Bioconductor Code: structToolbox

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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 https://github.com/computational-metabolomics/structToolbox/issues/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 https://github.com/computational-metabolomics/structToolbox/issues/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

Gavin Rhys Lloyd authored on 25/10/2020 08:50:13 • GitHub committed on 25/10/2020 08:50:13
Showing 1 changed files

@@ -1,14 +1,5 @@
                                     -#'linear model class
                                     -#'
                                     -#' Wrapper for R lm. See \code{lm} for details.
                                     -#'
                                     +#' @eval get_description('linear_model')
                                      #' @import struct
                                     -#' @param formula The formula to use.
                                     -#' @param na_action The action to take when missing values are present. Can any
                                     -#' one of 'na.omit','na.fail','na.exclude' or 'na.pass'. Default is 'na.omit'.
                                     -#' @param contrasts The contrasts for this model. If zero length then the default contrasts are used.
                                     -#' @param ... additional slots and values passed to struct_class
                                     -#' @return struct object
                                      #' @export linear_model
                                      #' @examples
                                      #' D = iris_DatasetExperiment()
@@ -43,27 +34,30 @@ linear_model = function(formula,na_action='na.omit',contrasts=list(),...) {
                                              adj_r_squared='entity'
                                          ),
                                     -    prototype = list(name='Linear Model',
                                     -        description='Used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance.',
                                     +    prototype = list(name='Linear model',
                                     +        description=paste0(
                                     +            'Linear models can be used to carry out ',
                                     +            'regression, single stratum analysis of variance and analysis ',
                                     +            'of covariance.'),
                                              type="regression",
                                              predicted='predicted_values',
                                              .params=c('formula','na_action','contrasts'),
                                              .outputs=c('lm','coefficients','residuals','fitted_values','predicted_values','r_squared','adj_r_squared'),
+                                    -
                                     -        formula=entity(name='Model Formula',
                                     -            description='Compact symbolic form of the equation to be fitted using a linear model_',
                                     -            value=y~x,
                                     -            type='formula',
                                     -            max_length=Inf
                                     -        ),
                                     -        na_action=enum(name='NA Action',
                                     -            description='The action to be taken when encoutering NA',
                                     +        libraries='stats',
                                     +        formula=ents$formula,
                                     +        na_action=enum(name='NA action',
                                     +            description=c(
                                     +                'na.omit' = 'Incomplete cases are removed.',
                                     +                'na.fail' = 'An error is thrown if NA are present.',
                                     +                'na.exclude'='Incomplete cases are removed, and the output result is padded to the correct size using NA.',
                                     +                'na.pass' = 'Does not apply a linear model if NA are present.'
                                     +            ),
                                                  value='na.omit',
                                                  type='character',
                                                  allowed=c('na.omit','na.fail','na.exclude','na.pass')
                                              ),
                                              contrasts=entity(name='Contrasts',
                                     -            description='The contrasts associated with a factor. If zero length then the default contrasts are used.',
                                     +            description='The contrasts associated with a factor.',
                                                  type='list'
                                              ),

@@ -12,10 +12,10 @@
                                      #' @export linear_model
                                      #' @examples
                                      #' M = linear_model()
                                     -linear_model = function(formula,na_action='na_omit',contrasts=list(),...) {
                                     +linear_model = function(formula,na_action='na.omit',contrasts=list(),...) {
                                          out=struct::new_struct('linear_model',
                                              formula=formula,
                                     -        na_action=nna_action,
                                     +        na_action=na_action,
                                              contrasts=contrasts,
                                              ...)
                                          return(out)

@@ -3,7 +3,7 @@
                                      #' wrapper for R lm.
                                      #'
                                      #' @import struct
                                     -#' @param ... slots and values for the new object
                                     +#' @param ... additional slots and values passed to struct_class
                                      #' @return struct object
                                      #' @export linear_model
                                      #' @examples
@@ -87,7 +87,7 @@ linear_model = function(...) {
+                                         )
+                                     )
                                     -#' @param ... slots and values for the new object
                                     +#' @param ... additional slots and values passed to struct_class
                                      #' @export
                                      #' @template model_train
                                      setMethod(f="model_train",
@@ -111,7 +111,7 @@ setMethod(f="model_train",
+                                         }
+                                     )
                                     -#' @param ... slots and values for the new object
                                     +#' @param ... additional slots and values passed to struct_class
                                      #' @export
                                      #' @template model_predict
                                      setMethod(f="model_predict",

