similar to: NA handling in tree package

Dear useRs: Release 3.0.0 of the randomSurvivalForest, an ensemble tree method for the analysis of right censored survival data, package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 3.0.0 Release 3.0.0 represents a major upgrade in the functionality of the 2.x releases. Key changes are as follows: o Missing data can be

Dear useRs: Release 3.0.0 of the randomSurvivalForest, an ensemble tree method for the analysis of right censored survival data, package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 3.0.0 Release 3.0.0 represents a major upgrade in the functionality of the 2.x releases. Key changes are as follows: o Missing data can be

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

Dear all, I am trying to impute data for a range of variables in my data set, of which unfortunately most variables have missing values, and some have quite a few. So I set up the predictor matrix to exclude certain variables (setting the relevant elements to zero) and then I run the imputation. This works fine if I use predictive mean matching for the continous variables in the data set. When I

I have a classification tree model similar to the following (slightly simplified here): > treemod<-tree(y~x) where y is a factor and x is a matrix of numeric predictors. They have dimensions: > length(y) [1] 1163 > dim(x) [1] 1163 75 I?ve evaluated the tree model and am happy with the fit. I also have a matrix of cases that I want to use the tree model to classify. Call it

Hi, I wrote a script in order to simulate data, which I will use for evaluating missing data and imputation. However, I'm having trouble with the last part of my script, in which a dataframe is constructed without missing values. This is my script: y1 <- rnorm(10,0,3) y2 <- rnorm(10,3,3) y3 <- rnorm(10,3,3) y4 <- rnorm(10,6,3) y <- c(y1,y2,y3,y4) a1 <-rep(1,20) a2

Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Hi I am trying to impute missing values for my data.frame. As I intend to use the complete data for prediction I am currently measuring the success of an imputation method by its resulting classification error in my training data. I have tried several approaches to replace missing values: - mean/median substitution - substitution by a value selected from the observed values of a variable - MLE

Hello all, I am using the clustering functions in R in order to work with large masses of binary time series data, however the clustering functions do not seem able to fit this size of practical problem. Library 'hclust' is good (though it may be sub par for this size of problem, thus doubly poor for this application) in that I do not want to make assumptions about the number of

I have a question on the output generated by randomForest in classification mode, specifically, the confusion matrix. The confusion matrix lists the various classes and how the forest classified each one, plus the classification error. Are these numbers essentially averages over all the trees in the forest? If so, is there a way I can get the standard deviation values out of the randomForest,

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Hello I am sorry fuch such a stupid question. Suppose I have a table of data having a lot of NAs and I want to replace those NAs by the mean of the column before NA replacement. How is it possible to do that efficiently ? Thanks in advance, Julie -- Julie Bernauer Yeast Structural Genomics http://www.genomics.eu.org

I am conducting a simulation study generating multivariate normal data, deleting observations to create a data set with missing values and then using multiple imputation via da.norm in Schafer's norm package. >From da.norm, I get the following error message: "Error: NA/NaN/Inf in foreign function call (arg 2)" The frequency of the error message seems to depend on the ratio of n

Begin forwarded message: > From: Ken Hutchison <vicvoncastle@gmail.com> > Date: January 15, 2012 8:54:49 PM EST > To: Ben Bolker <bbolker@gmail.com> > Subject: Re: [R] Trouble installing packages on R2.14.1 > > Check browser proxy settings and run R.exe with the proper flags to use them from cmd. > Hope that helps, > Ken > > > On Jan 15, 2012,

I am more than 'a little disappointed' that you expect a detailed explanation of the problems with your 'bug' report, especially as you did not provide any explanation yourself as to your reasoning (nor did you provide any credentials nor references). Note that 1) Your report did not make clear that this was only relevant to prediction intervals, which are not commonly used.

Dear Sir or Madam, I'm wondering if there is any routine or argument in the function 'glm.fit' that makes it handle NA's. The function 'glm' can handle NA's but I can't make make it work (or find anything written on this in the help files) with 'glm.fit'. Is it even possible in'glm.fit'? How? Thanks before hand, Fredrik Thuring, Business Researcher

[R 1.7.1 on Windows XP Pro] Since R allows missing values for character variables, why are NA's not propagated by character manipulation functions? For example: > temp <- c("a", NA) > temp [1] "a" NA > is.na(temp) [1] FALSE TRUE > paste(temp[1], temp[2]) [1] "a NA" > substr(temp, 1, 1) [1] "a" "N" >

Please find release 3.6.0 of the package "randomSurvivalForest" now available for download on CRAN. --------------------------------------------------------------------------------- CHANGES TO RELEASE ?3.6.0 RELEASE 3.6.0 represents the last and final major upgrade of this product. ?Current and future functionality will migrate to the new CRAN package, Random Forests for Survival,