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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,

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

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

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

...;-B%>&[60ZTXP9SR&6@10&EL*]F=3D/K%''DA MM$&3T1IP8.B-NL.GP>M:7_O6M!:X<-2VS@;BJT*AJK`UN/-G?A&.!);H0@W% M`=<!07]P3)`ZVFNBVM:9)Y<)5I8>O,EHT.EO7!Z4?!Z.''Z>@AXQLW265]7IY MSZ^^2D1"1PF->R^#_L0!7=K^NXVB@XU*#I@);''AQ"HBVW2;0UHU::S5J\H_Y M,B8,(AC&B9E!*3]Q3<7`CD+WX^[)#@VF.QP,.WVO-_V?K6V?_R''\N<0[AGDP -KCKDUB\"@@\%S`,````` ` end -- Linux-PAM: http://parc.power.net/morgan/Linux-PAM/index.html libpwdb: http://parc.power.net/morgan/libpwdb/index.html

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 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]

Sorry for the repeat. The PRESS statistic is defined as sum(y-yhat(i))^2, where yhat(i) denotes the ith predicted value using all the data except the ith case (as used typically in linear models). Thanks again Jacob Jacob L van Wyk Department of Mathematics and Statistics Rand Afrikaans University P O Box 524 Auckland Park 2006 South Africa Tel: +27-11-489-3080 Fax: +27-11-489-2832

depends on "autoconf: regenerate configure scripts with 4.4 version" This series removes some of the really old deadwood from the tools build and makes some other things which are on their way out configurable at build time with a default depending on how far down the slope I judge them to be. * nuke in tree copy of libaio * nuke obsolete tools: xsview, miniterm, lomount & sv *