similar to: Firths bias correction for log-linear models

hi all, I have a binary data set and am now confronted with a "separation" issue. I have two predictors, mood (neutral and sad) and game type (fair and non-fair). By "separation", I mean that in the non-fair game, whereas 20% (4/20) of sad-mood participants presented a positive response (coded as 1) in the non-fair game, none of neutral-mood participants did so (0/20). Thus,

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"

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 use the lme function from the nlme library (or alternatively from the Matrix library) to estimate a random effects model. Both functions return the covariance matrix of the estimated parameters. I have the following question: Is it possible to retrieve the information matrix of such a model (ie from the fitted object)? In particular, the information matrix can be computed as a sum of individual

Hi All, When I tried to do logistic regression (with high maximum number of iterations) I got the following warning message Warning message: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, As I checked from the Archive R-Help mails, it seems that this happens when the dataset exhibits complete separation. However, p-values

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 >

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

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### Warning messages: 1: algorithm did not converge 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,

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

> ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 9 Jul 2003 10:53:27 +0100 > From: David Firth <david.firth at nuffield.oxford.ac.uk> > Subject: [R] packaged datasets in .csv format > To: r-help at stat.math.ethz.ch > Message-ID: > <307D34CE-B1F3-11D7-A8D2-0050E4C03977 at nuffield.oxford.ac.uk> >

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 All I have been struggling with this model for some time now and I just can't get it to work correctly. The messages I get when running the code is: DLSODA- Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next step (H = step size). Solver will continue anyway. In above message, R = [1] 0 0 DINTDY- T (=R1) illegal In above message, R = [1]

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

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

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

Dear all, I am encountering some issues with my data and need some help. I am trying to run glm analysis with a presence/absence variable as response variable and several explanatory variable (time, location, presence/absence data, abundance data). First I tried to use the glm() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2:

A couple of clarifications for you. 1. I write mixed effects Cox models as exp(X beta + Z b), beta = fixed effects coefficients and b = random effects coefficients. I'm using notation that is common in linear mixed effects models (on purpose). About 2/3 of the papers use exp(X beta)* c, i.e., pull the random effects out of the exponent. Does it make a difference? Not much: b will be

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working