search for: likelihood

Hi all, I'll preface this with saying I've gone through the archives, and am still in need of some help. I've been using this likelihood model with mean = 0 and s.d. = sqrt( (c + ( 1 / N1 ) + ( 1 / N2 ) ) * x * ( 1 - x )), where c is a genetic drift parameter (usually very small, like between .005 - .001), N1 and N2 are my population sizes (~200), and x is a value between 0 and 1. The values I'm testing are usually between -.25...

Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use quasi-likelihood estimates? If not, how can I compare them? 2- Why the large differences in likelihood estimates between the two procedures? Thanks, Nestor

Dear all, is there a way to extract individual likelihoods from a glm/lrm object? By individual likelihoods, I mean the likelihoods whose product give the overall likelihood of the model. I guess the code in the base package, used to compute the Akaike Information Criterion may help me. However, I couldn't figure it out, probably because I'm rathe...

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the log-likelihood with other packages such as KFAS and FKF, the logli...

Dear Sir/Madam, I am using R version 1.8.1. I am doing following tast: First generate 100 Gaussion(3,1) numbers, then write the likelihood function to estimate the parameters of Gaussian distribution by direct maximizing the likelihood function. My likelihood function is: >fn<-function(x) >(-50*log((sd(x))^2))-50*log(sqrt(2*pi))-(1/2*((mean(x))^2))*(sum((x-(mean(x))^2)) After I input above function, the '' >...

I was playing around with the ghyp package and simulated series of t-distributed variables when suddenly i was not able to reproduce the log likelihood values reported by the package. When trying to reproduce the likelihood values, I summed the log(dt(x,v)) values and it worked with some simulated series but not all. Is there any obvious flaws with this script? library("ghyp") series_1=rt(10000,4) #simulating 10000 relation of stude...

I'm fitting glm models and the summary gives the deviance of the model . I would like to obtain the log likelihood How can I do ? Thanks -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:...

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers & happy holidays, Peter

Dear R users, I'm trying to fit a simple random intercept model with a fixed intercept. Suppose I want to assign a weight w_i to the i-th contribute to the log-likelihood, i.e. w_i * logLik_i where logLik_i is the log-likelihood for the i-th subject. I want to maximize the likelihood for N subjects Sum_i {w_i * logLik_i} Here is a simple example to reproduce # require(nlme) > foo <- Orthodont > lme(distance ~ 1, random =...

Hi there, I'm working with clustered data sets and trying to calculate log-likelihood (and/or AIC, AICc) for my models. In using the gee and geese packages one gets Wald test output; but apparently there is no no applicable method for "logLik" (log-likelihood)calculation. Is anyone aware of a way to calculate log-likelihood for GEE models? Thanks for the help, Bruce

I've computed a loglinear model on a categorical dataset. I would like to test whether an interaction can be dropped by comparing the log-likelihoods from two models(the model with the interaction vs. the model without). Since R does not immediately print the log-likelihood when I use the "glm" function, I used SAS initially. After searching for an extracting function, I found one in R. But, the log-likelihood given by SAS is diffe...

My question is with respect to mCLUST and the values of BIC and log likelihood. The relevant part of my R script is: ######################### BEGIN MDS ANALYSIS ######################### #load data data <- read.table("Ecoli33_Barry.dis", header = TRUE, row.names = 1) #perform MDS Scaling mds <- metaMDS(data, k = Dimensions, trymax = 20, autotransform =T...

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires computing the partial derivatives of that formul...

Dear all, I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings...

I forgot to add that I first gave a starting value for K. Nonlinear least squares won't work because my errors are not normally distributed. Any advide on my maximum likelihood function would be greatly appreciated. ---------------------------- Original Message ---------------------------- Subject: RE: [R] maximum likelihood problem From: "Ravi Varadhan" <rvaradhan at jhmi.edu> Date: Fri, October 1, 2010 5:10 pm To: mlarkin at rsmas.miami.edu...

Dear All, I have a question concerning the computation of the value of the Restricted Maximum Likelihood (REML) function evaluated at a given set of parameter estimates from the Maximum likelihood (ML) value. Following the book of Fitzmaurice, Laird and Ware (2004) "Applied Longitudinal Analysis" pp101, the REML likelihood can be computed by multiplying the ML likleihood by the square root o...

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests? It seems that they should be treated as quasilikelihoods which would make stepAIC() invalid and would require the use of add1() with a F-test for the reduction in deviance. An...

Two related questions. First, I am fitting a model with a single predictor, and then a null model with only the intercept. In theory, the fitted model should have a higher log-likelihood than the null model, but that does not happen. See the output below. My first question is, how can this happen? > m Call: glm(formula = school ~ sv_conform, family = binomial, data = dat, weights = weight) Coefficients: (Intercept) sv_conform -2.5430 0.2122 Degrees of Free...

Hi all, I'm working on a multinomial (or "polytomous") logistic regression using R and have made great progress using multinom() from the nnet library. My response variable has three categories, and there are two different possible predictors. I'd like to use the likelihoods of certain models (ie, saturated, fitteds, and null) to calculate Nagelkerke R-squared values for various fitted models. My question today is simple: once I have fitted a model using multinom(), how do I find the likelihood (or log likelihood) of my fitted model? I understand that this value...

Hi R community, I have some data set and construct the likelihood as follows likelihood <- function(alpha,beta){ lh<-1 d<-0 p<-0 k<-NULL data<-read.table("epidemic.txt",header = TRUE) attach(data, warn.conflicts = F) k <-which(inftime==1) d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta) p<-1 - exp...