search for: liklihoods

Dear all, I have been trying to fit my data (only right censored) with gumbel distribution using fitdistrplus. I am getting very high standard error. I have been wondering why. The followings are the outputs: fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.6), optim.method= "L-BFGS-B", lower = 0.0, upper = Inf) > summary(fit1) FITTING OF THE DISTRIBUTION ' gumbel

(Sorry if this is a repost, I got a bounce reply from the r-help server) Hi, I’m using the biglm() function to create some linear models for a very large data set than lm() can’t fit due to memory issues (the problem is with the number of interactions, I can fit the main effects model) I need to determine if the 2-way interactions are necessary or not. Ideally I’d like to use anova() to

I'm having trouble with adapt. I'm trying to use it in a Bayesian setting, to integrate the posterior distribution, and to find posterior means. I tried using the following script, and things went ok: data = rnorm(100,0.2,1.1) data = c(data,rnorm(10,3,1)) data = data[abs(data)<2*sd(data)] prior = function(x){ dgamma(x[2],shape=2,scale=1)*dnorm(x[1],0,.5) } liklihood =

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Hi, I'm not sure of the correct nomenclature or function for what I'm trying to do. I'm interested in calculated a logistic regression on a binary dependent variable (True,False). There are a few ways to easily do this in R. Both SVM and GLM work easily. The part that I want to add is "group wise" awareness. So that the algorithm computes the coefficients to maximize

Please forgive a rather na??ve question... Could someone please give a quick explanation for the differences in conf intervals achieved via confint.glm (based on profile liklihoods) and the intervals achieved using the Design library. For example, the intervals in the following two outputs are different. library(Design) x = rnorm(100) y = gl(2,50) d = data.frame(x = x, y = y) dd = datadist(d); options(datadist = 'dd') m1 = lrm(y~x, data = d) summary(m1) m2 = glm(y~...

I have this maximum liklihood estimate problem i need to find the roots of the following: [sum (from i=1 to n) ] ((2(x[i]-parameter)/(1+(x[i]-parameter)^2))=0 given to me is the x vector which has length 100 how would I find the roots using R? I have 2 thoughts...... 1 is using a grid search ... eg. brute force, just choosing a whole bunch of different values for my parameter .... such as

Apologies up front for failing to find related posts... Am I overlooking a way to get ''zfs send -i fs at 0 fs at 1 | zfs receive -n -v ...'' to show the contents of the stream? I''m looking for the equivalent of ufsdump 1f - fs ... | ufsrestore tv - . I''m hoping that this might be a faster way than using ''find fs -newer ...'' to learn

Hi: I have a query related to the J and Jcross functions in the SpatStat package. I use J to finding indications of clustering in my data, and Jcross to look for dependence between point patterns. I use the envelope function to do Monte Carlo tests to look for significance. So far so good. My question is how I can test to see if tests are significantly different. For example, if find J of

Hi All I have a data frame called "nbd" with two columns (x and T). Based on this dataset I want to find the parameters of a distribution with the following log-liklihood function and with r and alpha as its parameters: log(gamma(nbd$x+r))-log(gamma(r))+r*log(alpha)-(r+nbd$x)*log(nbd$T+alpha) the initial value for both parameters is 1. I would be thankful if you could help me

Hi All I have to run multiple stimations and to compute Likelihhod ratio. If I compute ls function with coef and summary I can extract "outputs" that I need. I am not able to find something similar to log liklihood.... Can you pease tell me running a ls function x on y how to extract if posible LR statitic or Likelihood or Log likelihood. Many thanks in advance. If you send me

This is not related to R but I was hoping that someone could help me. I am reading the "Understanding the Metropolis Hastings Algorithm" paper from the American Statistician by Chip and Greenberg, 1995, Vol 49, No 4. Right at the beginning they explain the algorithm for basic acceptance rejection sampling in which you want to simulate a density from f(x) but it's not easy and you

Greetings! I am looking for advice regarding the best way to compare GAMMs. I know other model outputs return enough information for R's AIC, ANOVA, etc. commands to function, but this is not the case with GAMM unless one specifies the gam or lme portion. I know these parts of the gamm contain items that will facilitate comparisons between gamms. Is it correct to simply use these values

Hi all, People watching the git commits might have noticed that I have been fixing a number of issues around undefined behaviour. Why you ask? * Some forms of undefined behaviour have potential for security exploits. * Compiler writers are free to replace anything which invokes UB with a NOP or even, nothing at all. * Having large numbers of UB warnings makes it difficult (or rather time

>From: Sundar Dorai-Raj <sundar.dorai-raj at PDF.COM> >Reply-To: sundar.dorai-raj at PDF.COM >To: Zhen Pang <nusbj at hotmail.com> >CC: r-help at stat.math.ethz.ch >Subject: Vectorising and loop (was Re: [R] optim "a log-likelihood >function") >Date: Wed, 29 Sep 2004 18:21:17 -0700 > > > >Zhen Pang wrote: > >> >>I also use

Hello, First time poster here so let me know if you need any more information. I am trying to run an ordered probit with maximum likelihood model in R with a very simple model (model <- econ3 ~ partyid). Everything looks ok until i try to run the optim() command and that's when I get " Error in rep(1, nrow(x)) : invalid 'times' argument". I had to adapt the code from a 4

On Wed, 11 Nov 2015 11:13:43 -0800, Daniel Berlin via llvm-dev wrote: <snip for gmane> > Heck, i could even reason about inline asm if i wanted to ;-). > > My point is that this call is super special compared to all other > calls, > and literally everything in LLVM has to understand that. > The liklihood of subtle bugs being introduced in functionality (IE >

On Wed, Nov 11, 2015 at 10:41 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > On Wed, Nov 11, 2015 at 10:32 AM, Reid Kleckner <rnk at google.com> wrote: > >> I think the idea is to model the intrinsic as a normal external function >> call: >> > - Can read/write escaped memory >> > - Escapes pointer args >> - Functionattrs cannot infer

...MER fit ph.fit.3<-glmer(poorhealth~RURAL+SMOKE+DRINK+EMPLOYED+INSURANCE+highereduc+INDIG+AGE+male+divorced+SINGLE+chronic+vigor_d+moderat_d+(1|munid),family=binomial(), data=mx.merge) I cannot find a method in R that will do the LR test between a glm and a glmer fit, so I try to do it using the liklihoods from both models #form the likelihood ratio test between the glm and glmer fits x2<--2*(logLik(ph.fit.2)-logLik(ph.fit.3)) > ML 79.60454 attr(,"nobs") n 45243 attr(,"nall") n 45243 attr(,"df") [1] 14 attr(,"REML") [1] FALSE attr(,&q...

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between