Dear all,
I am using caret package to analyze a microarray dataset with feature
selection using sbf(). My dataset is 3500 samples with 2200 genes, the
outcome Y variable is a factor of 12 levels. I used the following script to
run the feature selection:
library(caret)
ctrl <- sbfControl(functions
I'm trying to use the caret package to do repeated k-fold cross validation with
C5.0 decision trees.
The following code generates a working C5.0 decision tree (68% accuracy on
confusion matrix):
> model <- C5.0(as.factor(OneGM) ~., data=OneT.train)
> results <- predict(object=model, new
Hi,
In the Caret package, in one of the training model method for
classification , penalised multinomial regression has been used? I could
not find anywhere how the penalised parameters have been chosen here . I
need the statistical methods that have been used. Can someone please help
me?
Tha
> On Dec 20, 2016, at 1:30 PM, Its August via R-help
> wrote:
>
> rm(list=ls())set.seed(12345)library(mlbench)library(caret)
> options(error=utils::recover)
> #Pastebin link for Data: http://pastebin.com/raw/cg0Kiueqmydata.df <-
> read.table("data.PasteBin.txt", header=
Hello All,
I've a dataset of six samples and 1530 variables/features and wish to know the
the importance of features. I'm trying to use the "Rank Features By Importance"
as mentioned in Feature Selection with the Caret R Package
(http://machinelearningmastery.com/feature-selection-with-the-caret
Hi Max,
Thanks for your help. In the case of randomForest, the keyword
keep.inbag=TRUE in the train function provokes the return the information
about which data rows were in-bag in each tree. That should provide the
required info to re-compute the OOB error for any given alternative error
definit
Matt,
> I've been using a custom summary function to optimise regression model
> methods using the caret package. This has worked smoothly. I've been using
> the default bootstrapping resampling method. For bagging models
> (specifically randomForest in this case) caret can, in theory, uses the
>
Hi all,
I've been using a custom summary function to optimise regression model
methods using the caret package. This has worked smoothly. I've been using
the default bootstrapping resampling method. For bagging models
(specifically randomForest in this case) caret can, in theory, uses the
out-of-b
Here is an answer from Max Khun thank you !
Fabon,
If I understand the problem, there are two ways of doing it. First, if you
are using caret's trian(), rfe() or sbf(), if you set the seed right before
you call the models, they end up using the same resampled data sets. (btw,
if you use the resam
Hello,
Thank you for your reply but I'm not sure your code answers my needs,
from what I read it creates a 10-fold partition and then extracts the
kth partition for future processing.
My question was rather: once I have a 10-fold partition of my data,
how to supply it to the "train" function of t
Hi,
I did the similar experiment with my data. may be following code will give
you some idea. It might not be the best solution but for me it worked.
please do share if you get other idea.
Thank you
CODE###
library(dismo)
set.seed(111)
dd<-read.delim("yourfile.csv",sep=",",header=T)
Hi all,
I run R 2.11.1 under ubuntu 10.10 and caret version 2.88.
I use the caret package to compare different models on a dataset. In
order to compare their different performances I would like to use the
same data partitions for every models. I understand that using a LGOCV
or a boot type re-sam
Dear Max,
Thanks for the reply. I will wait for your further comment
on this.
Regards
Linda Garcia
On Wed, Apr 7, 2010 at 8:03 PM, Max Kuhn wrote:
> Linda,
>
> Thanks for the example.
>
> I did this to make it more reproducible:
>
> set.seed(1)
> X<-matrix(rnorm(50*100),nrow=
Linda,
Thanks for the example.
I did this to make it more reproducible:
set.seed(1)
X<-matrix(rnorm(50*100),nrow=50)
y<-rnorm(50*1)
dimnames(X)
colnames(X) <- paste("V", 1:nrow(X))
# Applying caret package
set.seed(2)
con<-trainControl(method="cv",number=10)
data<-NULL
d
Dear all,
I have used following code but everytime I encounter a problem of not having
coefficients for all the variables in the predictor set.
