- due to changes in struct (removal of unicode)
Gavin Rhys Lloyd authored on 02/02/2024 10:50:50
| ... | ... |
@@ -42,7 +42,7 @@ This object makes use of functionality from the following packages:\itemize{ \i
|
| 42 | 42 |
\section{Inheritance}{
|
| 43 | 43 |
|
| 44 | 44 |
A \code{PLSDA} object inherits the following \code{struct} classes: \cr\cr
|
| 45 |
-\code{PLSDA()} ⭢ \code{PLSR()} ⭢ \code{model()} ⭢ \code{struct_class()}
|
|
| 45 |
+\verb{[PLSDA]} >> \verb{[PLSR]} >> \verb{[model]} >> \verb{[struct_class]}
|
|
| 46 | 46 |
} |
| 47 | 47 |
|
| 48 | 48 |
\examples{
|
- get_description moved to struct and updated
Gavin Rhys Lloyd authored on 31/01/2024 09:48:31
| ... | ... |
@@ -7,11 +7,11 @@ |
| 7 | 7 |
PLSDA(number_components = 2, factor_name, pred_method = "max_prob", ...) |
| 8 | 8 |
} |
| 9 | 9 |
\arguments{
|
| 10 |
-\item{number_components}{(numeric, integer) The number of PLS components. The default is \code{2}.}
|
|
| 10 |
+\item{number_components}{(numeric, integer) The number of PLS components. The default is \code{2}.\cr}
|
|
| 11 | 11 |
|
| 12 | 12 |
\item{factor_name}{(character) The name of a sample-meta column to use.}
|
| 13 | 13 |
|
| 14 |
-\item{pred_method}{(character) Prediction method. Allowed values are limited to the following: \itemize{\item{\code{"max_yhat"}: The predicted group is selected based on the largest value of y_hat.}\item{\code{"max_prob"}: The predicted group is selected based on the largest probability of group membership.}} The default is \code{"max_prob"}.}
|
|
| 14 |
+\item{pred_method}{(character) Prediction method. Allowed values are limited to the following: \itemize{ \item{\code{"max_yhat"}: The predicted group is selected based on the largest value of y_hat.}\item{\code{"max_prob"}: The predicted group is selected based on the largest probability of group membership.}} The default is \code{"max_prob"}.}
|
|
| 15 | 15 |
|
| 16 | 16 |
\item{...}{Additional slots and values passed to \code{struct_class}.}
|
| 17 | 17 |
} |
| ... | ... |
@@ -37,14 +37,25 @@ A \code{PLSDA} object with the following \code{output} slots:
|
| 37 | 37 |
PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For >2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value. |
| 38 | 38 |
} |
| 39 | 39 |
\details{
|
| 40 |
-This object makes use of functionality from the following packages:\itemize{\item{\code{pls}}}
|
|
| 40 |
+This object makes use of functionality from the following packages:\itemize{ \item{\code{pls}}}
|
|
| 41 | 41 |
} |
| 42 |
+\section{Inheritance}{
|
|
| 43 |
+ |
|
| 44 |
+A \code{PLSDA} object inherits the following \code{struct} classes: \cr\cr
|
|
| 45 |
+\code{PLSDA()} ⭢ \code{PLSR()} ⭢ \code{model()} ⭢ \code{struct_class()}
|
|
| 46 |
+} |
|
| 47 |
+ |
|
| 42 | 48 |
\examples{
|
| 49 |
+M = PLSDA( |
|
| 50 |
+ number_components = 2, |
|
| 51 |
+ factor_name = "V1", |
|
| 52 |
+ pred_method = "max_prob") |
|
| 53 |
+ |
|
| 43 | 54 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 44 | 55 |
} |
| 45 | 56 |
\references{
|
| 46 | 57 |
Liland K, Mevik B, Wehrens R (2023). \emph{pls: Partial Least Squares and
|
| 47 |
-Principal Component Regression}. R package version 2.8-2, |
|
| 58 |
+Principal Component Regression}. R package version 2.8-3, |
|
| 48 | 59 |
\url{https://CRAN.R-project.org/package=pls}.
