version 1.2.1

cran · Apr 6, 2023 · 2a274a8 · 2a274a8
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: mixtur
 Title: Modelling Continuous Report Visual Short-Term Memory Studies
-Version: 1.2.0
-Date: 2021-07-27
+Version: 1.2.1
+Date: 2023-04-06
 Authors@R: c(
     person(
         given = "Jim", 
@@ -27,22 +27,22 @@ Description: A set of utility functions for analysing and modelling data from
     3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
     are also able to simulate from these models.
 Depends: R (>= 4.0)
-Imports: dplyr, ggplot2, rlang, tidyr, RColorBrewer
+Imports: dplyr, ggplot2, rlang, tidyr
 Suggests: knitr, rmarkdown
 License: GPL-3
 LazyData: true
 URL: https://github.com/JimGrange/mixtur
 BugReports: https://github.com/JimGrange/mixtur/issues
 Encoding: UTF-8
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.1
 Copyright: Some functions have been adapted from Matlab code written by
         Paul Bays (https://bayslab.com) published under GNU General
         Public License.
 NeedsCompilation: no
-Packaged: 2021-07-30 12:41:36 UTC; jimgrange
+Packaged: 2023-04-06 10:15:16 UTC; jimgrange
 Author: Jim Grange [aut, cre] (<https://orcid.org/0000-0002-8352-8390>),
   Stuart B. Moore [aut] (<https://orcid.org/0000-0002-0747-9304>),
   Ed D. J. Berry [ctb]
 Maintainer: Jim Grange <grange.jim@gmail.com>
 Repository: CRAN
-Date/Publication: 2021-08-03 08:00:02 UTC
+Date/Publication: 2023-04-06 11:00:02 UTC
diff --git a/MD5 b/MD5
@@ -1,26 +1,27 @@
-01508f5b8030097d8c49af5e3a35ba1c *DESCRIPTION
-a8b44b5e52cdb8d37884d12ec2dade8f *NAMESPACE
-2e0c6d7e83650da32b481b2b865e78d1 *NEWS.md
+5c8dbfeaecd592744d5f0adcd1bb71d4 *DESCRIPTION
+ac6a31cc73c38ed8a1510f466096c9b6 *NAMESPACE
+7a51616fdf9b9a0743ef237a2572b44d *NEWS.md
 50c6878b4a2fb4bbcd71b294a5ff90f8 *R/analysing.R
 49eb16a1dac8e90ce8533af677cec320 *R/data_descriptions.R
 5fce881fc6e2afd4abf208b538df57f3 *R/designing.R
 77038cc9fbd9c6d230ded50efbadba2c *R/modelling.R
-1860d4496cfb545e1513fadf40ee39a3 *R/plotting.R
-46d221ad15c4c6f4657543f42536af6c *R/simulating.R
+e2be981ec6a6ec0cdfa5f80f5a5c409f *R/plotting.R
+6a117748331b7e9c5545883f37a3978d *R/simulating.R
 0c67f69d88ccb7fe864fa4ccf7f6d216 *R/utilities.R
-e73be97bf7ad0bfc204f2d58e9254867 *README.md
+e89dfceb5578c4c55873a5996ac3d974 *README.md
 3ac8f5ad800a0a2df5b416890ee78435 *data/bays2009_full.rda
 102a1be4952a5ff33d4ba3a2d3854ec5 *data/bays2009_sample.rda
 1b190f0d51ee39e0874b696477e01a0f *data/berry_2019.rda
 74d12c079a320e3e7a9ba7b2a2d8b0f1 *data/oberauer_2017.rda
 90aae499035856c85943670440264ba0 *man/bays2009_full.Rd
 9d63c65b885637b7937ccfad5912aef2 *man/bays2009_sample.Rd
 55d2a2fad360327cabaeabd742d47076 *man/berry_2019.Rd
+f5c6916b452efe9f7b3f3b9624bc54c3 *man/figures/README-pressure-1.png
 c4f11b54d0d6d915c4ff5cea4fb2b457 *man/fit_mixtur.Rd
 8678f518e2df55242ae5966208ac3fdc *man/get_summary_statistics.Rd
 c277741a5ee87778d5bb3640824900df *man/oberauer_2017.Rd
-368d9eef959d15fec86527dc4d0c075b *man/plot_error.Rd
-7fb68a07a7e38abcdc4a0453044036dd *man/plot_model_fit.Rd
-e163b2a76a12ac2dc545f65ee56e8a95 *man/plot_model_parameters.Rd
-4805990e9f3af84e69c9c3e9e8de3226 *man/plot_summary_statistic.Rd
+6bff58a5a61dd2bf1b5e97aedbe8fea2 *man/plot_error.Rd
+e1a5160386f298f66a8a3fdbf48c8180 *man/plot_model_fit.Rd
+063fe1082a0bd8a6fc5ec58d5ecd9004 *man/plot_model_parameters.Rd
+1cb346e5060ccd44677acc5f46b41bbf *man/plot_summary_statistic.Rd
 3a1797a31d33ac019312885210788d91 *man/simulate_mixtur.Rd
diff --git a/NAMESPACE b/NAMESPACE
@@ -21,7 +21,6 @@ importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
 importFrom(dplyr,sample_n)
 importFrom(dplyr,select)
-importFrom(dplyr,slice)
 importFrom(dplyr,starts_with)
 importFrom(dplyr,summarise)
 importFrom(ggplot2,aes)