# code
rm(list=ls())
library(caret)
# generating response and design matrix
X<-matrix(rnorm(50*100),nrow=50)
y<-rnorm(50*1)
# Applying caret package
Kevin!
I've sent 5 replies to your questions already off-list.
The first question is easy (see below). The others will need more
information on your data (via str(trx) abd str(try)) and you versions
(sessionInfo()) as previously asked off-list.
Sorry to out you, but this is bad form.
Max
> I a
Hello,
I am learning caret package, and I want to use the RFE to reduce the
feature. I want to use RFE coupled Random Forest (RFE+FR) to complete this
task. As we know, there are a number of pre-defined sets of functions, like
random Forest(rfFuncs), however,I want to tune the parameters (mtr
Max, thx for the reply, i am amazed at the caret package.. very nice
okay, for time series, i think i can include a dummy variable for each
time period and try to remove the time effect, less 1 dummy, like one
does for seasonality. maybe that will work, if anyone has any
suggestions on how to
Zubin,
> Hello, I have some time series applications, where i have a large set of X
> variables (hundreds) and thousands of time data points (sampling every
> minute).
> I like to use the caret package to support the analysis, variable selection
> and model selection. However, reading the documen
Hello, I have some time series applications, where i have a large set of
X variables (hundreds) and thousands of time data points (sampling every
minute).
I like to use the caret package to support the analysis, variable
selection and model selection. However, reading the documentation, it
l
The help page for extractPredictions suggests and testing confirms
that the function expects a _list_ of models. The predict function
is suggested as the method to get predictions from a single model.
Giving the argument as a list does work with a single model, however:
> predict(glmmat)
Dear Sunny Vic,
I am forwarding it to the list, to help the helpers :-)
bests..
milton
On Mon, Jun 8, 2009 at 12:41 PM, sunny vic wrote:
> Hi Milton,
> here you go
>
> X1=rnorm(11, 50, 10)
> X2=rnorm(11, 20, 10)
> X3=rnorm(11, 50, 60)
> X4=rnorm(11, 10, 2)
> X5=rnorm(11, 5, 22)
>
> x<-cbind(
Hi Sonny Vic,
how about you send a reproducible code?
cheers
milton
On Mon, Jun 8, 2009 at 11:25 AM, sunny vic wrote:
> Hi all
> I am using the caret package and having difficulty in obtaining the
> results
> using regression, I used the glmnet to model and trying to get the
> coefficients an
Hi all
I am using the caret package and having difficulty in obtaining the results
using regression, I used the glmnet to model and trying to get the
coefficients and the model parameters I am trying to use the
extractPrediction to obtain a confusion matrix and it seems to be giving me
errors.
Alex,
> I am using "Caret"package for SVM regression and elastic net
> regression . I can get the final fiited vs observed values. How can I get
> the coefficients? Any ideas?
You didn't say what version of caret and R you are using, what kernel
or what type of coefficients.
If you tune a model
Dear All,
I am using "Caret"package for SVM regression and elastic net
regression . I can get the final fiited vs observed values. How can I get
the coefficients? Any ideas?
Thanks
Alex
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__
R-h
Forgot to cc...
On Fri, Sep 19, 2008 at 3:22 PM, Max Kuhn <[EMAIL PROTECTED]> wrote:
> A new version 3.41 is on
>
> https://r-forge.r-project.org/projects/caret/
>
> Until later tonight, you will have to get it via
>
> svn checkout svn://svn.r-forge.r-project.org/svnroot/caret
>
> and build it
Hi,
I am having problems passing arguments to method="gbm" using the train()
function.
I would like to train gbm using the laplace distribution or the quantile
distribution.
here is the code I used and the error:
gbm.test <- train(x.enet, y.matrix[,7],
method="gbm",
distribution=list(na
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