|
| 49 | 60 |
|
| 50 | 61 |
Perez NF, Ferre J, Boque R (2009). "Calculation of the reliability of |
- predicted group now correctly assigned based on ingroup probability or yhat value
Gavin Rhys Lloyd authored on 05/09/2023 15:04:56
| ... | ... |
@@ -4,13 +4,15 @@ |
| 4 | 4 |
\alias{PLSDA}
|
| 5 | 5 |
\title{Partial least squares discriminant analysis}
|
| 6 | 6 |
\usage{
|
| 7 |
-PLSDA(number_components = 2, factor_name, ...) |
|
| 7 |
+PLSDA(number_components = 2, factor_name, pred_method = "max_prob", ...) |
|
| 8 | 8 |
} |
| 9 | 9 |
\arguments{
|
| 10 | 10 |
\item{number_components}{(numeric, integer) The number of PLS components. The default is \code{2}.}
|
| 11 | 11 |
|
| 12 | 12 |
\item{factor_name}{(character) The name of a sample-meta column to use.}
|
| 13 | 13 |
|
| 14 |
+\item{pred_method}{(character) Prediction method. Allowed values are limited to the following: \itemize{\item{\code{"max_yhat"}: The predicted group is selected based on the largest value of y_hat.}\item{\code{"max_prob"}: The predicted group is selected based on the largest probability of group membership.}} The default is \code{"max_prob"}.}
|
|
| 15 |
+ |
|
| 14 | 16 |
\item{...}{Additional slots and values passed to \code{struct_class}.}
|
| 15 | 17 |
} |
| 16 | 18 |
\value{
|
| ... | ... |
@@ -32,7 +34,7 @@ A \code{PLSDA} object with the following \code{output} slots:
|
| 32 | 34 |
} |
| 33 | 35 |
} |
| 34 | 36 |
\description{
|
| 35 |
-PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable and applies a threshold to the predicted values in order to predict group membership for new samples. |
|
| 37 |
+PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable. For >2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value. |
|
| 36 | 38 |
} |
| 37 | 39 |
\details{
|
| 38 | 40 |
This object makes use of functionality from the following packages:\itemize{\item{\code{pls}}}
|
due to roxygen no longer needing % to be escaped.
- add markdown flag to description file
- use text format for citations (includes markdown)
- remove % from descriptions (doesnt work with current implementation)
Gavin Rhys Lloyd authored on 06/07/2023 15:19:12
| ... | ... |
@@ -41,15 +41,15 @@ This object makes use of functionality from the following packages:\itemize{\ite
|
| 41 | 41 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 42 | 42 |
} |
| 43 | 43 |
\references{
|
| 44 |
-Liland K, Mevik B, Wehrens R (2022). |
|
| 45 |
-\emph{pls: Partial Least Squares and Principal Component Regression}.
|
|
| 46 |
-R package version 2.8-1, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 44 |
+Liland K, Mevik B, Wehrens R (2023). \emph{pls: Partial Least Squares and
|
|
| 45 |
+Principal Component Regression}. R package version 2.8-2, |
|
| 46 |
+\url{https://CRAN.R-project.org/package=pls}.
|
|
| 47 | 47 |
|
| 48 |
-Perez NF, Ferre J, Boque R (2009). |
|
| 49 |
-``Calculation of the reliability of classification in discriminant partial least-squares binary classification.'' |
|
| 50 |
-\emph{Chemometrics and Intelligent Laboratory Systems}, \bold{95}(2), 122-128.
|
|
| 48 |
+Perez NF, Ferre J, Boque R (2009). "Calculation of the reliability of |
|
| 49 |
+classification in discriminant partial least-squares binary |
|
| 50 |
+classification." \emph{Chemometrics and Intelligent Laboratory Systems},
|
|
| 51 |
+\emph{95}(2), 122-128.
|
|
| 51 | 52 |
|
| 52 |
-Barker M, Rayens W (2003). |
|
| 53 |
-``Partial least squares for discrimination.'' |
|
| 54 |
-\emph{Journal of Chemometrics}, \bold{17}(3), 166-173.
|
|
| 53 |
+Barker M, Rayens W (2003). "Partial least squares for discrimination." |
|
| 54 |
+\emph{Journal of Chemometrics}, \emph{17}(3), 166-173.
