Fixed styler and prepare for CRAN

DESCRIPTION

@@ -1,10 +1,11 @@
 Package: simmr
 Type: Package
 Title: A Stable Isotope Mixing Model
-Version: 0.4.3.9000
-Date: 2020-11-10
+Version: 0.4.4
+Date: 2021-01-21
 Author: Andrew Parnell
-URL: https://github.com/andrewcparnell/simmr
+URL: https://github.com/andrewcparnell/simmr, http://andrewcparnell.github.io/simmr
+BugReports: https://github.com/andrewcparnell/simmr/issues
 Language: en-US
 Maintainer: Andrew Parnell <[email protected]>
 Description: Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load and simmr_mcmc. The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.

NEWS.md

@@ -1,7 +1,8 @@
-# simmr 0.4.3.9000
+# simmr 0.4.4
 
   - Updated the simmr_elicit function to provide a more explicit warning for bad input objects
   - Updated compare_sources and compare_groups to allow exporting of the plot object for editing purposes
+  - Used styler to correct code style
 
 # simmr 0.4.3
 

R/combine_sources.R

@@ -31,14 +31,18 @@
 #' data(geese_data)
 #'
 #' # Load into simmr
-#' simmr_1 = with(geese_data_day1,
-#'                simmr_load(mixtures=mixtures,
-#'                           source_names=source_names,
-#'                           source_means=source_means,
-#'                           source_sds=source_sds,
-#'                           correction_means=correction_means,
-#'                           correction_sds=correction_sds,
-#'                           concentration_means = concentration_means))
+#' simmr_1 <- with(
+#'   geese_data_day1,
+#'   simmr_load(
+#'     mixtures = mixtures,
+#'     source_names = source_names,
+#'     source_means = source_means,
+#'     source_sds = source_sds,
+#'     correction_means = correction_means,
+#'     correction_sds = correction_sds,
+#'     concentration_means = concentration_means
+#'   )
+#' )
 #'
 #' # Plot
 #' plot(simmr_1)
@@ -47,117 +51,121 @@
 #' simmr_1
 #'
 #' # MCMC run
-#' simmr_1_out = simmr_mcmc(simmr_1)
+#' simmr_1_out <- simmr_mcmc(simmr_1)
 #'
 #' # Print it
 #' print(simmr_1_out)
 #'
 #' # Summary
 #' summary(simmr_1_out)
-#' summary(simmr_1_out,type='diagnostics')
-#' summary(simmr_1_out,type='correlations')
-#' summary(simmr_1_out,type='statistics')
-#' ans = summary(simmr_1_out,type=c('quantiles','statistics'))
+#' summary(simmr_1_out, type = "diagnostics")
+#' summary(simmr_1_out, type = "correlations")
+#' summary(simmr_1_out, type = "statistics")
+#' ans <- summary(simmr_1_out, type = c("quantiles", "statistics"))
 #'
 #' # Plot
 #' plot(simmr_1_out)
-#' plot(simmr_1_out,type='boxplot')
-#' plot(simmr_1_out,type='histogram')
-#' plot(simmr_1_out,type='density')
-#' plot(simmr_1_out,type='matrix')
+#' plot(simmr_1_out, type = "boxplot")
+#' plot(simmr_1_out, type = "histogram")
+#' plot(simmr_1_out, type = "density")
+#' plot(simmr_1_out, type = "matrix")
 #'
-#' simmr_out_combine = combine_sources(simmr_1_out,
-#' to_combine=c('Source A','Source D'),
-#' new_source_name='Source A+D')
+#' simmr_out_combine <- combine_sources(simmr_1_out,
+#'   to_combine = c("Source A", "Source D"),
+#'   new_source_name = "Source A+D"
+#' )
 #' plot(simmr_out_combine$input)
-#' plot(simmr_out_combine,type='boxplot',title='simmr output: combined sources')
-#'
+#' plot(simmr_out_combine, type = "boxplot", title = "simmr output: combined sources")
 #' }
 #'
-#' @export 
-combine_sources = function(simmr_out,
-                           to_combine = simmr_out$input$source_names[1:2],
-                           new_source_name = 'combined_source') {
-   UseMethod('combine_sources')
+#' @export
+combine_sources <- function(simmr_out,
