similar to: Possible Bug In Validation of UTF-8 Sequences

I have a question on generating multivariate time series surrogates using the "surrogates" function in the RTisean library. The surrogate data matrices are always much shorter than the input matrices. FYI, I'm using R version 2.12.2 on Windows XP RTisean library v 3.0.14 Tisean algorithms v 3.0.13 Creating a surrogate univariate time series returns a time series with the

Hi! I experience an error with an invalid UTF-8 character passed to gsub(..., perl=TRUE); the interesting point is that with perl=FALSE (the default) no error happens. (The character itself was read from an invalid HTML file.) Illustration of the error: gsub("a", "", "\U3e3965", perl=FALSE) # [1] "\U3e3965" gsub("a", "",

R helpers, I would like to know if it is possible that the last version of R is not giving the surrogate splits when you perform a Multivariate regression tree analysis? I installed the programm in different computers and i run the some matrix and it didn't gave me this information. With a previus version R 2.1.1. I do get the information for the surrogates. Please let me know how to get the

# Roxygen2 The premise of `roxygen2` is simple: describe your functions in comments next to where their definitions and `roxygen2` will process your source code and comments to produce R compatible Rd files. Here's a simple example from the `stringr` package: ? ? #' The length of a string (in characters).? ? #'? ? #' @param string input character vector? ? #' @return numeric

# Roxygen2 The premise of `roxygen2` is simple: describe your functions in comments next to where their definitions and `roxygen2` will process your source code and comments to produce R compatible Rd files. Here's a simple example from the `stringr` package: ? ? #' The length of a string (in characters).? ? #'? ? #' @param string input character vector? ? #' @return numeric

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

These utility functions will be used in the OCaml inspection code. --- daemon/daemon_utils_tests.ml | 15 +++++++ daemon/utils.ml | 100 +++++++++++++++++++++++++++++++++++++++++++ daemon/utils.mli | 12 ++++++ 3 files changed, 127 insertions(+) diff --git a/daemon/daemon_utils_tests.ml b/daemon/daemon_utils_tests.ml index 892509d89..b1f02de30 100644 ---

R helpers, I am working with the R program performing multivariate regression trees (MRT). I have a matrix with species and environmental variables saved as a CSV file (sprot_matrix.csv), I have 42 species and 8 environmental variables (SECCHI+PH+TA+PTOT+NTOT+CHLA+AREA+ MEANDEP) for 104 samples Title SpA SpB SpC SpD Varible1 Variable2 Variable3 Sample1 Sample 2

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a

A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a

Fritz just put the updated tseries package to CRAN. I mainly removed (and corrected) code such that tseries fits together with package ts. New code is White's and Teraesvirta's tests for neglected non-linearity (also for the regression case). From the INDEX file: NelPlo Nelson-Plosser Macroeconomic Time Series adf.test Augmented Dickey-Fuller Test amif

Fritz just put the updated tseries package to CRAN. I mainly removed (and corrected) code such that tseries fits together with package ts. New code is White's and Teraesvirta's tests for neglected non-linearity (also for the regression case). From the INDEX file: NelPlo Nelson-Plosser Macroeconomic Time Series adf.test Augmented Dickey-Fuller Test amif

Dear r-help, I am looking for additional documentation on the "adj" column in rpart's splits matrix. The help says: adj gives the adjusted concordance for surrogate splits I am looking info about "adjusted concordance". I cannot find this phrase in either Therneau & Atkinson original RPART documentation or the CART book. This question came up in the

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Hi, When I look at the summary of an rpart object run on my data, I get 7 nodes but when I plot the rpart object, I get only 3 nodes. Should the number of nodes not match in the results of the 2 functions (summary and plot) or it is not always the same? Look forward to your reply, Carol -------------------------------------------- ?summary(rpart.res) Call: rpart(formula = mydata$class ~ ., data

Dear R-help, I'm applying the surrogate Poisson glm, by following Venables & Ripley (7.3 pp238-42). >overall_cbind(expand.grid(treatment=c("Pema","control"),age=c("young","adult","old"),repair=c("excellent","good","poor")),Fr=c(8,0,7,1,2,0,2,7,1,4,7,1, 0,3,2,5,1,9))