search for: briere

Hi, I have probability estimates from two predictive models. I have these estimates and also a binary outcome for a validation data set not used in calibrating either model. I would like to calculate the Brier score for both models on this binary outcome and test the hypothesis that the Brier scores are equal from the two models. I have not been able to find an R function to do this, can

Hello, I am using the val.prob function in the Design package. I understand how the Brier quadratic error score is calculated, but I do not know how the Brier score computed on the calibrated rather than raw predicted probabilities (B cal) is calculated. My question is: how are the calibrated probabilities calculated? Any explanation of this, or references to explanations of this, would be

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

Dear all, I would like to obtain the Brier score prediction error at different times t for an extended Cox model. Previously I have used the 'pec' function (pec{pec}) to obtain prediction error curves for standard Cox PH models but now I have data in the counting process format (I have a covariate with a time-varying effect) and it seems that the pec function does not support the counting

Hello I've decided to try and distill an earlier rather ill focused question to try and elicit a response. Any help is greatly appreciated. Why does mod.cox not work with sbrier whilst mod.km does? Can I make it work? > data(DLBCL) > DLBCL.surv<-Surv(DLBCL$time,DLBCL$cens) > > mod.km<-survfit(DLBCL.surv) > mod.cox<-survfit(coxph(DLBCL.surv~IPI, data=DLBCL)) >

...ur opinion ? More precisely, what problems are there in MS Office apps emulation ? Is there any kind of technical synthesis available on the subject ? Apart from that, is there any idea of the predictible date for a professional usable release ? Thank you in advance for help. Yours. Philippe BRIERE

Dear List, I am familier with binary models, however i am now trying to get predictions from a ordinal model and have a question. I have a data set made up of 12 categorical predictors, the response variable is classed as 1,2,3,4,5,6, this relates to threat level of the species ( on the IUCN rating). Previously i have combined levels 1 and 2 to form = non threatened and then combined 3-6 to

from samba 3.0.9 to 3.0.13 I can not access my shares from a NT4 box as usual before upgrading. I can access samba shares from linux via mount //server/testuser /mnt -o username=testuser,password=testpass as before. Even the newest version (3.0.14a) from samba.org doesnt't help. What has happened to samba ??? Any hints ?? Thanks for your thoughts, G?tz.

Dear List, I am looking to run a host of models (60) with three methods - lmer,glm and lrm. Is there a way to output the key stats into a table that i can copy to excel? I.e for lmer i would want AIC,BIC etc for lrm i would want Brier score, r2, c-value etc At present i am running the models from a script and then copying across the values into a excel spreadsheet however this is time

dear:teacher i have a problem which about the polr()(package "MASS"), if the response must have 3 or more levels? and how to fit the polr() to 2 levels? thank you. turly yours [[alternative HTML version deleted]]

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

Hi List, I don't understand why 'lrm' doesn't recognize the '~.' formula. I'm pretty sure it was working before. Please see below: I'm using R2.3.0, WinXP, Design 2.0-12 thanks, ...Tao > dat <- data.frame(y=factor(rep(1:2,each=50)), x1=rnorm(100), x2=rnorm(100), x3=rnorm(100)) > lrm(y~., data=dat, x=T, y=T) Error in terms.formula(formula, specials =

Package: logcheck Version: 1.2.39 Severity: normal Since I've upgraded my servers to sarge, I'm getting mail every hour for stuff that was duly included in /etc/logcheck/logcheck.ignore. Turns out that sarge's version no longer reads that file. If this was a conscious decision, then there should be some warning about this when upgrading (via debconf of NEWS.Debian). Also, the

Hi all, I have another question about the lrm function (from the rms library) that I cannot find the answer to. I get an error message when I try to fit a model, and I don't know what to make of it. Please forgive me for not having a toy example, but it appears the size and complexity of my data is somehow causing the error. The best I can do is show you what I type and what errors I get.

Hi, I've come across a strange error when using the lrm.fit function and the subsequent predict function. The model is created very quickly and can be verified by printing it on the console. Everything looks good. (In fact, the performance measures are rather nice.) Then, I want to use the model to predict some values. I get the following error: "fit was not created by a Design

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

...nection, so the client doesn't appear to be broadcasting. Yet it's not closing the connection either. I'm attaching ices2's config file. Any help or pointers would be appreciated; otherwise I'll have to sprinkle some fprintfs everywhere and hope for the best. :( -- Frederic Briere <*> fbriere@fbriere.net => <fbriere@abacom.com> IS NO MORE: <http://www.abacomsucks.com> <= -------------- next part -------------- A non-text attachment was scrubbed... Name: ices2.xml Type: text/xml Size: 2145 bytes Desc: not available Url : http://lists.xiph.org...

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through