similar to: Modifying glm.fit() / execution path

Dear R users, I use the glm() function to fit a generalized linear model with gamma distribution function and log link. I have read in the help page that the default method used by R is "glm.fit" (iteratively reweighted least squares, IWLS). Is it possible to use maximum likelihood method? Thanks Silvia Narduzzi Dipartimento di Epidemiologia ASL RM E Via di S. Costanza, 53 00198

1. You have levels(A) as "2" and "4", yet you showed equations for A=0 and A=1? 2. y = A + X + A*X means you're allowing the different groups of A to have different slopes. Probably not what you intended. 3. It's probably best to provide a small sample of the data (and R code) so we know how you got what you got. Andy From: Ben Ridenhour > > Hello, >

Hello all, I'm trying to establish some confidence intervals on predictions I am making using >predict(nls(...)) and predict.nls (unfortunately) does not utilize the se.fit option. A little more background is that I am trying to match the output with older SAS routines to maintain consistency. Because predict.nls does not provide se's for individual predictions, I have been using a

Full_Name: saraux Version: 2.6.1 OS: Windows vista Submission from: (NULL) (193.157.180.37) I would like to compute a glm with a distribution of poisson, using a maximum of likelihood method. But it seems not to work with a distribution of poisson. The same code with another distrubution (binomial for example) works. Here is the command I typed:

Hello, I am new to using R and I am having problems get boot() to work properly. Here is what I am trying to do: I have statistic called "cs". cs takes a data matrix (154 x 5) and calculates 12 different scores for me. cs outputs the data as a vector (12 x 1). cs doesn't really use weights, per se, however I have included this as one of the 2 arguments cs can take. I try

Hi all, I have a question regarding how to properly analyze a data set and then how to perform the analysis in R. First, I have data that I would like to analyze using a mixed GLM (I think this is the most appropriate method, but I am unsure). In a mixed model (y = X*beta+Z*gamma+epsilon), I would like to structure the variance matrices of gamma, G, and the error, R, to take advantage of all my

Hi, I am thinking about IWLS vs ML estimation. When I use glm() for a 2-parameter distribution (e.g., Weibull), I can otain the MLE of scale parameter given shape parameter through IWLS. Because this scale parameter usually converges to the MLE. In this point, I am wondering: i) can you say that the direct MLE, which is obtained by maximizing a likelihood function, is equalvant to the indirect

I am thoughly confused with the various Xen 3.0.3 versions for Suse. Firt there is not for suse 10.1, so I gues I woulllt the source tarball and compile. ( I tried the RPM for 10.0 (I am running SuSE 10.1) but it caused a kernel panic) I have 5 packages listed unter Yast software management (not including documentation,pdf, etc). Kernel-xen ver 2.6.16.21.25 Xen 3.02_09763-0.8 xen-libs

Hello, just wanted to ask if there are any other headers icecast would understad apart icecast-auth-user icecast-auth-message icecast-auth-timelimit ? continuing my previous post "listener redirection to informational mount" i feel a big demand to respond to unsuccsessful authentification attempts by sending back short audio message to the listener in the form like this: <?php

Thanks for the information Lee ! Still, something is still strange to me, since this two Panasonic Fax machines are receiving at least 20 multi-paged faxes a day (they are in same office as both Asterisk boxes, and me :) ). Beside that, POTS lines in those two faxes are from same PSTN switch as line in X100P in one of Asterisk boxes, and the ISDN PRI lines in other Asterisk box is from same PSTN

hi... i want to use maildir format so like i see un some manuals i create this directory for my virtual users: # ls -la /export/home/vmail/prueba.uy/t* /export/home/vmail/prueba.uy/toto1: total 4 drwxr-xr-x 2 vmail vmail 512 Apr 10 15:09 . drwxr-xr-x 6 vmail vmail 512 Apr 9 12:11 .. /export/home/vmail/prueba.uy/toto3: total 10 drwxr-xr-x 5 vmail vmail 512 Mar

Hi! I tested two different implementations of the robust MCD estimator: cov.mcd from the MASS package and covMcd from the rrcov package. Tests were done on the hbk dataset included in the rrcov package. Unfortunately I get quite differing results -- so the question is whether this differences are justified or an error on my side or a bug? Here is, what I did: > require(MASS) >

I do not seem to understad what this error is about. Some body help. wrong number of arguments (1 for 2) RAILS_ROOT: C:/INSTAN~1/rails_apps/project/config/.. Application Trace | Framework Trace | Full Trace #{RAILS_ROOT}/app/controllers/user_controller.rb:10:in `authenticate'' #{RAILS_ROOT}/app/controllers/user_controller.rb:10:in `process_login''

Hi all, I have a dataset of 2000 numbers ( it's noise measured with a scoop ) Now i want to know of my data is normal distributed (Gaussian distribution). I did already: - 68-95-99.7 test - Q-Q-plot and now i used "nortest library" and the Lilli.test() However i don't understad the output? lillie.test(z) Lilliefors (Kolmogorov-Smirnov) normality test data: z D =

What kind of special magic does glm have? I'm working on a logistic regression solver (L-BFGS) in c and I've been using glm to check my results. I came across a data set that has a very high condition number (the data matrix transpose the data matrix) that when running my solver does not converge, but the same data set with glm was converging ( I love R :) ). I noticed that glm using

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Hi, Does anyone know if there is a function to find the maximum likelihood estimates of glm using Newton Raphson metodology instead of using IWLS. Thanks Valeska Andreozzi -------------------------------------------------------- Department of Epidemiology and Quantitative Methods FIOCRUZ - National School of Public Health Tel: (55) 21 2598 2872 Rio de Janeiro - Brazil

We have a choice when calculating the Huber location estimate: > set.seed(221205) > y <- 7 + 3*rt(30,1) > library(MASS) > huber(y)$mu [1] 5.9117 > coefficients(rlm(y~1)) (Intercept) 5.9204 I was surprised to get two different results. The function huber() works directly with the definition whereas rlm() uses iteratively reweighted least squares. My surprise is

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,

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *