42)
 #' pattern <- spatstat.random::runifpoint(n = 100)
-#' pcf_fast <- estimate_pcf_fast(pattern)
+#' pcf_fast <- pcf_fast(pattern)
 #'
 #' @references
 #' Ripley, B.D., 1977. Modelling spatial patterns. Journal of the Royal Statistical Society.
@@ -32,7 +32,7 @@
 #' John Wiley & Sons, Chichester, UK. <isbn:978-0-471-93757-9>
 #'
 #' @export
-estimate_pcf_fast <- function(pattern, ...){
+pcf_fast <- function(pattern, ...){
 
   k_fun <- suppressMessages(spatstat.explore::Kest(X = pattern, ...)) # estimate K-fct
 

R/simulate_heterogenous_pattern.R → R/rheteroppp.R

@@ -1,4 +1,4 @@
-#' simulate_heterogenous_pattern
+#' rheteroppp
 #'
 #' @description Simulate heterogeneous pattern
 #'
@@ -23,7 +23,7 @@
 #' @examples
 #' set.seed(42)
 #' input_pattern <- spatstat.random::rpoispp(lambda = function(x , y) {100 * exp(-3 * x)}, nsim = 1)
-#' null_model <- simulate_heterogenous_pattern(input_pattern, nsim = 19)
+#' null_model <- rheteroppp(input_pattern, nsim = 19)
 #' spatstat.explore::envelope(Y = input_pattern, fun = spatstat.explore::pcf, nsim = 19,
 #' simulate = null_model)
 #'
@@ -35,7 +35,7 @@
 #' ecology. Chapman and Hall/CRC Press, Boca Raton, USA. <isbn:978-1-4200-8254-8>
 #'
 #' @export
-simulate_heterogenous_pattern <- function(x, nsim, fix_n = FALSE, ...) {
+rheteroppp <- function(x, nsim, fix_n = FALSE, ...) {
 
   if (!inherits(x = x, what = "ppp")) {
     stop("Please provide ppp object.", call. = FALSE)

R/simulate_antecedent_conditions.R → R/simulate-antecedent.R

@@ -1,4 +1,4 @@
-#' simulate_antecedent_conditions
+#' simulate_antecedent
 #'
 #' @description Simulate heterogenous pattern
 #'
@@ -33,7 +33,7 @@
 #' spatstat.geom::marks(pattern_b) <- "b"
 #' pattern <- spatstat.geom::superimpose(pattern_a, pattern_b)
 #'
-#' null_model <- simulate_antecedent_conditions(x = pattern, i = "a", j = "b", nsim = 19)
+#' null_model <- simulate_antecedent(x = pattern, i = "a", j = "b", nsim = 19)
 #' spatstat.explore::envelope(Y = pattern, fun = spatstat.explore::pcf,
 #' nsim = 19, simulate = null_model)
 #'
@@ -46,7 +46,7 @@
 #' ecology. Chapman and Hall/CRC Press, Boca Raton, USA. <isbn:978-1-4200-8254-8>
 #'
 #' @export
-simulate_antecedent_conditions <- function(x, i, j, nsim, heterogenous = FALSE, ...) {
+simulate_antecedent <- function(x, i, j, nsim, heterogenous = FALSE, ...) {
 
   # check if pattern ist marked
   if (!spatstat.geom::is.marked(x)) {

README.Rmd

@@ -58,15 +58,14 @@ data(spruces)
 
 Currently, `onpoint` provides three second-order summary functions, namely Besag's L-function centered to zero and the O-ring statistic and a fast estimation of the pair-correlation function. 
 
-Centering Besag's L-function to zero has the advantage of an easier interpretation and plotting (Haase 1995). The function `center_l_function()` can either deal with a point pattern and calculated the centered L-function directly, or center the L-function afterwards it was calculating using `spatstat`s `Lest()`.
+Centering Besag's L-function to zero has the advantage of an easier interpretation and plotting (Haase 1995). The function `center_Lest()` can either deal with a point pattern and calculated the centered L-function directly, or center the L-function afterwards it was calculating using `spatstat`s `Lest()`.
 
 ```{r calc_lfun}
 # calculate L-function
 l_function <- Lest(spruces, correction = "Ripley")
 
 # center L-function to zero
-# center_l_function <- center_l_function(l_function)
-l_function_centered <- center_l_function(spruces, correction = "Ripley")
+l_function_centered <- center_Lest(spruces, correction = "Ripley")
 ```
 
 ```{r plot_lfun, echo = FALSE, fig.width = 10, fig.height = 5, fig.align = "center"}
@@ -76,10 +75,10 @@ plot(l_function_centered, main = "centered Besag's L-function")
 par(mfrow = c(1, 1))
 ```
 