diff --git a/NEWS.md b/NEWS.md
@@ -2,6 +2,11 @@
 output: html_document
 ---
 
-mixtur 1.2.0
+mixtur v1.2.1
 ===========
-* Initial release to GitHub
+* Fixed issue that RColorBrewer package was imported but never used
+* Fixed issue in simulate_components() function caused by dplyr's slice()
+
+mixtur v1.2.0
+===========
+* Initial release to GitHub & to CRAN
diff --git a/R/plotting.R b/R/plotting.R
@@ -30,7 +30,7 @@
 #'@param return_data A boolean (TRUE or FALSE) indicating whether the data for
 #'the plot should be returned.
 #'@param palette A character stating the preferred colour palette to use. To
-#'see all available palettes, type display.brewer.all() into the console.
+#'see all available palettes, type ?scale_colour_brewer into the console.
 #'
 #'@return If \code{return_data} is set to \code{FALSE} (which it is by default),
 #' the function returns a ggplot2 object visualising the summary statistic
@@ -145,7 +145,7 @@ plot_summary_statistic <- function(data,
 #' @param return_data A boolean (TRUE or FALSE) indicating whether the data for
 #' the plot should be returned.
 #' @param palette A character stating the preferred colour palette to use. To
-#' see all available palettes, type display.brewer.all() into the console.
+#' see all available palettes, type ?scale_colour_brewer into the console.
 #'
 #' @importFrom stats sd
 #' @importFrom dplyr %>%
@@ -698,7 +698,9 @@ plot_mean_absolute_error <- function(data,
 #'@param return_data A boolean (TRUE or FALSE) indicating whether the data for
 #'the plot should be returned.
 #'@param palette A character stating the preferred colour palette to use. To
-#'see all available palettes, type display.brewer.all() into the console.
+#'see all available palettes, type ?scale_colour_brewer into the console.
+#'@param scale_y_axis A vector of 2 elements stating the minimum and maximum
+#'value to use for the y-axis in the plots.
 #'
 #'@return If \code{return_data} is set to \code{FALSE} (which it is by default),
 #' the function returns a ggplot2 object visualising the density distribution
@@ -739,7 +741,8 @@ plot_error <- function(data,
                        n_bins = 18,
                        n_col = 2,
                        return_data = FALSE,
-                       palette = "Dark2"){
+                       palette = "Dark2",
+                       scale_y_axis = NULL){
 
   condition <- NULL
   set_size <- NULL
@@ -880,39 +883,71 @@ plot_error <- function(data,
   # no set size or condition manipulation
   if(is.null(set_size_var) && is.null(condition_var)){
 
-    plot <- ggplot(final_data, aes(x = .data$x,
-                                   y = .data$mean_error)) +
-      geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
-                        ymin = .data$mean_error - .data$se_error),
-                    width = 0.00) +
-      geom_point() +
-      theme_bw() +
-      scale_x_continuous(limits = c(-pi, pi)) +
-      scale_y_continuous(limits = c(0,
-                                    max(final_data$mean_error) +
-                                      max(final_data$se_error))) +
-      labs(x = "Error (Radians)",
-           y = "Probability Density")
+
+    if(is.null(scale_y_axis)){
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(0,
+                                      max(final_data$mean_error) +
+                                        max(final_data$se_error))) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density")
+    } else {
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(scale_y_axis[1], scale_y_axis[2])) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density")
+    }
+
     }
 
 
   # no set size manipulation but there is a condition manipulation
   if(is.null(set_size_var) && !is.null(condition_var)){
 
-    plot <- ggplot(final_data, aes(x = .data$x,
-                                   y = .data$mean_error)) +
-      geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
-                        ymin = .data$mean_error - .data$se_error),
-                    width = 0.00) +
-      geom_point() +
-      theme_bw() +
-      scale_x_continuous(limits = c(-pi, pi)) +
-      scale_y_continuous(limits = c(0,
-                                    max(final_data$mean_error) +
-                                      max(final_data$se_error))) +
-      labs(x = "Error (Radians)",
-           y = "Probability Density") +
-      facet_wrap(vars(.data$condition), ncol = n_col)
+    if(is.null(scale_y_axis)){
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(0,
+                                      max(final_data$mean_error) +
+                                        max(final_data$se_error))) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$condition), ncol = n_col)
+    } else {
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(scale_y_axis[1], scale_y_axis[2])) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$condition), ncol = n_col)
+    }
+
 
