Merge pull request #60 from r-spatialecology/master

v1.0.1

Author: mhesselbarth

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: shar
 Title: Species-Habitat Associations
-Version: 1.0
+Version: 1.0.1
 Authors@R: c(person("Maximillian H.K.", "Hesselbarth", email = "[email protected]",
              role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1125-9918")),
              person("Marco", "Sciaini", email = "[email protected]",

NEWS.md

@@ -1,3 +1,7 @@
+# shar 1.0.1
+* Improvements
+  * No calculation of energy for printig (too slow)
+
 # shar 1.0
 * Improvements
   * Printing methods for most objects

R/print.rd_mar.R

@@ -3,7 +3,6 @@
 #' @description Print method for rd_mar object
 #'
 #' @param x Random patterns.
-#' @param calc_energy If TRUE energy is calculated.
 #' @param digits Number of decimal places (round).
 #' @param ... Arguments passed to cat
 #'
@@ -27,17 +26,13 @@
 
 #' @export
 print.rd_mar <- function(x,
-                         calc_energy = TRUE,
                          digits = 4,
                          ...) {
 
   # set length observed pattern to 0 and
   # return warning that energy can't be calculated
   if (!spatstat::is.ppp(x$observed)) {
 
-    warning("Energy can not be calculated without observed pattern.",
-            call. = FALSE)
-
     number_patterns_obs <- 0
 
     includes_observed <- "NA"
@@ -54,24 +49,6 @@ print.rd_mar <- function(x,
   # get number of randomized patterns plus observed pattern
   number_patterns <- length(x$randomized) + number_patterns_obs
 
-  # calculate energy
-  if (calc_energy) {
-
-    # try to calculate energy
-    energy <- tryCatch(expr = round(calculate_energy(x,
-                                                     return_mean = TRUE,
-                                                     verbose = FALSE),
-                                    digits = digits),
-                       error = function(e) "NA")
-  }
-
-  # not calculate energy
-  else {
-
-    energy <- NA
-  }
-
-
   # calculate mean iterations
   mean_iterations <- round(mean(unlist(x$iterations)),
                            digits = digits)
@@ -88,7 +65,6 @@ print.rd_mar <- function(x,
 
   # print result
   cat(paste0("No. of pattern: ", number_patterns, "\n",
-             "Mean energy: ", energy, "\n",
              "Method: ", x$method, "\n",
              "Observed pattern: ", includes_observed, "\n",
              "Iterations (mean): ", mean_iterations, "\n",

R/print.rd_pat.R

@@ -3,10 +3,7 @@
 #' @description Print method for rd_pat object
 #'
 #' @param x Random patterns.
-#' @param calc_energy If TRUE energy is calculated.
 #' @param digits Number of decimal places (round).
-#' @param weights Weights used to calculate energy. The first number refers to Gest(r),
-#' the second number to pcf(r).
 #' @param ... Arguments passed to cat
 #'
 #' @details
@@ -34,18 +31,13 @@
 
 #' @export
 print.rd_pat <- function(x,
-                         calc_energy = TRUE,
                          digits = 4,
-                         weights = c(0.5, 0.5),
                          ...) {
 
   # set length observed pattern to 0 and
   # return warning that energy can't be calculated
   if (!spatstat::is.ppp(x$observed)) {
 
-    warning("Energy can not be calculated without observed pattern.",
-            call. = FALSE)
-
     number_patterns_obs <- 0
 
     includes_observed <- "NA"
@@ -62,24 +54,6 @@ print.rd_pat <- function(x,
   # get number of randomized patterns plus observed pattern
   number_patterns <- length(x$randomized) + number_patterns_obs
 
-  # calculate energy
-  if (calc_energy) {
-
-  # try to calculate energy
-  energy <- tryCatch(expr = round(calculate_energy(x,
-                                                   weights = weights,
-                                                   return_mean = TRUE,
-                                                   verbose = FALSE),
-                                  digits = digits),
-                     error = function(e) NA)
-  }
-
-  # not calculate energy
-  else {
-
-    energy <- NA
-  }
-
   # calculate mean iterations
   mean_iterations <- round(mean(unlist(x$iterations)),
                            digits = digits)
@@ -90,13 +64,12 @@ print.rd_pat <- function(x,
 
   if (!any(is.na(stop_criterion))) {
 
-    stop_criterion <- paste0(paste0(names(stop_criterion), ":", stop_criterion),
+    stop_criterion <- paste0(paste0(names(stop_criterion), "=", stop_criterion),
                              collapse = " ")
   }
 
