Release 3 12 candidate (#32)

* fix base=10 regardless of input (see #15)

class constructor was always setting base to 10 instead of the input value

* merge bug fix 1.01 into dev (#19)

* bug fix issue #7

Correctly re-order the sample_meta column for colouring samples in the dendrogram plot

* version bump

bug fix issue #7

* fix for #18 (#20)

correctly reorder the factor labels so that the control group always ends up in the denominator for the fold change calculation.

* fix for #18

fixed incorrect length check on matching class labels.

* Issue 17 ttest factor (#21)

* convert to factor if not one already

fix for issue #17

* update roxygen version

* fix for issue #9 (#22)

changed from lapply to vapply and used drop=FALSE to ensure compatibility with a single factor.

* allow user to set lambda (#24)

- lambda changed to input parameter. NULL = uses pmp optimisation
- model_predict now uses the set value of lambda, or lambda_opt if used.
- documentation updated

* Feature non parametric fold change (#26)

* add "median" method

based on DOI: 10.1080/00949650212140 can now calcuate fold changes equivalent to using medians and corresponding confidence intervals

* update documentation

* update median method

now correctly calculates ratio of medians

* use wilcox for paired median intervals

make use of wilcox.test to estimate intervals for the median when using median for paired samples

* Issue 23 filter by name (#27)

* fix for #23

moved all model_apply functionality to model_predict so that model_train and model_predict can be used as well as model_apply

* update documentation

* Update mean_of_medians.R (#29)

fix for #28 
- correctly loop over all levels in the named factor

* Feature documentation 3 12 (#31)

* update documentation

Description and inputs now pulled from the object definitions for consistency.

* fix definition of label_features

allows NULL and description updated

* replace non ascii characters

* export mixed_effect object

* use correct object name to generate documentation

* export mixed_effect object

* remove non ascii characters

* update tests with new object name

* add import for capture.output

* add import for capture.output

* use pca_biplot in tests

chart was renamed

* add utils import

* update struct dependency version

* update documentation

* update news, version bump

Author: grlloyd

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: structToolbox
 Type: Package
 Title: Data processing & analysis tools for Metabolomics and other omics
-Version: 1.1.1
+Version: 1.1.2
 Authors@R: c(
     person(
         c("Gavin","Rhys"),
@@ -104,16 +104,17 @@ Collate:
     'vec_norm_class.R'
     'wilcox_test_class.R'
     'zzz.R'
-Depends: R (>= 4.0), struct (>= 0.99.10)
+Depends: R (>= 4.0), struct (>= 1.1.2)
 Imports: ggplot2,
     ggthemes,
     grid,
     gridExtra,
     methods,
     scales,
     sp,
-    stats
-RoxygenNote: 7.1.0
+    stats,
+    utils
+RoxygenNote: 7.1.1
 Suggests: agricolae,
  BiocFileCache,
  BiocStyle,

NAMESPACE

@@ -5,7 +5,7 @@ export(AUC)
 export(DFA)
 export(DatasetExperiment_boxplot)
 export(DatasetExperiment_dist)
-export(DatasetExperiment_factor_barchart)
+export(DatasetExperiment_factor_boxplot)
 export(DatasetExperiment_heatmap)
 export(HCA)
 export(HSD)
@@ -38,7 +38,7 @@ export(fisher_exact)
 export(fold_change)
 export(fold_change_int)
 export(fold_change_plot)
-export(forward_selection_byrank)
+export(forward_selection_by_rank)
 export(fs_line)
 export(glog_opt_plot)
 export(glog_transform)
@@ -65,7 +65,7 @@ export(mv_sample_filter_hist)
 export(nroot_transform)
 export(pairs_filter)
 export(pareto_scale)
-export(pca_biplot_plot)
+export(pca_biplot)
 export(pca_correlation_plot)
 export(pca_dstat_plot)
 export(pca_loadings_plot)
@@ -117,3 +117,4 @@ import(stats)
 import(struct)
 importFrom(scales,squish)
 importFrom(sp,point.in.polygon)
+importFrom(utils,capture.output)

NEWS

@@ -1,3 +1,13 @@
+Changes 1.1.2
++ Documentation updates
++ Bug fixes
+
+Changes 1.0.1
++ Fix HCA bug
+
+Changes 1.0.0
++ Bioconductor 3.11 release
+
 Changes 0.99.10
 + add Metabolmics BiocView
 + fix missing sections at end of vignette

