search for: 2log

Dear all, I am a little problem. In the help, AIC = - 2log L + k*edf. When the model is linear, the help said " -2log L is the deviance ". I have a model toto.lm with one output and three input where deviance(toto.lm) = 8.027 and edf =4. But AIC = -31.55. I don't understand why? Many thanks. Jean LEJEUNE Universit? de CAEN (France) -.-...

Dear R - users/Helpers I am dealing with bivariate Normal data with missing values. Further I am trying to implement Expectation-Maximization (EM) algorithm to resolve the missing data problem. Now one of the requirements is use the Log likehood function i.e -2Log so as to find a reliable convergence.... My question is there any R built function for the same or do i have to use the packages contributed by other R Developers. Regards Kunal

...ot;. I went to the R help, and found: "The criterion used is AIC = - 2*log L + k * edf, where L is the likelihood and edf the equivalent degrees of freedom (i.e., the number of free parameters for usual parametric models) of fit. For linear models with unknown scale (i.e., for lm and aov), -2log L is computed from the deviance and uses a different additive constant to logLik and hence AIC. If RSS denotes the (weighted) residual sum of squares then extractAIC uses for - 2log L the formulae RSS/s - n (corresponding to Mallows' Cp) in the case of known scale s and n log (RSS/n) for unknow...

Hello, I have found a problem with using the glm function with a gamma family. I have a vector of data, assumed to be generated by a gamma distribution. The parameters of this gamma distribution are estimated in two ways (i) using the glm() function, (ii) "by hand", using the optim() function. I find that the -2*likelihood at the maximum found by (i) is substantially larger than that

dear List: glm(a~b+c,family=binomial,data=x)->fit deviance(fit) returns the same as the residual deviance. I don't not know much about logistic regression.Some book tells that: " Deviance (likelihood ratio statistic): Deviance = -2log( likelihoodof the currentmodel /likelihoodof thesaturated model) Note: (1). The current model is the model of interest. (2). The saturated model is the full model that considers observed data as parameters, thus there are as many parameters as data points (the full model gives a perfect fit to t...

Dear list members, I have been doing robust regressions in R, using the MASS package for rlm and robustbase for logistic regressions. I must be doing something wrong, because my output does not include r-squares (or adjusted r-squares), or, in the case of glmrob, -2log likelihoods. Does anyone know how to get an output that includes these? Thanks so much for the help Celso -- Celso F. Rocha de Barros DPhil candidate in Sociology, University of Oxford [[alternative HTML version deleted]]

...roblem persist here is my smb.conf i've skipped shares for readability [global]workgroup = HACIENDA_SALTAnetbios name = procuracionrealm = salta02.localpassword server = salta02.salta02.localpreferred master = noserver string = Files Serversecurity = ADSencrypt passwords = truelog level = 2 vfs:2log file = /var/log/samba/%U.%m.logmax log size = 10000syslog = 0 lanman auth = yesclient lanman auth = yesclient plaintext auth = yes name resolve order = wins lmhosts hosts strict sync = yessync always = yeskernel change notify = yesdns proxy = no acl map full control = yesacl check permissions = Tr...

dear all, How can I compare regression coefficients across three (or more) groups? Thank you very much

Hi all, I am trying to use mle() to find a self-defined function. Here is my function: test <- function(a=0.1, b=0.1, c=0.001, e=0.2){ # omega is the known covariance matrix, Y is the response vector, X is the explanatory matrix odet = unlist(determinant(omega))[1] # do cholesky decomposition C = chol(omega) # transform data U = t(C)%*%Y WW=t(C)%*%X beta = lm(U~W)$coef Z=Y-X%*%beta