similar to: warning associated with Logistic Regression

Dear R-Help List, I'm trying to implement Firth's (1993) bias correction for log-linear models. Firth (1993) states that such a correction can be implemented by supplementing the data with a function of h_i, the diagonals from the hat matrix, but doesn't provide further details. I can see that for a saturated log-linear model, h_i=1 for all i, hence one just adds 1/2 to each count,

> > LLVA specifically is refering to a research project offshoot of llvm. > > LLVM instructions do not have 1:1 mappings to native instructions > > (sometimes multiple llvm instructions map to fewer native insts, > > sometimes the other way around). > > That's correct, and furthermore, LLVA (now called SVA = Secure > Virtual Architecture) uses essentially the

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Hi, I got two questions and would really appreciate any help from here. First, is the penalized maximum likelihood estimation(Firth Type Estimation) only fit for binary response (0,1 or TRUE, FALSE)? Can it be applied to multinomial logistic regression? If yes, what's the formula for LL and U(beta_i)? Can someone point me to the right reference? Second, when I used *logistf *on a dataset with

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Hi, we try to do a logistic regression with the function glm. But we notice that this function don't give the same results as the SAS proc catmod (differents estimate given). We try to change the contrast on R system with: > options(contrasts=c(unordered="contr.SAS",ordered="contr.poly")) We also try with brlr and logistf functions. Unfortunately, the estimate

I have a very simple problem. When using glm to fit binary logistic regression model, sometimes I receive the following warning: Warning messages: 1: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start,

Dear Colleagues I have been fitting some multinomial logistic regression models using R (version 1.6.1 on a linux box) and Stata 7. Although the vast majority of the parameter estimates and standard errors I get from R are the same as those from Stata (given rounding errors and so on), there are a few estimates for the same model which are quite different. I would be most grateful if

dear community, I am a beginer in R , and can't predict with logistic model in package logistf, could anyone help me ? thanks ! the following is my command and result : >library(logistf) >data(sex2) >fit<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sex2) >predict(fit,newdata=sex2) Error in predict(fit, newdata = sex2) : no applicable method for "predict"

This is the situation I have: All my hosts are the* same OS.* All my host are in the* same puppet environment,* so I cannot use %{environment} I have a module that sets all the *basic* functionality for the OS, resolution, authentication, security, packages, etc I have a module for each application hosted. At the moment all the ''data'' is in Puppet, mostly in parametrised

On Apr 3, 2007, at 10:55 AM, Andrew Lenharth wrote: > On 4/2/07, Fabian Scheler <fabian.scheler at gmail.com> wrote: >> Hello everybody, >> >> I'm curious whether there have been any attempts to perform >> performance analysis on the LLVA level. I am interested in the >> derivation of flow-facts (loop bounds etc. - what about the >>

Dear All, I'd like to fit a "kind" of logistic model to small data-set using nonlinear least-squares regression. A transcript of R-script are reproduced below. Estimated B and T (the model's coeff, herein B=-8,50 and T=5,46) seem appropriate (at least visually) but are quite diff from those obtained w/ SPSS (Levenberg-Marquardt): B=-19,56 and T=2,37. Am I doing something wrong in

Hello, R experts: I got data like this: group duplicate treatment A Y 5 A Y 3 A N 6 B Y 2 B N 4 B Y 1 How to sort the data and calculate the average treatment value for each group in two level of duplicate. Results like this: group duplicate treatment A Y 4 A N

Hi, I've been using the Matlab robustfit function for linear regressions where I suspect some data points are outliers. Is there an equivalent function in R? Take care, Darren PS, This is the Matlab help on robustfit: >> help robustfit ROBUSTFIT Robust linear regression B = ROBUSTFIT(X,Y) returns the vector B of regression coefficients, obtained by performing robust

Does anyone know what algorithm R uses in glm, family=binomial (i.e. a logit model)? I assume that it's in the source somewhere, but I wasn't able to find it. I'd like to know what file it's in (in a unix distribution of R). Thanks for your help. --------------------------- Barnet Wagman wagman at enteract.com 1361 N. Hoyne, 2nd floor Chicago, IL 60622 773-645-8369

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

Dear all, I know that when using R, people should have a sufficient level in statistics. As well, I'm not a genius, when dealing with logistic regressions. I would like to construct ICs, IPs, for a logistic regression, but the point is I have just 41 observations. I had a look at the Design package and noticeably the lrm function, but I'm still not able to reduce the IC's, as I

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine >

Is there a straightforward way to convert character information from decimal representation to the octal one used by R? I'd like something like a function ascii(number,base=10), such that > ascii(91) [1] "\133" I can easily do the mapping from 91 to 133, but what is a good way to operate on 133 to deliver "\133"? Would a lookup table be a better solution? David

Hi, I'm currently using biglm package to compute GLM outputs on a very large dataset. But no function computes standard erros of predictions. I look in what is done in R, namely in the function predict.glm.R in stats package. In this function, we call predict.lm to compute the standard errors (line 51). The code of predict.lm (in lm.R) is very hard to understand. I wonder if there is any