|
|
| 55 | 55 |
} |
Gavin Rhys Lloyd authored on 23/01/2023 10:57:52
| ... | ... |
@@ -41,9 +41,9 @@ This object makes use of functionality from the following packages:\itemize{\ite
|
| 41 | 41 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 42 | 42 |
} |
| 43 | 43 |
\references{
|
| 44 |
-Liland K, Mevik B, Wehrens R (2021). |
|
| 44 |
+Liland K, Mevik B, Wehrens R (2022). |
|
| 45 | 45 |
\emph{pls: Partial Least Squares and Principal Component Regression}.
|
| 46 |
-R package version 2.8-0, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 46 |
+R package version 2.8-1, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 47 | 47 |
|
| 48 | 48 |
Perez NF, Ferre J, Boque R (2009). |
| 49 | 49 |
``Calculation of the reliability of classification in discriminant partial least-squares binary classification.'' |
* add selectivity ratio
* replace vip_summary with feature_importance
renamed and now allows vip, sr and sr_pvalues to be plotted
* add equal_split model
random subsets so generate training sets with equal group numbers
* plot 1 - p-value
to conform with the "best" feature being a maximum value
* add resample iterator
subsample at random over a number of iterations. Option to use
different kinds of splitting methods. Corresponding chart.
* allow use of list() for factor_name
* force apply not to simplify output to guarantee returning a list
* update example
* add correct parameter
collect will collect the requested model output over all iterations in a list WORK IN PROGRESS
* add collection of multiple outputs of model sequence
* plot reg coeff on rhs
* match outputs of xval for use with grid search etc
* specify levels when converting predictions to factor
* change PLSDA to inherit from PLSR
rename some charts to be compatible with both PLSR and PLSDA
* allow y-block column selection
* re-assign y output after PLSR with factor
* update vignettes wrt PLS changes
* update documentation
* update R version to 4.1
* update documentation
* update documentation
* update scatter plot
- new scatter chart object
- used by PCA scores, PLSR/PLSDA scores
- other charts updated to reflect changes in scores plots where necessary
- added ycol param to plots for when y-block is a matrix
* add url to github
* add plsda scores alias
- plsda_scores_plot and pls_scores_plot do that same thing
Included for backwards compatability
- added components back as parameter for scores plots for backwards compatibility
* fix broken example
* fix broken tests
- scores is now returned as a DatasetExperiment object not a data.frame
* Update data_analysis_omics_using_the_structtoolbox.Rmd
- wrt changes in scores plots
* update documentation
* fix colnames for Y matrix
Gavin Rhys Lloyd authored on 28/02/2022 12:38:08
• GitHub committed on 28/02/2022 12:38:08
| ... | ... |
@@ -16,7 +16,7 @@ PLSDA(number_components = 2, factor_name, ...) |
| 16 | 16 |
\value{
|
| 17 | 17 |
A \code{PLSDA} object with the following \code{output} slots:
|
| 18 | 18 |
\tabular{ll}{
|
| 19 |
-\code{scores} \tab (data.frame) \cr
|
|
| 19 |
+\code{scores} \tab (DatasetExperiment) \cr
|
|
| 20 | 20 |
\code{loadings} \tab (data.frame) \cr
|
| 21 | 21 |
\code{yhat} \tab (data.frame) \cr
|
| 22 | 22 |
\code{design_matrix} \tab (data.frame) \cr
|
| ... | ... |
@@ -27,6 +27,8 @@ A \code{PLSDA} object with the following \code{output} slots:
|
| 27 | 27 |
\code{pls_model} \tab (list) \cr
|
| 28 | 28 |
\code{pred} \tab (data.frame) \cr
|
| 29 | 29 |
\code{threshold} \tab (numeric) \cr
|
| 30 |
+\code{sr} \tab (data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p < 0.05. \cr
|
|
| 31 |
+\code{sr_pvalue} \tab (data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution. \cr
|
|
| 30 | 32 |
} |
| 31 | 33 |
} |
| 32 | 34 |
\description{
|
Gavin Rhys Lloyd authored on 01/10/2021 16:52:55
| ... | ... |
@@ -39,9 +39,9 @@ This object makes use of functionality from the following packages:\itemize{\ite
|
| 39 | 39 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 40 | 40 |
} |
| 41 | 41 |
\references{
|
| 42 |
-Mevik B, Wehrens R, Liland K (2020). |
|
| 42 |
+Liland K, Mevik B, Wehrens R (2021). |
|
| 43 | 43 |
\emph{pls: Partial Least Squares and Principal Component Regression}.
