Displaying 20 results from an estimated 800 matches similar to: "mtry in ctree_control()"
2009 Jun 18
0
Fractional Polynomials (mfp) for Weibull Model
Dear R-users,
I am trying to modify the mfp() function in the mfp package to model
Weibull survival using fractional polynomials approach. However, I keep
getting into trouble when mfp.fit and other "hidden" functions can't be
found. I did find some of them in Splus but it's getting nowhere. I
wonder if any of you can give me some tips on how to modify it or any
experience
2007 Jun 05
1
R CMD BATCH command
The version of R on our unix system has been updated to version 2.5.0.
When I type the following command at the unix prompt:
'R CMD BATCH filename'
I receive the following error message:
Error in Sys.unsetenv("R_BATCH") : 'Sys.unsetenv' is not available on
this system
Execution halted.
'R CMD BATCH filename' used to work with the prior version of R that I
2006 Feb 27
2
singular convergence in glmmPQL
I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R:
Error in lme.formula(fixed=zz + arm.long,random=~1 |
2004 Oct 13
1
random forest -optimising mtry
Dear R-helpers,
I'm working on mass spectra in randomForest/R, and following the
recommendations for the case of noisy variables, I don't want to use the
default mtry (sqrt of nvariables), but I'm not sure up to which
proportion mtry/nvariables it makes sense to increase mtry without
"overtuning" RF.
Let me tell my example: I have 106 spectra belonging to 4 classes, the
2007 Oct 11
1
random forest mtry and mse
I have been using random forest on a data set with 226 sites and 36
explanatory variables (continuous and categorical). When I use
"tune.randomforest" to determine the best value to use in "mtry" there
is a fairly consistent and steady decrease in MSE, with the optimum of
"mtry" usually equal to 1. Why would that occur, and what does it
signify? What I would
2009 Sep 26
1
mboost_1.1-3 blackboost_fit (PR#13972)
Full_Name: Ivan the Terrible
Version: 2.9.2
OS: Windows XP SP3
Submission from: (NULL) (89.110.13.151)
When using the method blackboost_fit of the package mboost appear following
error :
Error in party:::get_variables(obj at responses) :
trying to get slot "responses" from an object (class "boost_data") that is not
an S4 object
Simple test case that produce bug:
2011 Feb 17
1
missing values in party::ctree
After ctree builds a tree, how would I determine the direction missing values follow by examining the BinaryTree-class object? For instance in the example below Bare.nuclei has 16 missing values and is used for the first split, but the missing values are not listed in either set of factors. (I have the same question for missing values among numeric [non-factor] values, but I assume the answer
2012 Jan 06
1
Please help!! How do I set graphical parameters for ploting ctree()
I'm trying to understand how to set graphical parameters for trees created with the party package. For example take the following code:
library(party)
data(airquality)
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq,
controls = ctree_control(maxsurrogate = 3))
plot(airct)
My problem is, I've got a ctree that has
2012 Jan 19
1
ctree question
Hello. I have used the "party" package to generate a regression tree as
follows:
>origdata<-read.csv("origdata.csv")
>ctrl<-ctree_control(mincriterion=0.99,maxdepth=10,minbucket=10)
>test.ct<-ctree(Y~X1+X2+X3,data=origdata,control=ctrl)
The above works fine. Orig data was my training data. I now have a test
data file (testdata), and
2010 Apr 07
1
extracting ctree() output information
Hi,
I am new to R and am using the ctree() function to do customer
segmentation. I am using the following code to generate the tree:
treedata$Response<-factor(treedata$Conversion)
fit<-ctree(Response ~
.,controls=ctree_control(mincriterion=0.99,maxdepth=4),data=treedata)
plot(fit)
print(fit)
The variable "Response" above equals 1 if the customer responded to an
offering and
2005 Jul 21
4
RandomForest question
Hello,
I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases.
I've seen that although there are only 32 explanatory variables the best classification performance is reached when
2011 Nov 17
1
tuning random forest. An unexpected result
Dear Researches,
I am using RF (in regression way) for analize several metrics extract from
image. I am tuning RF setting a loop using different range of mtry, tree
and nodesize using the lower value of MSE-OOB
mtry from 1 to 5
nodesize from1 to 10
tree from 1 to 500
using this paper as refery
Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007).
Random Forest Models
2010 Dec 21
1
randomForest: tuneRF error
Just curious if anyone else has got this error before, and if so,
would know what I could do (if anything) to get past it:
> mtry <- tuneRF(training, trainingdata$class, ntreeTry = 500, stepFactor = 2, improve = 0.05, trace = TRUE, plot = TRUE, doBest = FALSE)
mtry = 13 OOB error = 0.62%
Searching left ...
mtry = 7 OOB error = 1.38%
-1.222222 0.05
Searching right ...
mtry = 26
2005 Jan 06
1
different result from the same errorest() in library( ipred)
Dear all,
Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following?
errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err
[1] 0.03333333
> errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",
2010 Feb 03
0
mboost: how to implement cost-sensitive boosting family
mboost contains a blackboost method to build tree-based boosting models. I tried to write my own "cost-sensitive" ada family. But obviously my understanding to implement ngradient, loss, and offset functions is not right. I would greatly appreciate if anyone can help me out, or show me how to write a cost-sensitive family, thanks!
Follows are some families I wrote
ngradient <-
2003 Apr 12
5
rpart vs. randomForest
Greetings. I'm trying to determine whether to use rpart or randomForest
for a classification tree. Has anybody tested efficacy formally? I've
run both and the confusion matrix for rf beats rpart. I've looking at
the rf help page and am unable to figure out how to extract the tree.
But more than that I'm looking for a more comprehensive user's guide
for randomForest including
2009 Aug 13
2
randomForest question--problem with ntree
Hi,
I would like to use a random Forest model to get an idea about which variables from a dataset may have some prognostic significance in a smallish study. The default for the number of trees seems to be 500. I tried changing the default to ntree=2000 or ntree=200 and the results appear identical. Have changed mtry from mtry=5 to mtry=6 successfully. Have seen same problem on both a Windows
2013 Feb 03
3
RandomForest, Party and Memory Management
Dear All,
For a data mining project, I am relying heavily on the RandomForest and
Party packages.
Due to the large size of the data set, I have often memory problems (in
particular with the Party package; RandomForest seems to use less memory).
I really have two questions at this point
1) Please see how I am using the Party and RandomForest packages. Any
comment is welcome and useful.
2010 Mar 23
1
caret package, how can I deal with RFE+SVM wrong message?
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) when
RFE, and then I write code below, but there is something wrong message, How
can I deal with it?
2012 Dec 03
2
Different results from random.Forest with test option and using predict function
Hello R Gurus,
I am perplexed by the different results I obtained when I ran code like
this:
set.seed(100)
test1<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200)
predict(test1, newdata=cbind(NewBinaryY, NewXs), type="response")
and this code:
set.seed(100)
test2<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200,
xtest=NewXs, ytest=NewBinarY)
The