     # rename the final_data frame
     colnames(final_data)[1] <- condition_var
@@ -923,20 +958,37 @@ plot_error <- function(data,
   # set size manipulation, but no condition manipulation
   if(!is.null(set_size_var) && is.null(condition_var)){
 
-    plot <- ggplot(final_data, aes(x = .data$x,
-                                   y = .data$mean_error)) +
-      geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
-                        ymin = .data$mean_error - .data$se_error),
-                    width = 0.00) +
-      geom_point() +
-      theme_bw() +
-      scale_x_continuous(limits = c(-pi, pi)) +
-      scale_y_continuous(limits = c(0,
-                                    max(final_data$mean_error) +
+    if(is.null(scale_y_axis)){
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(0,
+                                      max(final_data$mean_error) +
                                       max(final_data$se_error))) +
-      labs(x = "Error (Radians)",
-           y = "Probability Density") +
-      facet_wrap(vars(.data$set_size), ncol = n_col)
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$set_size), ncol = n_col)
+    } else {
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error),
+                      width = 0.00) +
+        geom_point() +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(scale_y_axis[1], scale_y_axis[2])) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$set_size), ncol = n_col)
+    }
+
+
 
     # rename the final_data frame
     colnames(final_data)[1] <- set_size_var
@@ -950,25 +1002,45 @@ plot_error <- function(data,
     # add position jitter to avoid over-plotting
     pd <- position_dodge(0.1)
 
-    plot <- ggplot(final_data, aes(x = .data$x,
-                                   y = .data$mean_error,
-                                   group = .data$condition)) +
-      geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
-                        ymin = .data$mean_error - .data$se_error,
-                        colour = .data$condition),
-                    width = 0.00,
-                    position = pd) +
-      geom_point(aes(colour = .data$condition),
-                 position = pd) +
-      theme_bw() +
-      scale_x_continuous(limits = c(-pi, pi)) +
-      scale_y_continuous(limits = c(0,
-                                    max(final_data$mean_error) +
-                                      max(final_data$se_error))) +
-      scale_colour_brewer(palette = palette, name = condition_var) +
-      labs(x = "Error (Radians)",
-           y = "Probability Density") +
-      facet_wrap(vars(.data$set_size), ncol = n_col)
+    if(is.null(scale_y_axis)){
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error,
+                                     group = .data$condition)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error,
+                          colour = .data$condition),
+                      width = 0.00,
+                      position = pd) +
+        geom_point(aes(colour = .data$condition),
+                   position = pd) +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(0,
+                                      max(final_data$mean_error) +
+                                        max(final_data$se_error))) +
+        scale_colour_brewer(palette = palette, name = condition_var) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$set_size), ncol = n_col)
+    } else {
+      plot <- ggplot(final_data, aes(x = .data$x,
+                                     y = .data$mean_error,
+                                     group = .data$condition)) +
+        geom_errorbar(aes(ymax = .data$mean_error + .data$se_error,
+                          ymin = .data$mean_error - .data$se_error,
+                          colour = .data$condition),
+                      width = 0.00,
+                      position = pd) +
+        geom_point(aes(colour = .data$condition),
+                   position = pd) +
+        theme_bw() +
+        scale_x_continuous(limits = c(-pi, pi)) +
+        scale_y_continuous(limits = c(scale_y_axis[1], scale_y_axis[2])) +
+        labs(x = "Error (Radians)",
+             y = "Probability Density") +
+        facet_wrap(vars(.data$set_size), ncol = n_col)
+    }
+
 
     # rename the final_data frame
     colnames(final_data)[1] <- set_size_var
@@ -1302,7 +1374,7 @@ plot_precision <- function(data,
 #'@param n_col An integer controlling the number of columns in the resulting
 #'plot.
 #'@param palette A character stating the preferred colour palette to use. To
-#'see all available palettes, type display.brewer.all() into the console.
+#'see all available palettes, type ?scale_colour_brewer into the console.
 #'
 #'@return The function returns a ggplot2 object visualising the mean observed
 #' response error density distribution across participants (if applicable)
@@ -2035,7 +2107,7 @@ plot_model_fit <- function(participant_data,
 #'@param return_data A boolean (TRUE or FALSE) indicating whether the data for
 #'the plot should be returned.
 #'@param palette A character stating the preferred colour palette to use. To
-#'see all available palettes, type display.brewer.all() into the console.
+#'see all available palettes, type?scale_colour_brewer into the console.
 #'
 #'@return If \code{return_data} is set to \code{FALSE} (which it is by
 #' default),the function returns a ggplot2 object visualising the mean model