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Corection of package name

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prise6 2015-07-24 20:51:10 +02:00
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8
R/VirtualTwins.R
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@ -1,15 +1,15 @@
#' VirtualTwins : An adapation of VirtualTwins method created by Jared Foster.
#' aVirtualTwins : An adapation of VirtualTwins method created by Jared Foster.
#'
#' VirtualTwins is written mainly with reference classes. Briefly, there is three kinds of class :
#' aVirtualTwins is written mainly with reference classes. Briefly, there is three kinds of class :
#' \itemize{
#' \item \code{\link{VT.object}} class to represent RCT dataset used by VirtualTwins. To format correctly RCT dataset, use \code{\link{formatRCTDataset}}.
#' \item \code{\link{VT.object}} class to represent RCT dataset used by aVirtualTwins. To format correctly RCT dataset, use \code{\link{formatRCTDataset}}.
#' \item \code{\link{VT.difft}} class to compute difference between twins. Family \code{\link{VT.forest}} extends it to compute twins by random forest.
#' \item \code{\link{VT.tree}} class to find subgroups from \code{difft} by CART trees. \code{\link{VT.tree.class}} and \code{\link{VT.tree.reg}} extend it.
#' }
#'
#' @section TODO LIST:
#' \emph{last update : 11.06.2015}
#' \emph{last update : 24.07.2015}
#' \itemize{
#' \item More detailed documentation and vignettes
#' \item Write wrappers for classes

4
vignettes/full-example.Rmd
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@ -278,7 +278,7 @@ This idea is taken from *method 3* of Jared Foster paper :
> A modification of [previous methods] is to obtain $\hat{P_{1i}}$ and $\hat{P_{0i}}$ via cross-validation. In this méthod the specific data for subject $i$ is not used to obtain $\hat{P_{1i}}$ and $\hat{P_{0i}}$. Using k-fold cross-validation, we apply random forest regression approach to $\frac{k-1}{k}$ of the data and use the resulting predictor to obtain estimates of $P_{1i}$ and $P_{0i}$ for the remaining $\frac{1}{k}$ of the observations. This is repeated $k$ times.
To use this approach, type `VirtualTwins:::VT.forest.fold()`. This class takes in argument :
To use this approach, type `aVirtualTwins:::VT.forest.fold()`. This class takes in argument :
* `vt.object` : return of `vt.data()` function
* `fold` : number of fold (e.g. $5$)
@ -289,7 +289,7 @@ __NOTE:__ This function use only `randomForest` package.
```{r, cache=TRUE}
# initialize k-fold RF
model.fold <- VirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
model.fold <- aVirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
# grow RF with randomForest package options
# set do.trace option to see the 5 folds
model.fold$run(ntree = 500, do.trace = 500)