@@ -33,7 +33,7 @@ In univariate classical least squares regression a line is fitted between each f

 \section{Inheritance}{

 A \code{classical_lsq} object inherits the following \code{struct} classes: \cr\cr

-\code{classical_lsq()} ⭢ \code{model()} ⭢ \code{struct_class()}

+\verb{[classical_lsq]} >> \verb{[model]} >> \verb{[struct_class]}

 }

 \examples{

@@ -7,13 +7,13 @@

 classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

 }

 \arguments{

-\item{alpha}{(numeric) The p-value cutoff for determining significance. The default is \code{0.05}.}

+\item{alpha}{(numeric) The p-value cutoff for determining significance. The default is \code{0.05}.\cr}

-\item{mtc}{(character) Multiple test correction method. Allowed values are limited to the following: \itemize{\item{\code{"bonferroni"}: Bonferroni correction in which the p-values are multiplied by the number of comparisons.}\item{\code{"fdr"}: Benjamini and Hochberg False Discovery Rate correction.}\item{\code{"none"}: No correction.}} The default is \code{"fdr"}.}

+\item{mtc}{(character) Multiple test correction method. Allowed values are limited to the following: \itemize{ \item{\code{"bonferroni"}: Bonferroni correction in which the p-values are multiplied by the number of comparisons.}\item{\code{"fdr"}: Benjamini and Hochberg False Discovery Rate correction.}\item{\code{"none"}: No correction.}} The default is \code{"fdr"}.}

 \item{factor_names}{(character, list) The column names to regress against. If a character vector then the same list is used ofr all features. If a list of character vectors is provided it is assumed there is a different set of columns for each feature.}

-\item{intercept}{(logical) Model intercept. Allowed values are limited to the following: \itemize{\item{\code{"TRUE"}: An intercept term is included in the model.}\item{\code{"FALSE"}: An intercept term is not included in the model.}} The default is \code{TRUE}.}

+\item{intercept}{(logical) Model intercept. Allowed values are limited to the following: \itemize{ \item{\code{"TRUE"}: An intercept term is included in the model.}\item{\code{"FALSE"}: An intercept term is not included in the model.}} The default is \code{TRUE}.\cr}

 \item{...}{Additional slots and values passed to \code{struct_class}.}

 }

@@ -30,7 +30,19 @@ A  \code{classical_lsq} object with the following \code{output} slots:

 \description{

 In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance.

 }

+\section{Inheritance}{

+

+A \code{classical_lsq} object inherits the following \code{struct} classes: \cr\cr

+\code{classical_lsq()} ⭢ \code{model()} ⭢ \code{struct_class()}

+}

+

 \examples{

+M = classical_lsq(

+      alpha = 0.05,

+      mtc = "fdr",

+      factor_names = "V1",

+      intercept = FALSE)

+

 D = iris_DatasetExperiment()

 M = classical_lsq(factor_names = 'Species')

 M = model_apply(M,D)

@@ -11,7 +11,7 @@ classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

 \item{mtc}{(character) Multiple test correction method. Allowed values are limited to the following: \itemize{\item{\code{"bonferroni"}: Bonferroni correction in which the p-values are multiplied by the number of comparisons.}\item{\code{"fdr"}: Benjamini and Hochberg False Discovery Rate correction.}\item{\code{"none"}: No correction.}} The default is \code{"fdr"}.}

-\item{factor_names}{(character) The name of sample meta column(s) to use.}

+\item{factor_names}{(character, list) The column names to regress against. If a character vector then the same list is used ofr all features. If a list of character vectors is provided it is assumed there is a different set of columns for each feature.}

 \item{intercept}{(logical) Model intercept. Allowed values are limited to the following: \itemize{\item{\code{"TRUE"}: An intercept term is included in the model.}\item{\code{"FALSE"}: An intercept term is not included in the model.}} The default is \code{TRUE}.}

@@ -18,7 +18,14 @@ classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

 \item{...}{Additional slots and values passed to \code{struct_class}.}

 }

 \value{

-A  \code{classical_lsq} object.

+A  \code{classical_lsq} object with the following \code{output} slots:

+\tabular{ll}{

+\code{coefficients} \tab          (data.frame) The regression coefficients for each term in the model. \cr

+\code{p_value} \tab          (data.frame) The probability of observing the calculated statistic if the null hypothesis is true. \cr

+\code{significant} \tab          (data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold. \cr

+\code{r_squared} \tab          (data.frame) The value of R Squared for the fitted model. \cr

+\code{adj_r_squared} \tab          (data.frame) The value ofAdjusted  R Squared for the fitted model. \cr

+}

 }

 \description{

 In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance.