R/AUC_metric_class.R

@@ -1,19 +1,10 @@
-#' Area under ROC
-#'
-#' Area under ROC calculated by approximating the curve as a series of trapeziums.
-#' Only suitable for 2 classes.
-#' 
-#' @return A metric object with methods for calculating AUC
-#'
+#' @eval get_description('AUC')
 #' @examples
 #' D = iris_DatasetExperiment()
 #' XCV = kfold_xval(folds=5,factor_name='Species') *
 #'       (mean_centre() + PLSDA(number_components=2,factor_name='Species'))
 #' MET = AUC()
 #' XCV = run(XCV,D,MET)
-#'
-#' @param ... additional slots and values passed to struct_class
-#' @return struct object
 #' @export AUC
 AUC = function(...) {
     out=struct::new_struct('AUC',...)
@@ -23,9 +14,12 @@ AUC = function(...) {
 
 .AUC<-setClass(
     "AUC",
-    contains='metric',
-    prototype = list(name='Area under ROC',
-        type="classification"
+    contains=c('metric','stato'),
+    prototype = list(name='Area under ROC curve',
+        description=paste0("The area under the ROC curve of a classifier is ",
+            "estimated using the trapezoid method."),
+        type="classification",
+        stato_id="STATO:0000209"
     )
 )
 

R/DFA_class.R

@@ -1,10 +1,4 @@
-#' Discriminant Factor Analysis (DFA)
-#'
-#' Applies Discriminant Factor Analysis to a dataset.
-#' @param factor_name The sample_meta column name containing group labels
-#' @param number_components The number of discriminant factors to calculate
-#' @param ... additional slots and values passed to struct_class
-#' @return struct object
+#' @eval get_description('DFA')
 #' @export DFA
 #' @include entity_objects.R
 #' @examples
@@ -24,22 +18,42 @@ DFA = function(factor_name,number_components=2,...) {
     "DFA",
     contains = c('model'),
     slots=c(
-        number_components='numeric',
+        number_components='entity',
         factor_name='entity',
         scores='DatasetExperiment',
         loadings='data.frame',
         eigenvalues='data.frame',
         that='DatasetExperiment'
     ),
-
+    
     prototype=list(name = 'Discriminant Factor Analysis',
-        description = 'Applies DFA to a data matrix.',
+        description = paste0('Discriminant Factor Analysis (DFA) is a ',
+            'supervised classification method. Using a linear combination of the ',
+            'input variables, DFA finds new orthogonal axes (canonical values) ',
+            'to minimize the variance within each given class and maximize ',
+            'variance between classes.'),
         type = 'classification',
         predicted = 'that',
+        citations=list(
+            bibentry(
+                bibtype='Book',
+                title='Multivariate Statistical Methods: A Primer',
+                author=as.person('B.F.J. Manly'),
+                year='1986',
+                publisher='Chapman and Hall',
+                address='Boca Raton'
+            )
+        ),
         .params=c('factor_name','number_components'),
         .outputs=c('scores','loadings','eigenvalues','that'),
-
-        factor_name=ents$factor_name
+        
+        factor_name=ents$factor_name,
+        
+        number_components=entity(value = 2,
+            name = 'Number of components',
+            description = 'The number of DFA components calculated.',
+            type = c('numeric','integer')
+        )
 
     )
 )
@@ -84,11 +98,11 @@ setMethod(f="model_train",
 
         # projection
         ev = eigen(P)
-
+        
         # handle imaginary values (should be vv small)
         ev$values=Re(ev$values)
         ev$vectors=Re(ev$vectors)
-
+        
         # reduce to number of desired components
         ev$values=ev$values[1:M$number_components]
         ev$vectors=ev$vectors[,1:M$number_components]
@@ -122,7 +136,7 @@ setMethod(f="model_train",
 setMethod(f="model_predict",
     signature=c("DFA","DatasetExperiment"),
     definition=function(M,D) {
-
+        
         X=as.matrix(D$data)
         P=output_value(M,'loadings')
         that=X%*%as.matrix(P)
@@ -141,25 +155,8 @@ setMethod(f="model_predict",
 )
 