@@ -10,7 +10,7 @@
                                      #' M = linear_model()
                                      linear_model = function(...) {
                                          out=.linear_model()
                                     -    out=struct::.initialize_struct_class(out,...)
                                     +    out=struct::new_struct(out,...)
                                          return(out)
+                                     }
@@ -20,18 +20,18 @@ linear_model = function(...) {
                                          contains='model',
                                          slots=c(
                                              # INPUTS
                                     -        params_formula='entity',
                                     -        params_na_action='enum',
                                     -        params_contrasts='entity',
                                     +        formula='entity',
                                     +        na_action='enum',
                                     +        contrasts='entity',
                                              # OUTPUTS
                                     -        outputs_lm='entity',
                                     -        outputs_coefficients='entity',
                                     -        outputs_residuals='entity',
                                     -        outputs_fitted_values='entity',
                                     -        outputs_predicted_values='entity',
                                     -        outputs_r_squared='entity',
                                     -        outputs_adj_r_squared='entity'
                                     +        lm='entity',
                                     +        coefficients='entity',
                                     +        residuals='entity',
                                     +        fitted_values='entity',
                                     +        predicted_values='entity',
                                     +        r_squared='entity',
                                     +        adj_r_squared='entity'
                                          ),
                                          prototype = list(name='Linear Model',
@@ -39,48 +39,48 @@ linear_model = function(...) {
                                              type="regression",
                                              predicted='predicted_values',
                                     -        params_formula=entity(name='Model Formula',
                                     +        formula=entity(name='Model Formula',
                                                  description='Compact symbolic form of the equation to be fitted using a linear model_',
                                                  value=y~x,
                                                  type='formula',
                                                  max_length=Inf
                                              ),
                                     -        params_na_action=enum(name='NA Action',
                                     +        na_action=enum(name='NA Action',
                                                  description='The action to be taken when encoutering NA',
                                                  value='na.omit',
                                                  type='character',
                                                  allowed=c('na.omit','na.fail','na.exclude','na.pass')
                                              ),
                                     -        params_contrasts=entity(name='Contrasts',
                                     +        contrasts=entity(name='Contrasts',
                                                  description='The contrasts associated with a factor. If zero length then the default contrasts are used.',
                                                  type='list'
                                              ),
                                     -        outputs_lm=entity(name='Linear model object',
                                     +        lm=entity(name='Linear model object',
                                                  description='The lm object for this model_',
                                                  type='lm'
                                              ),
                                     -        outputs_coefficients=entity(name='Model coefficients',
                                     +        coefficients=entity(name='Model coefficients',
                                                  description='The coefficients for the fitted model_',
                                                  type='numeric'
                                              ),
                                     -        outputs_residuals=entity(name='Residuals',
                                     +        residuals=entity(name='Residuals',
                                                  description='The residuals for the fitted model_',
                                                  type='numeric'
                                              ),
                                     -        outputs_fitted_values=entity(name='Fitted values',
                                     +        fitted_values=entity(name='Fitted values',
                                                  description='The fitted values for the data used to train the model_',
                                                  type='numeric'
                                              ),
                                     -        outputs_predicted_values=entity(name='Predicted values',
                                     +        predicted_values=entity(name='Predicted values',
                                                  description='The predicted values for new data using the fitted model_',
                                                  type='numeric'
                                              ),
                                     -        outputs_r_squared=entity(name='R Squared',
                                     +        r_squared=entity(name='R Squared',
                                                  description='The value of R Squared for the fitted model_',
                                                  type='numeric'
                                              ),
                                     -        outputs_adj_r_squared=entity(name='Adjusted R Squared',
                                     +        adj_r_squared=entity(name='Adjusted R Squared',
                                                  description='The value ofAdjusted  R Squared for the fitted model_',
                                                  type='numeric'
+                                             )

@@ -102,6 +102,7 @@ setMethod(f="model.train",
+                                     )
                                      #' @export
                                     +#' @template model_predict
                                      setMethod(f="model.predict",
                                          signature=c("linear_model",'dataset'),
                                          definition=function(M,D)

@@ -20,7 +20,9 @@ linear_model<-setClass(
                                              outputs.coefficients='entity',
                                              outputs.residuals='entity',
                                              outputs.fitted_values='entity',
                                     -        outputs.predicted_values='entity'
                                     +        outputs.predicted_values='entity',
                                     +        outputs.r_squared='entity',
                                     +        outputs.adj_r_squared='entity'
                                          ),
                                          prototype = list(name='Linear Model',
@@ -64,6 +66,14 @@ linear_model<-setClass(
                                              outputs.predicted_values=entity(name='Predicted values',
                                                  description='The predicted values for new data using the fitted model.',
                                                  type='numeric'
                                     +        ),
                                     +        outputs.r_squared=entity(name='R Squared',
                                     +            description='The value of R Squared for the fitted model.',
                                     +            type='numeric'
                                     +        ),
                                     +        outputs.adj_r_squared=entity(name='Adjusted R Squared',
                                     +            description='The value ofAdjusted  R Squared for the fitted model.',
                                     +            type='numeric'
+                                             )
+                                         )
+                                     )
@@ -82,6 +92,10 @@ setMethod(f="model.train",
                                              M$coefficients=coefficients(M$lm)
                                              M$residuals=residuals(M$lm)
                                              M$fitted_values=fitted(M$lm)
+                                    +
                                     +        M$r_squared=summary(M$lm)$r.squared
                                     +        M$adj_r_squared=summary(M$lm)$adj.r.squared
+                                    +
                                              return(M)
+                                         }
+                                     )

@@ -41,7 +41,7 @@ linear_model<-setClass(
                                                  list=c('na.omit','na.fail','na.exclude','na.pass')
                                              ),
                                              params.contrasts=entity(name='Contrasts',
                                     -            description='The contrasts associated with a factor. If NULL then the default contrasts are used.',
                                     +            description='The contrasts associated with a factor. If zero length then the default contrasts are used.',
                                                  type='list'
                                              ),
@@ -74,7 +74,7 @@ setMethod(f="model.train",
                                          definition=function(M,D)
+                                         {
                                              X=cbind(D$data,D$sample_meta)
                                     -        if (is.null(M$contrasts)) {
                                     +        if (length(M$contrasts)==0) {
                                                  M$lm=lm(formula = M$formula, na.action = M$na_action,data=X) # default contrasts
                                              } else {
                                                  M$lm=lm(formula = M$formula, na.action = M$na_action, contrasts = M$contrasts,data=X)

Release 3 12 candidate (#32)

fix/update examples

fix/update tests

update to use new struct class constructors

incremental changes to use struct class constructors

remove all params_ and outputs_ tags

change "list" to "allowed" for enums

add @return to documentation

fix broken tests and...

use class contructors and...

update documentation and roxygen templates

use model_train roxygen template

add r2 to outputs

stricter typing of slots for entity objects

update 'type' and matching 'value' for prototype entity objects

update documentation and minor bioccheck fixes

change indentation for biocCheck

initial commit