@@ -2,35 +2,29 @@

 % Please edit documentation in R/classical_lsq_class.R

 \name{classical_lsq}

 \alias{classical_lsq}

-\title{Classical Least Squares regression}

+\title{Univariate Classical Least Squares Regression}

 \usage{

 classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

 }

 \arguments{

-\item{alpha}{p-value threshold for determining significance. Default alpha = 0.05.}

+\item{alpha}{(numeric) The p-value cutoff for determining significance. The default is \code{0.05}.}

-\item{mtc}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

+\item{mtc}{(character) Multiple test correction method. Allowed values are limited to the following: \itemize{\item{\code{"bonferroni"}: Bonferroni correction in which the p-values are multiplied by the number of comparisons.}\item{\code{"fdr"}: Benjamini and Hochberg False Discovery Rate correction.}\item{\code{"none"}: No correction.}} The default is \code{"fdr"}.}

-\item{factor_names}{the column name(s) of sample_meta to use}

+\item{factor_names}{(character) The name of sample meta column(s) to use.}

-\item{intercept}{[TRUE] or FALSE to include an intercept term in the fit}

+\item{intercept}{(logical) Model intercept. Allowed values are limited to the following: \itemize{\item{\code{"TRUE"}: An intercept term is included in the model.}\item{\code{"FALSE"}: An intercept term is not included in the model.}} The default is \code{TRUE}.}

-\item{...}{additional slots and values passed to struct_class}

+\item{...}{Additional slots and values passed to \code{struct_class}.}

 }

 \value{

-A STRUCT method object with functions for applying classical least squares

-

-struct object

+A  \code{classical_lsq} object.

 }

 \description{

-Classical least squares, where y is the response and X is the design matrix,

-applied to each feature individually. Here the response is taken from the

-data matrix and the design matrix is the taken from the specified sample meta

-data column.

+In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance.

 }

 \examples{

 D = iris_DatasetExperiment()

 M = classical_lsq(factor_names = 'Species')

 M = model_apply(M,D)

-

 }

@@ -4,10 +4,18 @@

 \alias{classical_lsq}

 \title{Classical Least Squares regression}

 \usage{

-classical_lsq(...)

+classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

 }

 \arguments{

-\item{...}{slots and values for the new object}

+\item{alpha}{p-value threshold for determining significance. Default alpha = 0.05.}

+

+\item{mtc}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

+

+\item{factor_names}{the column name(s) of sample_meta to use}

+

+\item{intercept}{[TRUE] or FALSE to include an intercept term in the fit}

+

+\item{...}{additional slots and values passed to struct_class}

 }

 \value{

 A STRUCT method object with functions for applying classical least squares

@@ -20,18 +28,6 @@ applied to each feature individually. Here the response is taken from the

 data matrix and the design matrix is the taken from the specified sample meta

 data column.

 }

-\section{Slots}{

-

-\describe{

-\item{\code{alpha}}{p-value threshold for determining significance. Default alpha = 0.05.}

-

-\item{\code{mtc}}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

-

-\item{\code{factor_names}}{the column name(s) of sample_meta to use}

-

-\item{\code{intercept}}{[TRUE] or FALSE to include an intercept term in the fit}

-}}

-

 \examples{

 D = iris_DatasetExperiment()

 M = classical_lsq(factor_names = 'Species')

@@ -11,6 +11,8 @@ classical_lsq(...)

 }

 \value{

 A STRUCT method object with functions for applying classical least squares

+

+struct object

 }

 \description{

 Classical least squares, where y is the response and X is the design matrix,

@@ -7,13 +7,7 @@

 classical_lsq(...)

 }

 \arguments{

-\item{alpha}{p-value threshold for determining significance. Default alpha = 0.05.}

-

-\item{mtc}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

-

-\item{factor_names}{the column name(s) of sample_meta to use}

-

-\item{intercept}{[TRUE] or FALSE to include an intercept term in the fit}

+\item{...}{slots and values for the new object}

 }

 \value{

 A STRUCT method object with functions for applying classical least squares

@@ -24,6 +18,18 @@ applied to each feature individually. Here the response is taken from the

 data matrix and the design matrix is the taken from the specified sample meta

 data column.

 }

+\section{Slots}{

+

+\describe{

+\item{\code{alpha}}{p-value threshold for determining significance. Default alpha = 0.05.}

+

+\item{\code{mtc}}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

+

+\item{\code{factor_names}}{the column name(s) of sample_meta to use}

+

+\item{\code{intercept}}{[TRUE] or FALSE to include an intercept term in the fit}

+}}

+

 \examples{

 D = iris_DatasetExperiment()

 M = classical_lsq(factor_names = 'Species')

new file mode 100644

@@ -0,0 +1,32 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/classical_lsq_class.R

+\name{classical_lsq}

+\alias{classical_lsq}

+\title{Classical Least Squares regression}

+\usage{

+classical_lsq(...)

+}

+\arguments{

+\item{alpha}{p-value threshold for determining significance. Default alpha = 0.05.}

+

+\item{mtc}{multiple test correction method to apply. Can be: holm, hochberg, hommel, bonferroni, BH, BY, fdr or none}

+

+\item{factor_names}{the column name(s) of sample_meta to use}

+

+\item{intercept}{[TRUE] or FALSE to include an intercept term in the fit}

+}

+\value{

+A STRUCT method object with functions for applying classical least squares

+}

+\description{

+Classical least squares, where y is the response and X is the design matrix,

+applied to each feature individually. Here the response is taken from the

+data matrix and the design matrix is the taken from the specified sample meta

+data column.

+}

+\examples{

+D = iris_DatasetExperiment()

+M = classical_lsq(factor_names = 'Species')

+M = model_apply(M,D)

+

+}