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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, which is equivalent to the Jeffrey's prior, but I'd also like to get bias corrected estimates for other log...

.... 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 contributions: Sum_i H_i = H, where H_i are the contributions of each case to the Hessian matrix H. Are these individual contributions computed somewhere in either of the functions to estimate random effects models, and is it possible to extract them somehow from the fitted objects? ingmar -- Ingmar Visser Department of...

...scale parameter does not change)? 2) My second question relates to the first. a) given a model from survreg, say mwa above, how should i do to extract the base hazard and the hazard of each patient given a set of predictors? With the hazard function for the ith individual in the study given by h_i(t) = exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that predict(mwa, type='linear') is \beta'x_i. b) since I need the coefficient intercept from the model to obtain the scale parameter to obtain the base hazard function as defined in Collett (h_0(t)...

...scale parameter does not change)? 2) My second question relates to the first. a) given a model from survreg, say mwa above, how should i do to extract the base hazard and the hazard of each patient given a set of predictors? With the hazard function for the ith individual in the study given by h_i(t) = exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that predict(mwa, type='linear') is \beta'x_i. b) since I need the coefficient intercept from the model to obtain the scale parameter to obtain the base hazard function as defined in Collett (h_0(t)...

...to compensate. > 2) My second question relates to the first. > a) given a model from survreg, say mwa above, how should i do to extract the > base hazard and the hazard of each patient given a set of predictors? With the > hazard function for the ith individual in the study given by h_i(t) = > exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that > predict(mwa, type='linear') is \beta'x_i. No, it's beta'x_i for the accelerated failure parametrization of the Weibull. In terms of the CDF F_i(t) = F_0( exp((t+beta'x_i)/sc...

[Sorry, it seems that I overlooked this message -- alex] Hi, Since nvi is hard to come by, here is a little program to mimic the behavior of nvi on any file you care to try... usage: locklogin file_to_lock It first tries POSIX locking and then does BSD flavor. It seems that it is BSD after all that is causing the trouble... Best wishes Andrew #!/bin/csh -f # Uuencoded gz-compressed file

...cumulative hazard function for each subject. 2. Choose an arbitrary value for the variance parameter of the frailties (call it theta). 3. Compute for each subject an estimate of the value of their frailties, USING this variance parameter theta: frailty_i= \frac(1+\theta \times c_i}{1+\theta \times H_i} (formula on p. 321), where H is the cumulative hazard for the subject. So if theta is 0 (no variance), then frailty=1 (i.e., no excess frailty). As theta goes to infinity, the estimated frailty is simply the ratio 1/(cumulative risk so far) or 1/(cumulative risk so far), depending on whether the s...

...d then I'll paste my code. If anyone can spot something wrong with my implementation it would really make my day. (1) dV/dt = (I_ext - I_int-I_coup)/C I_ext = injected current I_int = Sum of all ion currents I_coup = coupling current (but we're not using it here ) (2) I_i = g_i * m_i^pi * h_i^pi(V-E) i identifies the ion, thus I_K would be Potassium current. (3) dm/dt = (m_inf*V - m)/tau_m (4) dh/dt = (h_inf*V-h)/tau_h (5) The Nernst equation is used to calculate reversal potential for Ca: Eca = 12.2396 * log(13000/Ca2+) (6) d[Ca_2+]/dt = (F*I_Ca - [Ca2+] + C0)/Tau_Ca tau_m, tau_h...

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 *