-The O-ring statistic O(r) (Wiegand & Moloney 2004) can be calculated using `estimate_o_ring()`. Generally speaking, O(r) scales the pair correlation g(r) function with help of the intensity \eqn{\lambda}. One advantage of the O-ring statistic is that it can be interpreted as a neighborhood density because it is a probability density function (Wiegand & Moloney 2004).
+The O-ring statistic O(r) (Wiegand & Moloney 2004) can be calculated using `Oest()`. Generally speaking, O(r) scales the pair correlation g(r) function with help of the intensity \eqn{\lambda}. One advantage of the O-ring statistic is that it can be interpreted as a neighborhood density because it is a probability density function (Wiegand & Moloney 2004).
 
 ```{r calc_oring}
-o_ring <- estimate_o_ring(spruces)
+o_ring <- Oest(spruces)
 ```
 
 Of course, both summary functions can be used in combination with `spatstat`'s `envelope()` function.
@@ -92,20 +91,20 @@ oring_envelope <- envelope(spruces, fun = estimate_o_ring, nsim = 199, verbose =
 plot(oring_envelope, main = "O-ring statistic")
 ```
 
-`estimate_pcf_fast()` estimates the pair-correlation function based on Ripley's K-function, which is faster than estimation the pair-correlation function directly.
+`pcf_fast()` estimates the pair-correlation function based on Ripley's K-function, which is faster than estimation the pair-correlation function directly.
 
 ```{r calc_fast, collapse = TRUE}
-estimate_pcf_fast(spruces)
+pcf_fast(spruces)
 ```
 
 ### Null models 
 
 `onpoint` includes two functions to simulate null model patterns. 
 
-`simulate_heterogenous_pattern()` is a convienent wrapper around a few `spatstat` functions to straighforward simulate a heterogeneous Poisson process.
+`rheteroppp()` is a convienent wrapper around a few `spatstat` functions to straighforward simulate a heterogeneous Poisson process.
 
 ```{r calc_hetero}
-null_model_hetero <- simulate_heterogenous_pattern(spruces, nsim = 199)
+null_model_hetero <- rheteroppp(spruces, nsim = 199)
 
 hetero <- envelope(spruces, fun = pcf, 
                    funargs = list(correction = "Ripley", divisor = "d"),
@@ -117,13 +116,13 @@ hetero <- envelope(spruces, fun = pcf,
 plot(hetero, main = "Heterogeneous Poisson process")
 ```
 
-To simulate antecedent conditions in which only one pattern influences the other, but not the other way around (Wiegand & Moloney 2004, Velazquez et al. 2016), `simulate_antecedent_conditions()` can be used. This null model randomizes only one type of points (e.g. seedlings), while keeping the other type of points constant (e.g. mature trees) to check for associations between the two.
+To simulate antecedent conditions in which only one pattern influences the other, but not the other way around (Wiegand & Moloney 2004, Velazquez et al. 2016), `simulate_antecedent()` can be used. This null model randomizes only one type of points (e.g. seedlings), while keeping the other type of points constant (e.g. mature trees) to check for associations between the two.
 
 ```{r calc_antecedent}
 marks(spruces) <- ifelse(marks(spruces) > 0.3, yes = "adult", no = "seedling")
 
-null_model_antecedent <- simulate_antecedent_conditions(spruces, 
-                                                        i = "seedling", j = "adult", nsim = 199)
+null_model_antecedent <- simulate_antecedent(spruces, i = "seedling", j = "adult",
+                                             nsim = 199)
 
 antecedent <- envelope(spruces, fun = pcf, 
                        funargs = list(correction = "Ripley", divisor = "d"),
@@ -137,7 +136,7 @@ plot(antecedent, main = "Antecedent conditions")
 
 ### Various
 
-To plot simulation envelopes using quantum plots (e.g. Esser et al. 2015), just pass an `envelope` object as input to `plot_quantums()`.
+To plot simulation envelopes using quantum plots (e.g., Esser et al. 2015), just pass an `envelope` object as input to `plot_quantums()`.
 
 ```{r plot_quantums, fig.width = 7.5, fig.height = 5, fig.align = "center"}
 plot_quantums(antecedent, ylab = "g(r)")

_pkgdown.yml

@@ -7,16 +7,16 @@ template:
 reference:
   - title: Summary functions
     contents:
-      - center_l_function
-      - estimate_o_ring
-      - estimate_pcf_fast
+      - center_Lest
+      - Oest
+      - pcf_fast
       - summarize_envelope
 
   - title: Null models
     contents:
       - rlabel_local
-      - simulate_heterogenous_pattern
-      - simulate_antecedent_conditions
+      - rheteroppp
+      - simulate_antecedent
 
   - title: Various
     contents:

cran-comments.md

@@ -1,3 +1,6 @@
+# onpoint 1.1
+* Improved function names
+
 # onpoint 1.0.6
 * Minor methodological improvements