cran comments

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François Vieille 2016-10-09 02:45:00 +02:00
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cran-comments.md Normal file
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@ -0,0 +1,26 @@
This is the first submission
----------------------------------------------------------------
## Test environments
* Linux, Debian jessie, R 3.2.5
* win-builder (devel and release)
## R CMD check result
There were no ERRORs or WARNINGs.
Only one NOTE:
[french]
* VT.difft: possible error in new(structure("VT.difft", package = "aVirtualTwins"), ...): ... utilisé dans une situation où il n'existe pas
[english]
* VT.difft: possible error in new(structure("VT.difft", package = "aVirtualTwins"), ...): ... used in a situation where it does not exist
It seems to be a temporary bug in R-devel [ref](http://r.789695.n4.nabble.com/R-CMD-check-quot-quot-used-in-a-situation-where-it-does-not-exist-td4701779.html)
This NOTE doesn't exist in stable version of R. No NOTEs for win-builder tests.

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@ -6,7 +6,7 @@ library(aVirtualTwins)
library(randomForest)
# Sepsis is a csv file available in SIDES example to this address:
# http://biopharmnet.com/wiki/Software_for_subgroup_identification_and_analysis
# http://biopharmnet.com/subgroup-analysis-software/
# type ?sepsis to see details
# I downloaded zip file and extract the sepsis.csv in data-raw folder.
sepsis.csv <- read.csv(file = "data-raw/sepsis.csv", na.strings = ".")

