similar to: val.prob in the Design package - Calibrated Brier Score

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

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'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)) >

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

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 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

Hi, after upgrading my R install from 2.5 -> 2.6.1 and performing multiple iterations of update.packages(), I am getting an odd error when trying to plot a calibration curve from the val.prob() function in package Design. when running this function (which used to work) I get the following error message: Error in .C("lowess", x = as.double(xy$x[o]), as.double(xy$y[o]), n,

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 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

Good afternoon! I need to evaluate the goodness-of-fit (aka calibration) for survival probability estimates from a Cox model. I tried to use 'calibrate' in the Design package but I'm not sure if it should/would produce what I need (ie a chi-sq type statistic with a table of expected vs observed probabilities). Any other functions I should be aware of? Also, has anybody come across

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

Dear R users: In Prof. Harrell's library rms, calibrate.rms plot the Bias-corrected Probability and Apparent Probability. The latter one can be retrieved from class calibrate.default. But how to retrieve the former one. BW *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

Greetings: I'm running R-1.9.1 on Fedora Core 2 Linux. I tested a proportional odds logistic regression with MASS's polr and Design's lrm. Parameter estimates between the 2 are consistent, but the standard errors are quite different, and the conclusions from the t and Wald tests are dramatically different. I cranked the "abstol" argument up quite a bit in the polr

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

Hi, I have a binary variable and corresponding predicted probability (using logistic regression on some explanatoey variables); I want to check that the model is well-calibrated using a calibration plot. how can I have the calibration plot for my data? thanks. [[alternative HTML version deleted]]

Hi, I created a logistic regression model using the glmnet package. This model is of class "glmnet" or "lognet". I wanted to plot a calibration curve for this model using the calibrate() function from rms package, but the objects used are different, rms requires a fit from lrm(). Is there another function for getting the calibration plot for this glmnet object, or can anyone

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

On Sat, Feb 1, 2014 at 1:16 PM, Alexandre Courbot <acourbot at nvidia.com> wrote: > GK20A's timer is directly attached to the system timer and cannot be > calibrated. Skip the calibration phase on that chip since the > corresponding registers do not exist. Just a curiosity: What timer resolution does the HW initialise at? > > Signed-off-by: Alexandre Courbot <acourbot

Hello! I'm having an echo problem with a TDM card. The TDM card is being fed by a channel bank just 12 or so feet away. When you put an analog handset on the line, both the RX and TX volume seem to be just fine. However, when I use the TDM card, I have to have an rxgain of 13.5, and even then, the audio is relatively quiet. I'm also getting echo on these lines, so I have turned

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 =