Preparing for CRAN upload

[agghoo.git] / man / AgghooCV.Rd

diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd
deleted file mode 100644 (file)
index 75d1ab6..0000000
--- a/man/AgghooCV.Rd
+++ /dev/null
@@ -1,110 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/R6_AgghooCV.R
-\name{AgghooCV}
-\alias{AgghooCV}
-\title{R6 class with agghoo functions fit() and predict().}
-\description{
-Class encapsulating the methods to run to obtain the best predictor
-from the list of models (see 'Model' class).
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-new}{\code{AgghooCV$new()}}
-\item \href{#method-fit}{\code{AgghooCV$fit()}}
-\item \href{#method-predict}{\code{AgghooCV$predict()}}
-\item \href{#method-clone}{\code{AgghooCV$clone()}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-new"></a>}}
-\if{latex}{\out{\hypertarget{method-new}{}}}
-\subsection{Method \code{new()}}{
-Create a new AgghooCV object.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, quality = NULL)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{data}}{Matrix or data.frame}
-
-\item{\code{target}}{Vector of targets (generally numeric or factor)}
-
-\item{\code{task}}{"regression" or "classification"}
-
-\item{\code{gmodel}}{Generic model returning a predictive function}
-
-\item{\code{quality}}{Function assessing the quality of a prediction;
-quality(y1, y2) --> real number}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-fit"></a>}}
-\if{latex}{\out{\hypertarget{method-fit}{}}}
-\subsection{Method \code{fit()}}{
-Fit an agghoo model.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(
-  CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
-  mode = "agghoo"
-)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{CV}}{List describing cross-validation to run. Slots:
-- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
-- V: number of runs (default: 10)
-- test_size: percentage of data in the test dataset, for MC
-  (irrelevant for V-fold). Default: 0.2.
-- shuffle: wether or not to shuffle data before V-fold.
-  Irrelevant for Monte-Carlo; default: TRUE}
-
-\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-predict"></a>}}
-\if{latex}{\out{\hypertarget{method-predict}{}}}
-\subsection{Method \code{predict()}}{
-Predict an agghoo model (after calling fit())
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{X}}{Matrix or data.frame to predict}
-
-\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
-average models performances (TODO: bad idea?!)}
-}
-\if{html}{\out{</div>}}
-}
-}
-\if{html}{\out{<hr>}}
-\if{html}{\out{<a id="method-clone"></a>}}
-\if{latex}{\out{\hypertarget{method-clone}{}}}
-\subsection{Method \code{clone()}}{
-The objects of this class are cloneable with this method.
-\subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{<div class="arguments">}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{</div>}}
-}
-}
-}