   # print result
   cat(paste0("No. of pattern: ", number_patterns, "\n",
-             "Mean energy: ", energy, "\n",
              "Method: ", x$method, "\n",
              "Observed pattern: ", includes_observed, "\n",
              "Iterations (mean): ", mean_iterations, "\n",

README.Rmd

@@ -45,7 +45,7 @@ library(shar)
 library(raster)
 ```
 
-**shar** comes with build-in example data sets. `species_a` and `species_b` are examplary location of species, e.g. trees, as `ppp`-objects from the `spatstat` package. `landscape` contains examplary continious environmental data. However, all methods depend on discrete data. Therefore we need to classify the data first. 
+**shar** comes with build-in example data sets. `species_a` and `species_b` are exemplary location of species, e.g. trees, as `ppp`-objects from the `spatstat` package. `landscape` contains examplary continuous environmental data. However, all methods depend on discrete data. Therefore we need to classify the data first. 
 
 ```{r environmental-data}
 landscape_classified <- classify_habitats(raster = landscape, classes = 5)
@@ -73,7 +73,7 @@ gamma_test <- fit_point_process(pattern = species_a, process = "cluster", n_rand
 reconstruction <- reconstruct_pattern_cluster(pattern = species_b, n_random = 39, verbose = FALSE) # takes some time
 ```
 
-Of coures, there are several utility functions. For example, you can plot a randomized pattern or calculate the differences between the observed pattern and the randomized patterns (using summary functions). 
+Of course, there are several utility functions. For example, you can plot a randomized pattern or calculate the differences between the observed pattern and the randomized patterns (using summary functions). 
 
 ```{r plot-random_pattern, fig.width = 5.5, fig.height = 4, fig.align = "center"}
 plot_randomized_pattern(reconstruction, verbose = FALSE, ask = FALSE)
@@ -83,7 +83,7 @@ plot_randomized_pattern(reconstruction, verbose = FALSE, ask = FALSE)
 calculate_energy(reconstruction, verbose = FALSE)
 ```
 
-The data was created that `species_a` has a negative association to habitat 4 and `species_b` has a positive association to habitat 5. At one point a posititive association to one habitat leads consequently to a negative association to another habitat (and vice versa). All this can be seen in the results.
+The data was created that `species_a` has a negative association to habitat 4 and `species_b` has a positive association to habitat 5. At one point a positive association to one habitat leads consequently to a negative association to another habitat (and vice versa). All this can be seen in the results.
 
 ```{r results}
 results_habitat_association(pattern = species_a, raster = torus_trans)

README.html

@@ -7,13 +7,14 @@ status](https://travis-ci.org/r-spatialecology/shar.svg?branch=master)](https://
 status](https://ci.appveyor.com/api/projects/status/08hgwkr82pqb6ykq/branch/master?svg=true)](https://ci.appveyor.com/project/mhesselbarth/shar/branch/master)
 [![Coverage
 status](https://codecov.io/gh/r-spatialecology/shar/branch/master/graph/badge.svg)](https://codecov.io/gh/r-spatialecology/shar?branch=master)
-[![Project Status: Active – The project has reached a stable, usable
-state and is being actively
-developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
-[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
+[![Project
+Status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![Lifecycle:
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/shar)](https://cran.r-project.org/package=shar)
-[![](http://cranlogs.r-pkg.org/badges/grand-total/shar)](http://cran.rstudio.com/web/packages/shar/index.html)
+[![CRAN
+logs](http://cranlogs.r-pkg.org/badges/grand-total/shar)](http://cran.rstudio.com/web/packages/shar/index.html)
 [![License: GPL
 v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
@@ -96,7 +97,7 @@ Plotkin et al. (2000) or pattern reconstruction (Tscheschel & Stoyan
 ``` r
 gamma_test <- fit_point_process(pattern = species_a, process = "cluster", n_random = 39, verbose = FALSE)
 
-reconstruction <- reconstruct_pattern(pattern = species_b, n_random = 39, verbose = FALSE) # takes some time
+reconstruction <- reconstruct_pattern_cluster(pattern = species_b, n_random = 39, verbose = FALSE) # takes some time
 ```
 
