search for: liklihood

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

...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 = function(x,val){ prod(dnorm(val,m=x[1],sd=sqrt(.5/x[2])))#/(pnorm(2,x[1],x[2])-pnorm(-2,x[1],x[2]))^length(val) } unsc.post = function(x,val){ prior(x)*liklihood(x,val) } cons = adapt(2,c(-5,0),c(5,10),f=unsc.post,min=1e+04,max=5e+05,val=data) However, if I try to add a skewness parameter usin...

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 [[alternative HTML version deleted]]

...terested 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 the liklihood of of a "True" label per group. An toy explanation is probably best. I've been looking at horse racing models as a fun field to learn about statistics and R. So, for this example, lets assume the following: 100 horses in our stable 10 horses per race 75 races this season (some ho...

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 choo...

...#39; to learn what''s changed between fs at 0 and fs at 1. I''d probably use this functionality to produce a list of files to backup with cpio. I don''t want to make archival backups using the stream produced by ''zfs send'' because of caviots regarding the liklihood of change in future releases. If not ''zfs receive, is there another utility that would do this job? Thanks. This message posted from opensolaris.org

...ook 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 pattern X and J of pattern Y, how could I determine the liklihood that those results come from different processes? Similarly, if I find J of marks X and Y, and X and Z, how could I determine the liklihood that Y and Z come from different processes? I would appreciate advice. Cheers Robert Biddle

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 to solve this problem in R. Regards, Ali [[alternative HTML version delete...

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 some manual or webpage guide I will be very happy. I will try to improve my posts and questions. [[alternative HTML version delet...

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

...unction, 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 for this purpose? For example, the lme portion of the gamm returns a log liklihood value that could be used to calculate information criteria. However, I am wondering whether entire gamms be compared using this, or only the lme part. Maybe my thinking about the lme and gam portions of gamms is incorrect? If this appears to be the case, let me know! In general, if someone co...

...er 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 consuming) to check them all for the possibility that they are a potentially exploitable. Most of these UB changes have little liklihood of performance regressions. However, this one: commit 1b8af6bb45a9ad74fa374fb6414974e63ffc793b Author: Erik de Castro Lopo <erikd at mega-nerd.com> Date: Sat Aug 29 05:21:43 2015 +1000 libFLAC/fixed.c: Fix undefined behaviour Left shift if a negative integer such...

...one for example, >> >> l<-i:(k-j+i) >> s<-rep(0,k) >> s[l]<-choose(j,i)*choose((k-j),(l-i))/choose(k,l) >> ss<-sum(s*x0) >> >>then sum all the log(ss) is my log-liklihood function. >> >>One loop from 1 to 200 is inevitable. I have tried to use vector, however, >>I only can simply to this situation. Thanks. >> >>Regards, >> >>Zhen >> > >Zhen, > Your question doesn't really have much to do with optim, so...

...g Republican). Here is the code I used: setwd("C:/Users/Terry/Desktop/Terry/School/Fall 2012/ML/HW") #Load Libraries library(MASS) library(tile) library(simcf) #Load Data econrate <- read.csv("hw4econ3.csv", header=TRUE, sep=",") attach(econrate) #Ordered Probit Liklihood llk.oprobit3 <- function(param, x, y) { os <- rep(1, nrow(x)) x <- cbind(os, x) b <- param[1:ncol(x)] t2 <- param[(ncol(x)+1)] xb <- x%*%b p1 <- log(pnorm(-xb)) if (t2<=0) p2 <- -(abs(t2)*10000) else p2 <- log(pnorm(t2-xb)-pnorm(-xb)) p3 <- log(1-...

...: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 > analysis/etc doing the wrong thing because it is not special cased) > seems super high to me. I do agree this is a concern. >> I don't know how you could practically deploy a super-duper LTO mode >> that doesn't all...

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(,&...

...a produces F values close to 0 and p values close to 1 (see example below). Is this a bug, or am I doing something fundamentally wrong? If anova doesn't work with lmer is there a way to perform hypothesis tests on fixed effects in an lmer model? I was going to just delete terms and then do liklihood ratio tests, but according to Pinheiro & Bates (p. 87) that's very untrustworthy. Any suggestions? I'm using R 2.1.1 on windows XP and lme4 0.98-1 Any help will be much appreciated. many thanks Robert ############################### The data are somewhat like this #setting up the...