+                            to_combine = simmr_out$input$source_names[1:2],
+                            new_source_name = "combined_source") {
+  UseMethod("combine_sources")
 }
-#' @export 
-combine_sources.simmr_output = function(simmr_out,
-                           to_combine = simmr_out$input$source_names[1:2],
-                           new_source_name = 'combined_source') {
+#' @export
+combine_sources.simmr_output <- function(simmr_out,
+                                         to_combine = simmr_out$input$source_names[1:2],
+                                         new_source_name = "combined_source") {
   # A posteriori combining of sources
-  
+
   # Check only two sources to be combined
-  if (length(to_combine) != 2)
+  if (length(to_combine) != 2) {
     stop("Currently only two sources can be combined")
-
+  }
+
   # # Check class
   # if (class(simmr_out) != 'simmr_output')
   #   stop("Only objects of class simmr_output can be run through this function")
-  
+
   # Find which columns to combine by number
-  to_combine_cols = sort(match(to_combine, simmr_out$input$source_names))
-  if (any(is.na(to_combine_cols)))
-    stop('1 or more source names not found')
-
-  simmr_new_out = simmr_out
-
+  to_combine_cols <- sort(match(to_combine, simmr_out$input$source_names))
+  if (any(is.na(to_combine_cols))) {
+    stop("1 or more source names not found")
+  }
+
+  simmr_new_out <- simmr_out
+
   # 1 combine the chosen source means
-  old_source_means = simmr_out$input$source_means
-  simmr_new_out$input$source_means = old_source_means[-to_combine_cols[2], ,
-                                                      drop = FALSE]
-  simmr_new_out$input$source_means[to_combine_cols[1], ] = apply(old_source_means[to_combine_cols, ,drop = FALSE], 2, 'mean')
-
+  old_source_means <- simmr_out$input$source_means
+  simmr_new_out$input$source_means <- old_source_means[-to_combine_cols[2], ,
+    drop = FALSE
+  ]
+  simmr_new_out$input$source_means[to_combine_cols[1], ] <- apply(old_source_means[to_combine_cols, , drop = FALSE], 2, "mean")
+
   # 2 combine the source sds
-  old_source_sds = simmr_out$input$source_sds
-  simmr_new_out$input$source_sds = old_source_sds[-to_combine_cols[2], ,drop= FALSE]
-  simmr_new_out$input$source_sds[to_combine_cols[1], ] = apply(old_source_sds[to_combine_cols, , drop = FALSE], 2, function(x)
-    sqrt(sum(x ^ 2)))
-
+  old_source_sds <- simmr_out$input$source_sds
+  simmr_new_out$input$source_sds <- old_source_sds[-to_combine_cols[2], , drop = FALSE]
+  simmr_new_out$input$source_sds[to_combine_cols[1], ] <- apply(old_source_sds[to_combine_cols, , drop = FALSE], 2, function(x) {
+    sqrt(sum(x^2))
+  })
+
   # 3 combine the correction means
-  old_correction_means = simmr_out$input$correction_means
-  simmr_new_out$input$correction_means = old_correction_means[-to_combine_cols[2], ,drop = FALSE]
-  simmr_new_out$input$correction_means[to_combine_cols[1], ] = apply(old_correction_means[to_combine_cols, ,drop = FALSE], 2, 'mean')
-  
+  old_correction_means <- simmr_out$input$correction_means
+  simmr_new_out$input$correction_means <- old_correction_means[-to_combine_cols[2], , drop = FALSE]
+  simmr_new_out$input$correction_means[to_combine_cols[1], ] <- apply(old_correction_means[to_combine_cols, , drop = FALSE], 2, "mean")
+
   # 4 combine the correction sds
-  old_correction_sds = simmr_out$input$correction_sds
-  simmr_new_out$input$correction_sds = old_correction_sds[-to_combine_cols[2], ,drop = FALSE]
-  simmr_new_out$input$correction_sds[to_combine_cols[1], ] = apply(old_correction_sds[to_combine_cols, , drop = FALSE], 2, function(x)
-    sqrt(sum(x ^ 2)))
-
