similar to: Censored Brier Score and Royston/Sauerbrei's D

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)) >

Dear R I am trying to understand and use the flexible parametric survival model suggested by Royston and Parmar. However I am stuck trying to plot the adjusted survival curves for different covariates in the following code: library(flexsurv) library(graphics) spl <- flexsurvspline(Surv(futime, fustat) ~ rx+ecog.ps+resid.ds+age, data = ovarian, k=2, scale="odds") spl the code

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

hello I'm learning mgcv and would like to obtain numerical output corresponding to plot.gam. I can do so when seWithMean=FALSE (the default) but only approximately when seWithMean=TRUE. Can anyone show how to obtain the exact values? Alternatively, can you clarify the explanation in the manual "Note that, if seWithMean=TRUE, the confidence bands include the uncertainty about the

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

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

Dear R-list readers: Royston described the effect of sample size on the p-value obtained from the Shapiro-Francia test (Estimating departure from normality. Stat Med 1991;10:1283-93). He developed two indices from the Shapiro-Francia test (i) V' - an index of departure from normality and (ii) v' - a plot of the cumulative squared residuals. He mentioned the availability (in

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

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

Is there a way to tell glm() that rows in the data represent a certain number of observations other than one? Perhaps even fractional values? Using the weights argument has no effect on the standard errors. Compare the following; is there a way to get the first and last models to produce the same results? data(sleep) coef(summary(glm(extra ~ group, data=sleep))) coef(summary(glm(extra ~ group,

An embedded and charset-unspecified text was scrubbed... Name: n?o dispon?vel URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20080528/96325940/attachment.pl>

Hi, I am using logistic regression to classify a binary psychometric data. using glm() and then predict.glm() i got the predicted odds ratio of the testing data. Next i am going to plot ROC curve for the analysis of my study. Now what i will do: 1. first select a cut-off (say 0.4) and classify the output of predict.glm() into {0,1} segment and then use it to draw ROC curve using ROCR package

On Wed, Jul 20, 2005 at 12:00:36PM +0000, freebsd-security-request@freebsd.org wrote: > From: Joachim Str?mbergson <watchman@ludd.ltu.se> > Subject: Adding OpenBSD sudo to the FreeBSD base system? > To: freebsd-security@freebsd.org > Message-ID: <42DCC503.5000408@ludd.ltu.se> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > Aloha! > >

I am new to R, and I am trying to cut a continuous variable BMI into different categories and can't figure out how to use it. I would like to cut it into four groups: <20, 20-25, 25-30 and >= 30. I am having difficulty figuring the code for <20 and >=30? Please help. Thank you. -- View this message in context:

Thank you very much for your help, your fuction runs on R without any change, and the results are the same that your obtain with Splus, but there still a small difference. In SAS W=0.960439 With your shapiro.wilk.test W=0.9606107 But the p-values are almost the same. Thank you very much for your help. > Here is a function for the Shapiro-wilk test that I obtained from StatLib. > Using

I would like to determine the probability of an event at a specific timepoint given the linear predictor of a given Cox model. For instance, assume that I fit the following model: data(pbc) fit <- coxph(Surv(time, status)~ age, data=pbc) To extract the value of the linear predictor for each patient in the dataset: prd <- predict(fit, newdata=pbc, type="lp")

Hello, I am having some trouble with the 'censoring-adjusted C-index' by Uno et al, in the package survAUC. The relevant function is UnoC. The question has to do with what happens when I specify a time point t for the upper limit of the time range under consideration (we want to avoid using the right-end tail of the KM curve). Copying from the example in the help file: TR <-

Hi, I''m trying to create a method to return a random date within a range. So far, I''ve come up with this... require ''parsedate'' def random_date (a, b) adate = ParseDate.parsedate(a) bdate = ParseDate.parsedate(b) atime = Time.local(*adate) btime = Time.local(*bdate) atime + (rand * (atime - btime) ) end ...and it doesn''t seem to

In the next month or two I've got to upgrade a number of servers that are currently on an EOL'd version of 4-STABLE. I foresee that I'll have very limited time to do full OS upgrades on these systems in the coming several years, so I want to make sure I bring them onto an extended-life branch. Right now 4.11 has the furthest projected EOL date (Jan 31 2007), and the projected EOL