 
-#' dfa_scores_plot class
-#'
-#' 2d scatter plot of discriminant factor scores.
-#'
+#' @eval get_description('dfa_scores_plot')
 #' @import struct
-#' @param components The discriminant factors to plot (\code{numeric(2)})
-#' @param points_to_label "none", "all", or "outliers" will be labelled on the plot.
-#' @param factor_name The sample_meta column name to use for colouring the points.
-#' You can provide up to two factors for this plot.
-#' @param ellipse "all" will plot all ellipses, "group" will only plot group ellipses,
-#' "none" will not plot any ellipses and "sample" will plot ellipse for all samples (ignoring group).
-#' @param label_filter Only include labels for samples in the group specified by label_filter.
-#' If zero length then all labels will be included.
-#' @param label_factor The sample_meta column to use for labelling the samples.
-#' If 'rownames' then the rownames will be used.
-#' @param label_size The text size of the labels.NB ggplot units, not font size units.
-#' Default 3.88.
-#' @param ... additional slots and values passed to struct_class
-#' @return struct object
 #' @export dfa_scores_plot
 #' @include DFA_class.R
 #' @examples
@@ -206,54 +203,67 @@ dfa_scores_plot = function(
     ),
 
     prototype = list(name='DFA scores plot',
-        description='Plots a 2d scatter plot of the selected components',
+        description='A scatter plot of the selected DFA components.',
         type="scatter",
-        .params=c('components','points_to_label','factor_name','ellipse','label_filter','label_factor','label_size'),
+        libraries=c('scales','ggplot2'),
+        .params=c('components','points_to_label','factor_name','ellipse',
+            'label_filter','label_factor','label_size'),
 
         components=entity(name='Components to plot',
             value=c(1,2),
             type='numeric',
-            description='the components to be plotted e.g. c(1,2) plots component 1 on the x axis and component 2 on the y axis.',
+            description=paste0('The components selected for plotting.'),
             max_length=2
         ),
 
-        points_to_label=enum(name='points_to_label',
+        points_to_label=enum(name='Points to label',
             value='none',
             type='character',
-            description='("none"), "all", or "outliers" will be labelled on the plot.',
+            description=c(
+                'none' = 'No samples labels are displayed.', 
+                "all" = 'The labels for all samples are displayed.', 
+                "outliers" = 'Labels for for potential outlier samples are displayed.'
+            ),
             allowed=c('none','all','outliers')
         ),
-        factor_name=entity(name='Factor name',
-            value='factor',
-            type='character',
-            description='The column name of sample meta to use for plotting. A second column can be included to plot using symbols.',
-            max_length=2
-        ),
-        ellipse=enum(name = 'Plot ellipses',description=c(
-            '"all" will plot all ellipses',
-            '"group" will only plot group ellipses',
-            '"none" will not plot any ellipses',
-            '"sample" will plot ellipse for all samples (ignoring group)'),
+        factor_name=ents$factor_name,
+        ellipse=enum(
+            name = 'Plot ellipses',
+            description=c(
+                "all" = paste0('Hotelling T2 95\\% ellipses are plotted for all groups and all samples.'),
+                "group" = 'Hotelling T2 95\\% ellipses are plotted for all groups.',
+                "none" = 'Ellipses are not included on the plot.',
+                "sample" = 'A Hotelling T2 95\\% ellipse is plotted for all samples (ignoring group)'),
             allowed=c('all','group','none','sample'),
-            value='all'),
-        label_filter=entity(name='Label filter',
+            value='all'
+        ),
+        label_filter=entity(
+            name='Label filter',
             value=character(0),
             type='character',
-            description='Only include the param.group labels included in label_filter. If zero length then all labels will be included.'
+            description=paste0(
+                'Labels are only plotted for the named groups. If ',
+                'zero-length then all groups are included.'
+            )
         ),
         label_factor=entity(name='Factor for labels',
-            description='The column name of sample_meta to use as labels. "rownames" will use the row names from sample_meta.',
+            description=paste0('The column name of sample_meta to use for ',
+                'labelling samples on the plot. "rownames" will use the row ',
+                'names from sample_meta.'),
             type='character',
-            value='rownames'),
+            value='rownames',
+            max_length=1),
         label_size=entity(name='Text size of labels',
-            description='The text size of labels. Note this is not in Font Units. Default 3.88.',
+            description='The text size of labels. Note this is not in Font Units.',
             type='numeric',
-            value=3.88)
+            value=3.88,
+            max_length=1)
     )
 )
 
 
 
+
 #' @importFrom sp point.in.polygon
 #' @import ggplot2
 #' @importFrom scales squish
@@ -269,7 +279,7 @@ setMethod(f="chart_plot",
         }
         opt=param_list(obj)
         scores=output_value(dobj,'scores')$data
-
+        
         if (length(obj$factor_name)==1) {
             shapes = 19 # filled circles for all samples
         } else {