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@ -93,7 +93,7 @@ Related function in aVirtualTwins package : `vt.tree()`.
See __Introduction__.
*Sepsis* dataset is a simulated clinical trial with two groups treatment about sepsis desease. See details.
This dataset is taken from [SIDES method](http://biopharmnet.com/wiki/Software_for_subgroup_identification_and_analysis)
This dataset is taken from [SIDES method](http://biopharmnet.com/subgroup-analysis-software/)
*Sepsis* contains simulated data on 470 subjects with a binary outcome survival, that stores survival status for patient after 28 days of treatment, value of 1 for subjects who died after 28 days and 0 otherwise. There are 11 covariates, listed below, all of which are numerical variables.
@ -117,7 +117,7 @@ True subgroup is `PRAPACHE <= 26 & AGE <= 49.80`. __NOTE:__ This subgroup is def
* `BLADL` : Baseline activity of daily living score
* `BLLBILI` : Baseline local bilirubin
__Source:__ http://biopharmnet.com/wiki/Software_for_subgroup_identification_and_analysis
__Source:__ http://biopharmnet.com/subgroup-analysis-software/
-----------
@ -131,7 +131,7 @@ type `?vt.data` for more details.
__NOTE:__ if running VT with interactions between $T$ and $X$, set `interactions = TRUE`.
Code of `vt.data()` :
```{r, eval = F}
```{r, collapse=T, eval = F}
vt.data <- function(dataset, outcome.field, treatment.field, interactions = TRUE, ...){
data <- formatRCTDataset(dataset, outcome.field, treatment.field, interactions = TRUE)
VT.object(data = data, ...)
@ -139,7 +139,7 @@ vt.data <- function(dataset, outcome.field, treatment.field, interactions = TRUE
```
__Example with Sepsis__
```{r}
```{r, collapse=T}
# load library VT
library(aVirtualTwins)
# load data sepsis
@ -152,7 +152,7 @@ vt.o <- vt.data(sepsis, "survival", "THERAPY", TRUE)
__Quick example__
*Sepsis* does not have any categorical variable, following example show how `vt.data` deals with categorical values depending on `interactions` parameter
```{r}
```{r, collapse=T}
# Creation of categorical variable
cat.x <- rep(1:5, (nrow(sepsis))/5)
cat.x <- as.factor(cat.x)
@ -162,7 +162,7 @@ vt.o.tmp <- vt.data(sepsis.tmp, "survival", "THERAPY", TRUE)
Dummies variables are created for each category of `cat.x` variable. And `cat.x` is removed from dataset.
```{r, echo = FALSE}
```{r, collapse=T, echo = FALSE}
rm(vt.o.tmp, cat.x, sepsis.tmp)
```
@ -189,7 +189,7 @@ Class `vt.forest("one", ...)` is used. It takes in arguments :
* `...` : options to `randomForest()` function
__with `randomForest`__
```{r}
```{r, collapse=T}
# use randomForest::randomForest()
library(randomForest, verbose = F)
# Reproducibility
@ -210,7 +210,7 @@ __with `party`__
`cforest()` can be usefull however computing time is really long. I think there is an issue when giving *cforest object* in Reference Class parameter. Need to fix it.
```{r}
```{r, collapse=T}
# # use randomForest::randomForest()
# library(party, verbose = F)
# # Reproducibility
@ -229,7 +229,7 @@ Using `caret` can be usefull to deal with parallel computing for example.
__NOTE:__ For `caret` levels of outcome can't be 0, so i'll change levels name into "n"/"y"
```{r}
```{r, collapse=T}
# Copy new object
vt.o.tr <- vt.o$copy()
# Change levels
@ -269,7 +269,7 @@ Function `vt.forest("double", ...)` is used. It takes in arguments :
__NOTE:__ use `trt` parameter in `VT.object::getX()` or `VT.object::getY()` methods to obtain part of data depending on treatment. See following example.
__with `randomForest`__
```{r}
```{r, collapse=T}
# grow RF for T = 1
model.rf.trt1 <- randomForest(x = vt.o$getX(trt = 1),
y = vt.o$getY(trt = 1))
@ -306,7 +306,7 @@ To use this approach, use `vt.forest("fold", ...)`. This class takes in argument
__NOTE:__ This function use only `randomForest` package.
```{r, cache=F}
```{r, collapse=T, cache=F}
# initialize k-fold RF
# you can use randomForest options
@ -325,7 +325,7 @@ Anyway, aVirtualTwins package can be used. To do so, you can use `VT.difft()` cl
* `twin2` : estimate of $P(Y_{i} = 1 | T = 1-T_{i})$ : meaning response probability under the other treatment.
* `method` : _absolute_ (default), _relative_ or _logit_. See `?VT.difft` for details.
```{r}
```{r, collapse=T}
# you get twin1 and twin2 by your own method
# here, i'll use random number between 0 and 1 :
twin1_random <- runif(470)
@ -367,7 +367,7 @@ To compute a classifiction tree, `vt.tree("class", ...)` is used. Internally, `r
See `?VT.tree` for details.
```{r}
```{r, collapse=T}
# initialize classification tree
tr.class <- vt.tree("class",
vt.difft = vt.f.rf,
@ -389,7 +389,7 @@ Use regression tree to explain $Z$ by covariables $X$. Then some leafs have pred
The function to use is `vt.tree("reg", ...)`. It takes same parameters than classification mehod.
```{r}
```{r, collapse=T}
# initialize regression tree
tr.reg <- vt.tree("reg",
vt.difft = vt.f.rf,
@ -418,7 +418,7 @@ This function takes in argument :
If `vt.tree` is a list, unique subgroups are printed.
```{r}
```{r, collapse=T}
# use tr.class computed previously
vt.sbgrps <- vt.subgroups(tr.class)
# print tables with knitr package
@ -428,20 +428,20 @@ knitr::kable(vt.sbgrps)
You can plot one tree with package `rpart.plot`
```{r, echo=F, fig.align='center', fig.height=4, fig.width=6}
```{r, collapse=T, echo=F, fig.align='center', fig.height=4, fig.width=6}
library(rpart.plot)
rpart.plot(tr.class$tree2$tree, type = 1, extra = 1)
```
If you want to see competitors split :
```{r}
```{r, collapse=T}
tr.class$tree2$createCompetitors()
head(tr.class$tree2$competitors)
```
If you want to print incidence of a subgroup :
```{r}
```{r, collapse=T}
vt.o$getIncidences("PRAPACHE >= 26 & AGE >= 52")
# or
# tr.class$tree2$getIncidences("PRAPACHE >= 26 & AGE >= 52")
@ -449,7 +449,7 @@ vt.o$getIncidences("PRAPACHE >= 26 & AGE >= 52")
If you want to get infos about the tree
```{r}
```{r, collapse=T}
tr.class$tree2$getInfos()
# access Ahat
# tr.class$tree2$Ahat
@ -457,7 +457,7 @@ tr.class$tree2$getInfos()
You can re-run rpart computation:
```{r}
```{r, collapse=T}
tr.class$tree2$run(maxdepth = 2)
```