|
| 44 |
-R package version 2.7-3, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 44 |
+R package version 2.8-0, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 45 | 45 |
|
| 46 | 46 |
Perez NF, Ferre J, Boque R (2009). |
| 47 | 47 |
``Calculation of the reliability of classification in discriminant partial least-squares binary classification.'' |
Gavin Rhys Lloyd authored on 07/07/2021 09:37:19
• GitHub committed on 07/07/2021 09:37:19
| ... | ... |
@@ -14,7 +14,20 @@ PLSDA(number_components = 2, factor_name, ...) |
| 14 | 14 |
\item{...}{Additional slots and values passed to \code{struct_class}.}
|
| 15 | 15 |
} |
| 16 | 16 |
\value{
|
| 17 |
-A \code{PLSDA} object.
|
|
| 17 |
+A \code{PLSDA} object with the following \code{output} slots:
|
|
| 18 |
+\tabular{ll}{
|
|
| 19 |
+\code{scores} \tab (data.frame) \cr
|
|
| 20 |
+\code{loadings} \tab (data.frame) \cr
|
|
| 21 |
+\code{yhat} \tab (data.frame) \cr
|
|
| 22 |
+\code{design_matrix} \tab (data.frame) \cr
|
|
| 23 |
+\code{y} \tab (data.frame) \cr
|
|
| 24 |
+\code{reg_coeff} \tab (data.frame) \cr
|
|
| 25 |
+\code{probability} \tab (data.frame) \cr
|
|
| 26 |
+\code{vip} \tab (data.frame) \cr
|
|
| 27 |
+\code{pls_model} \tab (list) \cr
|
|
| 28 |
+\code{pred} \tab (data.frame) \cr
|
|
| 29 |
+\code{threshold} \tab (numeric) \cr
|
|
| 30 |
+} |
|
| 18 | 31 |
} |
| 19 | 32 |
\description{
|
| 20 | 33 |
PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable and applies a threshold to the predicted values in order to predict group membership for new samples. |
* 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
| ... | ... |
@@ -2,23 +2,39 @@ |
| 2 | 2 |
% Please edit documentation in R/PLSDA_class.R |
| 3 | 3 |
\name{PLSDA}
|
| 4 | 4 |
\alias{PLSDA}
|
| 5 |
-\title{PLSDA model class}
|
|
| 5 |
+\title{Partial least squares discriminant analysis}
|
|
| 6 | 6 |
\usage{
|
| 7 | 7 |
PLSDA(number_components = 2, factor_name, ...) |
| 8 | 8 |
} |
| 9 | 9 |
\arguments{
|
| 10 |
-\item{number_components}{The number of PLS components to calculate.}
|
|
| 10 |
+\item{number_components}{(numeric, integer) The number of PLS components. The default is \code{2}.}
|
|
| 11 | 11 |
|
| 12 |
-\item{factor_name}{The sample-meta column name to use.}
|
|
| 12 |
+\item{factor_name}{(character) The name of a sample-meta column to use.}
|
|
| 13 | 13 |
|
| 14 |
-\item{...}{additional slots and values passed to struct_class}
|
|
| 14 |
+\item{...}{Additional slots and values passed to \code{struct_class}.}
|
|
| 15 | 15 |
} |
| 16 | 16 |
\value{
|
| 17 |
-struct object |
|
| 17 |
+A \code{PLSDA} object.
