similar to: Survival 2.39

I'm trying to convert a dataset from the time-independent analysis form > head(addicts) id clinic status survtime prison meth clinic01 1 1 1 1 428 0 50 1 2 2 1 1 275 1 55 1 3 3 1 1 262 0 55 1 into the "counting data format" necessary to perform extended Cox regression. I got

To Danardonos concern of splitting time for records with delayed entry: This can fairly easily be accomodated, by simply splitting time in small intervals of time since entry into the study, and then compute the value of the other timescales for each of these e.g.: current.age <- time.from.entry + age.at.entry but the cut on the other timescales will not be exactly where you may want them

Hi all, I just uploaded a new version of the bpcp package. It calculates confidence intervals for a survival distribution for right-censored data using the newly developed beta product confidence procedure. Previously developed methods can have substantial error rate inflation for the lower limit, especially at the right end of the curves when there are small numbers of events. The bpcp method

Dear R-help Community, I'm currently struggling with some issues extending the proportional Cox model with time-dependent coefficients and could really need some help. Since I'm not experienced in adding code in an email in a nice way I add the link to my question and code: https://stats.stackexchange.com/questions/297052/time-dependend-coefficients-in-a-cox-model Essentially, I try

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz

I've just released the latest R-Forge versions of heplots 0.9-3 and candisc 0.5-18 to CRAN. They should appear there within a day or two. == heplots The heplots package provides functions for visualizing hypothesis tests in multivariate linear models (MANOVA, multivariate multiple regression, MANCOVA, etc.). They represent sums-of-squares-and-products matrices for linear hypotheses and for

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three entries that belong to the same person, if an event happen at

The package 'lmvar' (https://CRAN.R-project.org/package=lmvar) fits a model which is linear in the expected value and log-linear in the variance. The fit is like the classical linear fit implemented in 'lm' but lifts the assumption of a constant variance which is implicit in 'lm'. Instead, different observations can have different variances. The package comes with utility

Dear all, I'd like to introduce a new R package called *phylogram,* for working with evolutionary trees as deeply-nested lists known as "dendrogram" objects. The package provides functions for importing and exporting phylogenetic trees in the Newick parenthetic text format, as well as several functions for command-line tree manipulation. With an emphasis on speed and computational

I'm pleased to announce the release of the vcdExtra package, v. 0.5-0 from R-Forge to CRAN, on its way to a CRAN server near you. vcdExtra was originally designed to serve as a sandbox for introducing extensions of mosaic plots and other visualizations for categorical data, particularly those that apply to (poisson surrogate) loglinear models fitted using glm() and related, generalized

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

Dear R users, I am doing Cox regression using coxph(Survival) or cph(Design). I have time varying effects (diagnosed with schoenfeld residuals Chi2 test and graph) so I first want to split time into 2 separate intervals : t<6months and t>=6months, to estimate one hazard ratio (hr) for each interval. I am analysing Overall survival according to 3 prognostic factors (age,deep,ldh).

Recently Brian Ripley had occasion to mock my inability to see a comment in the code for arima(), in the stats package. After considerable dredging around in the r-news archives I found reference to keep.source() and keep.source.pkgs(), which I conjectured just ***might*** possibly be the ``obvious'' resource to which Prof. Ripley thought I ought to have resorted. However, after

Dear useRs: We would like to introduce the "pedgene" package, version 1.2, available now on CRAN, with a brief manual available as a vignette: http://cran.r-project.org/web/packages/pedgene/index.html The pedgene package performs gene-level kernel and burden association tests with disease status and continuous response for pedigree data, as described in our recent paper: Schaid, D.

Hello, I wanted to announce the release of a new package on CRAN wbstats. >From the description "Tools for searching and downloading data and statistics from the World Bank Data API (http://data.worldbank.org/developers/api-overview) and the World Bank Data Catalog API (http://data.worldbank.org/developers/data-catalog-api)" Here is a (hopefully) useful vignette if you would like

R Users, The d/p/q/r functions for the bridge distribution are now available in bridgedist. When a random intercept follows the bridge distribution, as detailed in Wang and Louis (2003) <doi:10.1093/biomet/90.4.765 <http://dx.doi.org/10.1093/biomet/90.4.765>>, a marginalized random-intercept logistic regression will still be a logistic regression with marginal coefficients that are

Dear R enthusiasts, I am happy to announce that the package Leab*R*a is now on CRAN (https://cran.r-project.org/web/packages/leabRa/) Leab*R*a provides the Leabra artificial neural network algorithm (O?Reilly, 1996, ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf) for R. Leabra stands for ?local error driven and associative biologically realistic algorithm?. It is the Rolls

hsdar is back on CRAN We are very happy to announce that a new version of the hsdar-package (hyperspectral data analysis in R) is available on CRAN. The introductory vignette is available here: https://cran.r-project.org/web/packages/hsdar/vignettes/Hsdar-intro.pdf The main class "Speclib" has been changed in that the additional information is now stored in the SI slot (function

Dear all, A revised version of OutliersO3 is available on CRAN: <https://cran.r-project.org/web/packages/OutliersO3/index.html <https://cran.r-project.org/web/packages/OutliersO3/index.html>>. The package has been restructured. The default is now that the tolerance level is set individually for each of the (six) outlier methods included. Plots have been added, as have outlier