Displaying 20 results from an estimated 30000 matches similar to: "Repeated k-fold Cross-Validation with Stepwise Regression"
2001 Jun 28
0
: k-fold cross validation for fda,mda etc
Hi all,
Has anyone tried to do k-fold cross validation for flexible discriminant
analysis ( mda library), for example, using crossval() in bootstrap?
The problem is that the function crossval() requires a separate matrix
for predictors and another for responses, whereas the function fda(),
using the formula argument only.
Is there another way of doing k-fold cross validation for functions
which
2006 Nov 16
3
X-fold cross validation function for discriminant analysis
Hi all,
I ran a discriminant analysis with some data and want to get a general idea
of prediction error rate. Some have suggested using X-fold cross validation
procedure. Anyone know if there is a function for this in R?
Thanks,
Wade
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2012 May 21
1
Need Help in K-fold validation in Decision tree
Hi ,
I have built decision tree using rpart . I want to do k Fold validation on
the decision tree .
Could you help how can i do that .. please tell the package which required
for K fold validation.
Regards,
Santosh
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2012 Feb 16
1
Repeated cross-validation for a lm object
Dear R users
I'd like to hear from someone if there is a function to do a repeated k-fold
cross-validation for a lm object and get the predicted values for every
observation. The situation is as follows:
I had a data set composed by 174 observations from which I sampled randomly
a subset composed by 150 observations. With the subset (n = 150) I fitted
the model: y = a + bx. The model
2006 Jun 07
1
knn - 10 fold cross validation
Hi,
I was trying to get the optimal 'k' for the knn. To do this I was using the following function :
knn.cvk <- function(datmat, cl, k = 2:9) {
datmatT <- (datmat)
cv.err <- cl.pred <- c()
for (i in k) {
newpre <- as.vector(knn.cv(datmatT, cl, k = i))
cl.pred <- cbind(cl.pred, newpre)
cv.err <- c(cv.err, sum(cl != newpre))
}
2012 Nov 09
0
10-Fold Cross Validation AND Random Forest
Hi,
I am using the Random Forest package to classify observations into one of two classes. My data is unbalanced with the minority class accounting for 7% of total data set.
I have heard the 10-Fold Cross validation can help me with improving classification. But being new at most of this it's not something I can do from scratch on my own. So I have spent all this morning trying to find a
2007 Feb 12
0
V fold cross validation
Does any package available in R contain the V-fold cross-validation method for determining the right number of clusters?
Any help greatly appreciated
Kind regards
Dr Graham Leask
Economics and Strategy Group
Aston Business School
Aston University
Aston Triangle
Birmingham
B4 7ET
Tel: Direct line 0121 204 3150
email g.leask@aston.ac.uk
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2007 Oct 30
1
NAIVE BAYES with 10-fold cross validation
hi there!!
i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas??
i am very glad about any help!!
i need a naive bayes with 10-fold cross validation:
code:
library(e1071)
model <- naiveBayes(code ~ ., mydata)
tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min,
sampling = c("cross"),
2007 Jul 23
4
nnet 10-fold cross-validation
Hi
It clear that to do a classification with svm under 10-fold cross
validation one uses
svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross =
10)
What corresponds to the nnet?
nnet(.....,cross=10)?
Regards
2007 Sep 25
1
10- fold cross validation for naive bayes(e1071)
Hallo!
I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that?
I tried to do it myself by applying the tune function first:
library(e1071)
tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,
2011 Dec 12
1
k-folds cross validation with conditional logistic
--begin inclusion --
I have a matched-case control dataset that I'm using conditional
logistic regression (clogit in survival) to analyze. I'm trying to
conduct k-folds cross validation on my top models but all of the
packages I can find (CVbinary in DAAG, KVX) won't work with clogit
models. Is there any easy way to do this in R?
-end inclusion --
The clogit funciton is simply a
2005 Feb 25
0
Bayesian stepwise (was: Forward Stepwise regression based onpartial F test)
oops,
Forgot to cc to the list.
Regards,
Mike
-----Original Message-----
From: dr mike [mailto:dr.mike at ntlworld.com]
Sent: 24 February 2005 19:21
To: 'Spencer Graves'
Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based
onpartial F test)
Spencer,
Obviously the problem is one of supersaturation. In view of that, are you
aware of the following?
A Two-Stage
2012 Nov 15
1
Stepwise regression scope: all interacting terms (.^2)
Dear Gurus,
Thank you in advance for your assistance. I'm trying to understand scope better when performing stepwise regression using "step." I have a model with a binary response variable and 10 predictor variables. When I perform stepwise regression I define scope=.^2 to allow interactions between all terms. But I am missing something. When I perform stepwise regression (both
2013 Apr 17
1
Regularized Regressions
Hi all,
I would greatly appreciate if someone was so kind and share with us a
package or method that uses a regularized regression approach that balances
a regression model performance and model complexity.
That said I would be most grateful is there is an R-package that combines
Ridge (sum of squares coefficients), Lasso: Sum of absolute coefficients
and Best Subsets: Number of coefficients as
2003 Jun 20
2
stepwise regression
Hi,
S-PLUS includes the function "stepwise" which can use a variety of
methods to conduct stepwise multiple linear regression on a set of
predictors. Does a similar function exist in R? I'm having difficulty
finding one. If there is one it must be under a different name because
I get an error message when I try 'help(stepwise)' in R.
Thanks for your help,
Andy Taylor
2008 Sep 26
1
Tolerance levels in stepwise regression
Hello,
I have been using the step() function for stepwise regression and was
wondering if there was a way to specify a tolerance level either using
step() or another stepwise function. So far I have only found an option to
specify tolerance in lm.fit() but I am not an experienced R user and am not
quite sure if this command can be implemented using a stepwise function. I
have tried simply
2006 Dec 14
3
Stepwise regression
Dear all,
I am wondering why the step() procedure in R has the description 'Select a
formula-based model by AIC'.
I have been using Stata and SPSS and neither package made any reference to
AIC in its stepwise procedure, and I read from an earlier R-Help post that
step() is really the 'usual' way for doing stepwise (R Help post from Prof
Ripley, Fri, 2 Apr 1999 05:06:03
2000 Nov 01
0
Forward stepwise regression
I have a question regarding doing forward stepwise regression using the step function and an lm model.
I am trying to run a stepwise procedure that will add a predictor variable, one at a time, conduct an F test to determine whether or not to drop any previous terms, and terminate when no other predictors qualify to be added. Using the step function and looking at the example in the book, I
2005 May 07
1
help for bootstrap of backward stepwise logistic regression
I would like to perform a bootstrap validation of a backward stepwise
logistic regression analysis, but I am a beginner with R and I am not
sure of how to do it.
Is there anyone that can send me a sample file in tab format (that I
can modify in Excel by pasting my data) and the pertinent R algorithm?
Many thanks
Giuseppe
--
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Dr. Giuseppe Biondi
2011 Jul 07
1
Automated stepwise multiple linear regression
Dear forum members,
hope you can understand my unprofessionell English. I have never been
working with RStudio before (even not with R), so I immediately need some
help, because I want to carry out an automated multiple stepwise linear
regression between temperatures and different surface parameters.
Data can be found in several .xls- or .csv-files within one folder. Files
are named