+  old_correction_sds <- simmr_out$input$correction_sds
+  simmr_new_out$input$correction_sds <- old_correction_sds[-to_combine_cols[2], , drop = FALSE]
+  simmr_new_out$input$correction_sds[to_combine_cols[1], ] <- apply(old_correction_sds[to_combine_cols, , drop = FALSE], 2, function(x) {
+    sqrt(sum(x^2))
+  })
+
   # 5 combine the concentraion means
-  old_concentration_means = simmr_out$input$concentration_means
-  simmr_new_out$input$concentration_means = old_concentration_means[-to_combine_cols[2], ,drop = FALSE]
-  simmr_new_out$input$concentration_means[to_combine_cols[1], ] = apply(old_concentration_means[to_combine_cols, ,drop = FALSE], 2, 'mean')
-  
+  old_concentration_means <- simmr_out$input$concentration_means
+  simmr_new_out$input$concentration_means <- old_concentration_means[-to_combine_cols[2], , drop = FALSE]
+  simmr_new_out$input$concentration_means[to_combine_cols[1], ] <- apply(old_concentration_means[to_combine_cols, , drop = FALSE], 2, "mean")
+
   # 6 change the source names
-  old_source_names = simmr_out$input$source_names
-  simmr_new_out$input$source_names = old_source_names[-to_combine_cols[2]]
-  simmr_new_out$input$source_names[to_combine_cols[1]] = new_source_name
-  
+  old_source_names <- simmr_out$input$source_names
+  simmr_new_out$input$source_names <- old_source_names[-to_combine_cols[2]]
+  simmr_new_out$input$source_names[to_combine_cols[1]] <- new_source_name
+
   # 7 Change n_sources
-  simmr_new_out$input$n_sources = simmr_new_out$input$n_sources - 1
-  
+  simmr_new_out$input$n_sources <- simmr_new_out$input$n_sources - 1
+
   # 8 Sum across all the output values
-  n_groups = simmr_out$input$n_groups
+  n_groups <- simmr_out$input$n_groups
   for (j in 1:n_groups) {
-    simmr_new_out$output[[j]] = simmr_out$output[[j]]
+    simmr_new_out$output[[j]] <- simmr_out$output[[j]]
     # Change sims.list and sims.matrix
     # First sims.matrix
-    sims.matrix = simmr_out$output[[j]]$BUGSoutput$sims.matrix
-    new_sims.matrix = sims.matrix[,-(to_combine_cols[2]+1)]
-    new_sims.matrix[,to_combine_cols[1]+1] = sims.matrix[,to_combine_cols[1]+1] + sims.matrix[,to_combine_cols[2]+1]
-    colnames(new_sims.matrix)[to_combine_cols[1]+1] = new_source_name
-    simmr_new_out$output[[j]]$BUGSoutput$sims.matrix = new_sims.matrix
+    sims.matrix <- simmr_out$output[[j]]$BUGSoutput$sims.matrix
+    new_sims.matrix <- sims.matrix[, -(to_combine_cols[2] + 1)]
+    new_sims.matrix[, to_combine_cols[1] + 1] <- sims.matrix[, to_combine_cols[1] + 1] + sims.matrix[, to_combine_cols[2] + 1]
+    colnames(new_sims.matrix)[to_combine_cols[1] + 1] <- new_source_name
+    simmr_new_out$output[[j]]$BUGSoutput$sims.matrix <- new_sims.matrix
     # Now sims.list
-    sims.list = simmr_out$output[[j]]$BUGSoutput$sims.list
-    new_sims.list = sims.list
-    new_sims.list$p = sims.list$p[,-to_combine_cols[2]]
-    new_sims.list$p[,to_combine_cols[1]] = sims.list$p[,to_combine_cols[2]] + sims.list$p[,to_combine_cols[1]]
-    colnames(new_sims.list$p)[to_combine_cols[1]] = new_source_name
-    simmr_new_out$output[[j]]$BUGSoutput$sims.list = new_sims.list
+    sims.list <- simmr_out$output[[j]]$BUGSoutput$sims.list
+    new_sims.list <- sims.list
+    new_sims.list$p <- sims.list$p[, -to_combine_cols[2]]
+    new_sims.list$p[, to_combine_cols[1]] <- sims.list$p[, to_combine_cols[2]] + sims.list$p[, to_combine_cols[1]]
+    colnames(new_sims.list$p)[to_combine_cols[1]] <- new_source_name
+    simmr_new_out$output[[j]]$BUGSoutput$sims.list <- new_sims.list
   }
-  
-  class(simmr_new_out) = 'simmr_output'
+
+  class(simmr_new_out) <- "simmr_output"
   return(simmr_new_out)
-
 }