search for: rescorla

..., hessian=TRUE) Warning messages: 1: NaNs produced in: dweibull(x, shape, scale, log) 2: NA/Inf replaced by maximum positive value 3: NaNs produced in: dweibull(x, shape, scale, log) 4: NA/Inf replaced by maximum positive value > Can someone offer some advice here? Thanks, -Ekr -- [Eric Rescorla ekr at rtfm.com] http://www.rtfm.com/

I've been using R to do logistic regresssion, and that's working well, but there are two things I haven't figured out how to do. (1) Is there some pre-existing function that will let you compute the odds ratios and confidence intervals for them for a specific fit. I know how to do this manually or even write a function that I can call with the coefficients and se, but

I've noticed that glm and lrm give extremely different results if you attempt to fit a saturated model to a dataset with zero cells. Consider, for instance the data from, Agresti's Death Penalty example [0]. The crosstab table is: , , PENALTY = NO VIC DEF BLACK WHITE BLACK 97 52 WHITE 9 132 , , PENALTY = YES VIC DEF BLACK WHITE BLACK 6 11

Is there some package out there that implements functions for reliability analysis, especially for software reliability? In particular, I'm looking for: * Laplace factor (Cox & Lewis 1978) * Goel-Okumoto fitting Thanks in advance, -Ekr -- [Eric Rescorla ekr at rtfm.com]

Frank E Harrell Jr wrote: > Eric Rescorla <ekr at rtfm.com> wrote: > >> (2) I'd like to compute goodness-of-fit statistics for my fit >> (Hosmer-Lemeshow, Pearson, etc.). I didn't see a package that >> did this. Have I missed one? > > Hosmer-Lemeshow has low power and relies on arbitrary binning o...

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Hi, I've been banging my head against the following problem for a while and thought the fine people on r-help might be able to help. I'm using the survival package. I'm studying the survival rate of a population with a preexisting linear-like event rate (there are theoretical reasons to believe it's linear, but of course it's subject to the usual sampling noise) Some of the

Hi, I'm doing some coxph fits using the survival package. There are a large number of potential predictors and so I was considering using stepAIC for model selection. However, in the early stages I'm getting complaints like the following: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Loglik converged before variable 1,2 ; beta may be infinite.