similar to: Reliability analysis and Laplace factor functions

I'm trying to fit a Weibull distribution to some data via maximum likelihood estimation. I'm following the procedure described by Doug Bates in his "Using Open Source Software to Teach Mathematical Statistics" but I keep getting warnings about NaNs being converted to maximum positive value: > llfunc <- function (x) { -sum(dweibull(AM,shape=x[1],scale=x[2], log=TRUE))} >

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 of > predicted probabilities. The Hosmer-Le Cessie

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

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Does anyone have any R code that shows how to do a Laplace Approximation? I know there are a variety of these numerical approximation algorithms and I'm pretty open at this point, I'm just curious how it's approximated in R code. I have seen some functions in packages, but I think they all call C code, and am looking for an example in R only. Thanks. -- View this message in

Can anyone show how to calculate a multivariate Laplace density? Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Multivariate-Laplace-density-tp3864072p3864072.html Sent from the R help mailing list archive at Nabble.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

Dear users, is anybody of you aware of a R command to perform laplace transform or even its inversion? Thank you very much. Luca

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

Dear R useRs, A new package 'Reliability' is now available on CRAN. It is mainly a set of functions functions for estimating parameters in software reliability models. Only infinite failure models are implemented so far. This is the first version of the package. The canonical reference is: J.D. Musa, A. Iannino, and K. Okumoto. Software Reliability: Measurement, Prediction,

Hello! My question is stated in the Subject: Is it possible to model with Laplace's error distribution? For example, lmer() function have few families of functions, like binomial etc., but not Laplace. Is there any other package that would allow for Laplace? Or is there a way to give "user-defined" family? Sincerely, P. Milin

Max Okumoto <okumoto@ucsd.edu> wrote: [CC changed to freebsd-security instead of the cvs list] We're talking about replacing the home-grown mkfifo() funktion in make (a modified copy of mkstemp()) with mkdtemp() and creating the fifo in this new directory. Max worries about a possible race with this new approach. > Its not a race between two nice programs :-) The function

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

I've created an SVM in R using the kernlab package, however it's running incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution). CPU is running at 25% and RAM utilization is a mere 17% ... it's not a hardware bottleneck. Similar calculations using data mining algorithms in SQL Server analysis services run about 40x faster. Through trial and error, we

Hi there, Is there an implementation of the Gaveh Stehfest algorithm in R somewhere ? Or some other inversion ? Thanks, Tolga

Hi all; I am sure this question has been brought up before, but I could not find any information in the FAQ or docs etc. Quick question; Is there a way of syncing multiple servers simultaneously from 1 source server? Thanks, -- Vaibhav Goel extension 6347

Dear all, I am trying to understand the output from a binomial lmer object and why the scaling of a fixed effect changes the variance components. In the model p2rec is cbind(number recruits2,number recruits 1), Pop is populations (five level factor) and ja is year (covariate running from 1955-2004). I.e. biologically I am interested to see how the proportion of recruits from the second

Hi I am new to Linux. I am using Redhat 9.0. I have installed the Samba software, configured the smb.conf, added my client Windows XP box into the hosts.allow, hosts.equiv etc and started the smb service. Still I am unable to map the Linux share on to my Windows XP box. I have tried enabling and disabling the XP firewall also. I can see the Linux machine name in the Windows explorer "My

> On Mar 24, 2015, at 3:22 PM, Xinliang David Li <davidxl at google.com> wrote: > > On Tue, Mar 24, 2015 at 2:47 PM, Bob Wilson <bob.wilson at apple.com <mailto:bob.wilson at apple.com>> wrote: >> >>> On Mar 24, 2015, at 12:08 PM, Xinliang David Li <davidxl at google.com> wrote: >>> >>> On Tue, Mar 24, 2015 at 10:54 AM, Bob

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model