|
|
| 18 | 18 |
} |
| 19 | 19 |
\description{
|
| 20 |
-Partial least squares (PLS) discriminant analysis (DA) model class. This object can be used to train/apply PLS models. |
|
| 20 |
+PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable and applies a threshold to the predicted values in order to predict group membership for new samples. |
|
| 21 |
+} |
|
| 22 |
+\details{
|
|
| 23 |
+This object makes use of functionality from the following packages:\itemize{\item{\code{pls}}}
|
|
| 21 | 24 |
} |
| 22 | 25 |
\examples{
|
| 23 | 26 |
M = PLSDA('number_components'=2,factor_name='Species')
|
| 24 | 27 |
} |
| 28 |
+\references{
|
|
| 29 |
+Mevik B, Wehrens R, Liland K (2020). |
|
| 30 |
+\emph{pls: Partial Least Squares and Principal Component Regression}.
|
|
| 31 |
+R package version 2.7-3, \url{https://CRAN.R-project.org/package=pls}.
|
|
| 32 |
+ |
|
| 33 |
+Perez NF, Ferre J, Boque R (2009). |
|
| 34 |
+``Calculation of the reliability of classification in discriminant partial least-squares binary classification.'' |
|
| 35 |
+\emph{Chemometrics and Intelligent Laboratory Systems}, \bold{95}(2), 122-128.
|
|
| 36 |
+ |
|
| 37 |
+Barker M, Rayens W (2003). |
|
| 38 |
+``Partial least squares for discrimination.'' |
|
| 39 |
+\emph{Journal of Chemometrics}, \bold{17}(3), 166-173.
|
|
| 40 |
+} |
Gavin Rhys Lloyd authored on 06/02/2020 13:51:52
| ... | ... |
@@ -4,9 +4,13 @@ |
| 4 | 4 |
\alias{PLSDA}
|
| 5 | 5 |
\title{PLSDA model class}
|
| 6 | 6 |
\usage{
|
| 7 |
-PLSDA(...) |
|
| 7 |
+PLSDA(number_components = 2, factor_name, ...) |
|
| 8 | 8 |
} |
| 9 | 9 |
\arguments{
|
| 10 |
+\item{number_components}{The number of PLS components to calculate.}
|
|
| 11 |
+ |
|
| 12 |
+\item{factor_name}{The sample-meta column name to use.}
|
|
| 13 |
+ |
|
| 10 | 14 |
\item{...}{additional slots and values passed to struct_class}
|
| 11 | 15 |
} |
| 12 | 16 |
\value{
|
| ... | ... |
@@ -16,5 +20,5 @@ struct object |
| 16 | 20 |
Partial least squares (PLS) discriminant analysis (DA) model class. This object can be used to train/apply PLS models. |
| 17 | 21 |
} |
| 18 | 22 |
\examples{
|
| 19 |
-M = PLSDA() |
|
| 23 |
+M = PLSDA('number_components'=2,factor_name='Species')
|
|
| 20 | 24 |
} |
Gavin Rhys Lloyd authored on 04/02/2020 17:18:11
Gavin Rhys Lloyd authored on 19/12/2019 15:14:02
| ... | ... |
@@ -9,6 +9,9 @@ PLSDA(...) |
| 9 | 9 |
\arguments{
|
| 10 | 10 |
\item{...}{slots and values for the new object}
|
| 11 | 11 |
} |
| 12 |
+\value{
|
|
| 13 |
+struct object |
|
| 14 |
+} |
|
| 12 | 15 |
\description{
|
| 13 | 16 |
Partial least squares (PLS) discriminant analysis (DA) model class. This object can be used to train/apply PLS models. |
| 14 | 17 |
} |
...update some documentation
Gavin Rhys Lloyd authored on 17/12/2019 17:24:38
...rename all function with dot to underscore
replace dataset with DatasetExperiment
Gavin Rhys Lloyd authored on 17/12/2019 15:48:01
| 1 | 1 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,14 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/PLSDA_class.R |
|
| 3 |
+\name{PLSDA}
|
|
| 4 |
+\alias{PLSDA}
|
|
| 5 |
+\title{PLSDA model class}
|
|
| 6 |
+\usage{
|
|
| 7 |
+PLSDA(...) |
|
| 8 |
+} |
|
| 9 |
+\description{
|
|
| 10 |
+Partial least squares (PLS) discriminant analysis (DA) model class. This object can be used to train/apply PLS models. |
|
| 11 |
+} |
|
| 12 |
+\examples{
|
|
| 13 |
+M = PLSDA() |
|
| 14 |
+} |