 Of coures, there are several utility functions. For example, you can
@@ -111,13 +112,22 @@ plot_randomized_pattern(reconstruction, verbose = FALSE, ask = FALSE)
 
 ``` r
 calculate_energy(reconstruction, verbose = FALSE)
-#>  [1] 0.01356462 0.01867389 0.01455831 0.01754614 0.01858267 0.02008615
-#>  [7] 0.01544740 0.01470591 0.01255234 0.01837727 0.01622154 0.01765028
-#> [13] 0.01517141 0.02075634 0.01836698 0.01523319 0.01832501 0.02072956
-#> [19] 0.01845929 0.01733616 0.01639753 0.01887276 0.01738685 0.01361427
-#> [25] 0.01619153 0.01695853 0.01856459 0.01692591 0.01986748 0.01604911
-#> [31] 0.01972613 0.01518272 0.01681647 0.02010567 0.01472619 0.01794340
-#> [37] 0.01740046 0.01531350 0.01869501
+##  randomized_1  randomized_2  randomized_3  randomized_4  randomized_5 
+##    0.01716646    0.02080741    0.02469685    0.02252225    0.01905148 
+##  randomized_6  randomized_7  randomized_8  randomized_9 randomized_10 
+##    0.02317256    0.01467501    0.02009006    0.01796639    0.01742050 
+## randomized_11 randomized_12 randomized_13 randomized_14 randomized_15 
+##    0.01967685    0.01531558    0.01429541    0.02049072    0.01755755 
+## randomized_16 randomized_17 randomized_18 randomized_19 randomized_20 
+##    0.01672621    0.01993774    0.01695963    0.01949814    0.01368794 
+## randomized_21 randomized_22 randomized_23 randomized_24 randomized_25 
+##    0.02298234    0.01629161    0.02034008    0.01466430    0.02274970 
+## randomized_26 randomized_27 randomized_28 randomized_29 randomized_30 
+##    0.01857961    0.01489574    0.01694560    0.01860603    0.02151825 
+## randomized_31 randomized_32 randomized_33 randomized_34 randomized_35 
+##    0.01719001    0.01744145    0.01735466    0.01988854    0.02075585 
+## randomized_36 randomized_37 randomized_38 randomized_39 
+##    0.02174524    0.02042854    0.01674873    0.01900174
 ```
 
 The data was created that `species_a` has a negative association to
@@ -128,22 +138,22 @@ be seen in the results.
 
 ``` r
 results_habitat_association(pattern = species_a, raster = torus_trans)
-#> > Input: randomized raster | Quantile thresholds: negative < 0.025 - positive > 0.975
-#>   habitat count lo hi significance
-#> 1       1     9  2 14         n.s.
-#> 2       2    25  9 24     positive
-#> 3       3    27 11 26     positive
-#> 4       4     0 11 29     negative
-#> 5       5    12  5 18         n.s.
+## > Input: randomized raster | Quantile thresholds: negative < 0.025 - positive > 0.975
+##   habitat count lo hi significance
+## 1       1     9  2 14         n.s.
+## 2       2    25  9 24     positive
+## 3       3    27 11 26     positive
+## 4       4     0 11 29     negative
+## 5       5    12  5 18         n.s.
 
 results_habitat_association(pattern = reconstruction, raster = landscape_classified)
-#> > Input: randomized point pattern | Quantile thresholds: negative < 0.025 - positive > 0.975
-#>   habitat count    lo    hi significance
-#> 1       1     8  4.85 43.15         n.s.
-#> 2       2    22 26.80 68.10     negative
-#> 3       3    33 46.80 74.10     negative
-#> 4       4    19 29.80 69.10     negative
-#> 5       5   118  9.95 37.05     positive
+## > Input: randomized point pattern | Quantile thresholds: negative < 0.025 - positive > 0.975
+##   habitat count    lo    hi significance
+## 1       1     8  1.00 23.35         n.s.
+## 2       2    22 25.85 49.10     negative
+## 3       3    33 40.80 68.05     negative
+## 4       4    19 44.00 71.10     negative
+## 5       5   118 20.85 60.05     positive
 ```
 
 ## References

README.md

@@ -9,12 +9,20 @@ reference:
     contents:
       - species_a
       - species_b
+      - landscape
+      - gamma_test
+      - reconstruction
+      - reconstruct_pattern_marks
+      - random_walk
+      - torus_trans
   - title: Randomize data
     contents:
       - fit_point_process
       - randomize_raster
-      - reconstruct_marks
-      - reconstruct_pattern
+      - reconstruct_pattern_homo
+      - reconstruct_pattern_hetero
+      - reconstruct_pattern_cluster
+      - reconstruct_pattern_marks
       - translate_raster
   - title: Analyse results
     contents:
@@ -30,6 +38,9 @@ reference:
       - estimate_pcf_fast
       - extract_points
       - rcpp_sample
+      - print.rd_mar
+      - print.rd_pat
+      - print.rd_ras
 
 navbar:
   title: shar

_pkgdown.yml

@@ -1,5 +1,8 @@
 For details changes, please see NEWS.md.
 
+## shar 1.0.1
+Some minor improvments to existing functions
+
 ## shar 1.0
 New functionality and renameing/splitting of some functions