similar to: GLS model - diagnostic plots

Dear Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is

Hello, I am currently trying to show the abundance of two species of zooplankton within the North Sea as pie chart bubble plots. I followed Werner Wernersen's advice in R help (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48644.html) and used Paul Murrell's paper "Integrating Grid Graphics Output with Base Graphics Output" (in R News) to try and do this (using the gridBase

Dear Thomas, I want to use evaluate effect of Age using restricted cubic form in the FGR function as Fgr.crr <- FGR(Hist(time, event) ~ rcs(Age_years), data=dat) It provides error. " Error in parse(text = termtext, keep.source = FALSE): .... 1: response ~ rcs(Age_years Do I need to change any of the R code? Regards Amalraj Raja The University of Aberdeen is a charity

Dear All, I would like to use R function 'selectFGR' of fine gray model in competing risks model. I used the 'Melanoma' data in 'riskRegression' package. Some of the variables are factor. I get solution for full model but not in variable selection model. Any advice how to use factor variable in 'selectFGR' function. The following R code is produced below

Hi , library(foreign) write.dta(data1, "data1.dta") should work. The file will be saved in the working directory. Use getwd() to know the working directory. Best wishes Amalraj Raja -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of rosario scandurra Sent: 22 March 2018 07:47 To: r-help at r-project.org Subject: [R] exporting data to stata

On Thu, Mar 22, 2018 at 4:52 AM, Raja, Dr. Edwin Amalraj <amalraj.raja at abdn.ac.uk> wrote: > Hi , > > library(foreign) > write.dta(data1, "data1.dta") > > should work. I don't think so: > library(foreign) > example(svydesign) > write.dta(dstrat, "~/Downloads/foo.dta") Error in write.dta(dstrat, "~/Downloads/foo.dta") : The

Hello, We previously had Win2008 R2 DCs which we have begun to replace with Win2012 servers (forest is still at 2008 level however). I was able to query DFS via rpcclient but now I find that I am unable to. I know that SMB3 was introduced in Win2012 but I believe that it should auto-negotiate earlier versions. I did however test from v4.1.9 as well, unfortunately that still fails. From Samba3

Dear Raja, A Fine and Gray model can be fitted using the standard coxph function with weights that correct for right censoring and left truncation. Hence I guess any function that allows to perform stepwise regression with coxph should work. See e.g. my article in Biometrics https://doi.org/10.1111/j.1541-0420.2010.01420.x, or the vignette "Multi-state models and competing risks" in the

Dear R users, I am new to R and wanted to apply competing risk methods in my research work. I used the R code given by Zhang et al in his paper 'Nomogram for survival analysis in the presence of competing risks published in Ann Trans Med 2017:5(20):403. I am struggling with getting calibration curve thro' internal validation. I am happy to receive suggestion in the coding as well

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

Hi, Sorry not to provide R-code in my previous mail. R code is below #install.packages("rms") require(rms) #install.packages("mstate") library(mstate) require(splines) library(ggplot2) library(survival) library(splines) #install.packages("survsim") require(survsim) set.seed(10) df<-crisk.sim(n=500, foltime=10, dist.ev=rep("lnorm",2),

Hi I am using Ubuntu 8.04 64 bit, R as below, Matlab 7.6.0. I would like to transfer mat files back and forward between R and Matlab. Whilst I have used Matlab for years its been a long time since I have used R (hence question may be a bit simple) Running code A <- c(1:10) dim(A) <- c(2,5) library(R.matlab) writeMat('A.mat', A=A) Does not appear to generate any mat file

Representative small sample of data: algorithmID <- factor(c(rep('alg1',4),rep('alg2',4),rep('alg3',4))) threshold <- factor(rep(c(.45,.50,.55,.60),times=3)) score <- c(30,32,31,30,10,12,13,14,22,21,20,24) d <- data.frame(algorithmID,threshold,score) AlgorithmID is the name of each algorithm; threshold is the value of a parameter used by the algorithm that

I am trying to fit a generalised least squares model using gls in the nlme package. The model seems to fit very well when I plot the fitted values against the original values, and the model parameters have quite narrow confidence intervals (all are significant at p<5%). The problem is that the log likelihood is always given as -Inf. This doesn't seem to make sense because the model

Hi, I am new to R and I want to export data into Stata. Could somebody help with that? Thanks a lot. This is the code I am using: > setwd("D:/datasets/Seg-bcn/ESBD") > data1 <- readRDS("r17045_ESDB_Habitatges_BDD_V_1_0.rds") > library(foreign) > write.dta(data="data1", file = "D:/datasets/data1.dta") Error in write.dta(data =

A small contribution :) I downloaded and installed 2.0.4b and compiled with NIS+ support since the fixed configuration for it was one of the new features. All worked fine, except that I found I couldn't change passwords. It turned out to be not a configuration error, but that some of the NIS+ routines hadn't been implemented yet. So I did that :) So far my mods seem to be working, but I

Hello, I have written code to plot an xyplot as follows: library(lattice) xyplot(len~ageJan1|as.factor(cohort),groups=sex,as.table=T,strip=strip.c ustom(bg='white',fg='white'),data=dat, xlab="Age (January 1st)",ylab="Length (cm)",main="Linear models for male and female cod, by cohort",type='p',

Dear list, I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter? International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade

Did I understand from something Jeremy said recently that a recommended mode of operation for those wishing to try the full PDC support might be: 1. all file servers running production version (currently 2.0.4b) 2. single PDC machine running HEAD branch in order to get the stability and performance of the production version while still being able to use the latest PDC functionality? In

I wish place the following axis label in such a manner that some of the text is plain and the scientific name is in italics (i.e. a mixture of two font types) Using plot: mtext("Total Landings of Pecten maximus (tonnes)",font,=3, side=2, line=3) makes everything italic, but how do I apply the font change to only "Pecten maximus"? Rgds Phil