Fixed dependency break on scales (default behaviour in hue_pal raises

error now)

Author: ntarabelloni

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: roahd
 Type: Package
 Title: Robust Analysis of High Dimensional Data
-Version: 1.4
-Date: 2018-04-11
+Version: 1.4.1
+Date: 2018-08-18
 Authors@R: c( person("Nicholas", "Tarabelloni", role = c("aut", "cre"),
                      email = "[email protected]"),
               person("Ana", "Arribas-Gil", role = "aut",

NEWS.md

@@ -6,37 +6,15 @@ Here's a list of what is changed in this update of __roahd__:
 
 #### Major upgrades
 
-1) Extended Spearman's correlation coefficient computation for multivariate datasets with more than two 
-components.
-
-2) Added bootstrap-based computation of Spearman's correlation coefficient bias and standard deviation.
-
-3) Added methods to provide bootstrap-based confidence intervals on Spearman's coefficients for two 
-univariate functional datasets or a multivairate functional dataset.
-
-4) Added a bootstrap-based test on Spearman's correlation coefficient for two multivariate functional datasets.
-
-5) Added an outliergram version (without graphical display of original data) of multivariate functional datasets.
-
-6) Added example multivariate functional datasets of ECG signals.
-
 #### Minor updates
 
-1) Added two convenience functions to append compatible functional datasets (univariate or multivariate).
-
-2) Added a [-operator overload for multivariate functional dataset representation __mfData__.
-
-
 ### Fixes
 
-#### Major fixes
+Fixed dependency error on a new version of `scales` package that breaks the use of multivariate fbplot in the corner-case of zero
+outliers.
 
-1) Fixed bug in cor_spearman function. Now the standard spearman correlation is not computed on ranks of MHI/MEI, but on
-MHI/MEI itself. The difference is very small, but allows for full reproducibility of the results in the original paper.
+#### Major fixes
 
 #### Minor fixes 
 
-1) Fixed typos in doc
-
-2) Standardised formulas for the application of F inflations in outliergram and boxplot
 

R/fbplot.R

@@ -352,8 +352,15 @@ fbplot.fData = function( Data,
 
     col_non_outlying = set_alpha( col_non_outlying, 0.5 )
 
-    col_outlying = scales::hue_pal( h = c( - 90, 180  ),
-                                    c = 150 )( length( ID_out ) )
+    if( length( ID_out ) > 0 )
+    {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( length( ID_out ) )
+    } else {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( 1 )
+    }
+
     col_envelope = set_alpha( 'blue', alpha = 0.4 )
     col_center = set_alpha( 'blue', alpha = 1 )
     col_fence_structure = 'darkblue'
@@ -568,8 +575,15 @@ provided in the multivariate version of the functional boxplot' )
 
     col_non_outlying = set_alpha( col_non_outlying, 0.5 )
 
-    col_outlying = scales::hue_pal( h = c( - 90, 180  ),
-                                    c = 150 )( length( ID_out ) )
+    if( length( ID_out ) > 0 )
+    {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( length( ID_out ) )
+    } else {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( 1 )
+    }
+
     col_envelope = set_alpha( 'blue', alpha = 0.4 )
     col_center = set_alpha( 'blue', alpha = 1 )
     col_fence_structure = 'darkblue'

R/outliergram.R

@@ -295,8 +295,17 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05,
 
     col_non_outlying = set_alpha( col_non_outlying, 0.5 )
 
-    col_outlying = scales::hue_pal( h = c( - 90, 180  ),
-                                    c = 150 )( length( out$ID_SO ) )
+    if( length( out$ID_SO ) > 0 )
+    {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( length( out$ID_SO ) )
+
+    } else
+    {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( 1 )
+
+    }
 
     dev.cur()
     par( mfrow = c( 1, 2 ) )
@@ -613,8 +622,15 @@ multivariate_outliergram = function( mfData,
 
     col_non_outlying = set_alpha( col_non_outlying, 0.5 )
 
-    col_outlying = scales::hue_pal( h = c( - 90, 180  ),
-                                    c = 150 )( length( out$ID_SO ) )
+    if( length( out$ID_SO ) > 0 )
+    {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( length( out$ID_SO ) )
+
+    } else {
+      col_outlying = scales::hue_pal( h = c( - 90, 180  ),
+                                      c = 150 )( 1 )
+    }
 
     dev.cur()
     # Plotting outliergram