similar to: Using cforest on a hierarchically structured dataset

On Fri, Oct 5, 2018 at 2:07 PM hmh <hugomh at gmx.fr> wrote: > > On 05/10/2018 10:28, Annaert Jan wrote: > > you discard any time series structure; > But that is PRECISELY what a call a bug: > There should not be any "time series structure" in the output or rnorm, > runif and so on but there is one. > > rnorm(N,0,1) > should give on average the same

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

What is the function call interface for predict in the package party for cforest? I am looking at the documentation (the vignette) and ?cforest and from the examples I see that one can call the function predict on a cforest classifier. The method predict seems to be a method of the class RandomForest objects of which are returned by cforest. --------------------------- > cf.model =

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls

> ---------- Forwarded message ---------- > Date: Mon, 18 Jul 2011 10:17:00 -0700 (PDT) > From: KHOFF <kuphoff at gmail.com> > To: r-help at r-project.org > Subject: [R] cforest - keep.forest = false option? > > Hi, > > I'm very new to R. I am most interested in the variable importance > measures > that result from randomForest, but many of my predictors

Hi, I'm very new to R. I am most interested in the variable importance measures that result from randomForest, but many of my predictors are highly correlated. My first question is: 1. do highly correlated variables render variable importance measures in randomForest invalid? and 2. I know that cforest is robust to highly correlated variables, however, I do not have enough space on my

Hi Everyone, I would like to do sequential subtractions within a group so that I know the time between separate observations for a group of individuals. My data: data <- structure(list(group = c("IND1", "IND1", "IND2", "IND2", "IND2", "IND3", "IND4", "IND5", "IND6", "IND6"), date_obs =

Greetings, I am using cforest to predict age of fishes using several variables; as it is rather difficult to age fishes I would like to show that a small subset of fish (training dataset) can be aged, then using RF analysis, age can accurately be predicted to the remaining individuals not in the subsample. In cforest_unbiased the samples are drawn without replacement and so it creates a default

Dear mailing list, I am using the cforest() method from the party package to train a randomForest with ten input parameters which sometimes contain "NA"s. The predicted variable is a binary decision. Building the tree works fine without warnings or error messages, but when using the predict() statement for validation, I run in an error: forest <- cforest(V31 ~ V1+V2+V3,

All -- I have been trying to work with the 'Party' package using R v2.15.1 and have cobbled together a (somewhat) functioning code from examples on the web. I need to run a series of unbiased, conditional, cForest tests on several subsets of data which I have made into a loop. The results ideally will be saved to an output file in matrix form. The two questions regarding the script in

Hi all, I'm having great trouble working with the Cforest (from the party package) and Random forest functions. Large data set seem to create very large model objects which means I cannot work with the number of observations I need to, despite running on a large 8GB 64-bit box. I would like the object to only hold the trees themselves as I intend to export them out of R. Is there anyway,

> ---------- Forwarded message ---------- > Date: Wed, 5 Oct 2011 21:09:41 +0000 > From: Chris <christopher.a.hane at gmail.com> > To: r-help at stat.math.ethz.ch > Subject: Re: [R] Party extract BinaryTree from cforest? > > I found an internal workaround to this to support printing and plot type > simple, > > tt<-party:::prettytree(cf at ensemble[[1]],

Hi, Could anyone help me understand how the mincriterion threshold works in ctree and cforest of the party package? I've seen examples which state that to satisfy the p < 0.05 condition before splitting I should use mincriterion = 0.95 while the documentation suggests I should use mincriterion = qnorm(0.95) which would obviously feed the function a different value. Thanks in advance,

Dear List, I'm trying to find a way to extract the individual conditional inference trees from cforest ( a modelling function in the party package) in a manner analogous to getTree in randomForest and I'm struggling. I can see that the information is held within the ensemble list, but haven't been able to work out how this sequence of nested lists is structured or if any of the items

Hi Everyone, I have the following problem: data <- structure(list(prochi = c("IND1", "IND1", "IND1", "IND2", "IND2", "IND2", "IND2", "IND3", "IND4", "IND5"), date_admission = structure(c(6468, 6470, 7063, 9981, 9983, 14186, 14372, 5129, 9767, 11168), class = "Date")), .Names =

Hello, I am a R beginner and hoping to obtain some hints or suggestions about using permutations to sort a data set I have. Here is an example dataset: Ind1 11 00 12 15 28 Ind2 21 33 22 67 52 Ind3 22 45 21 22 56 Ind4 11 25 74 77 42 Ind5 41 32 67 45 22 This will be read into a variable using read.table. What I want to do is permute these individuals and every

Hello, I am doing a simple linear regression analysis that includes few variables. I am using a bootstrap analysis to obtain the variation of my variables to replacement. I am trying to obtain the coefficients 95% confidence interval from the bootstrap procedure. Here is my script for the bootstrap: N = length (data_Pb[,1]) B = 10000 stor.r2 = rep(0,B) stor.r2 = rep(0,B) stor.inter =

Friends Briefly... In the documentation for lda in MASS it describes the value 'scaling' as 'a matrix which transforms observations into discriminint functions...'. How? Verbosely... I have a matrix of data. 9 independent variables and describing 3-classes. About 100 observations in total. A 10x100 matrix of data. I am trying to generate two discriminant functions and i

Dear R experts, I have a problem with a function I wrote. The fuction looks like this: series<-function(x,s){ foo<-list(); ind3<-integer(); for (j in diff){ for (i in 1:(n-12)){ if (!(x[i,j]==0)&!(x[i+1,j]==0)&!(x[i+2,j]==0)&!(x[i+3,j]==0)&!(x[i+4,j]==0)&!(x[i+5,j]==0)&!(x[i+6,j]==0)