similar to: Loglikelihood for x factorial?

Hi R users Is there anything wrong in "is" function? (R 2.7.2) I believe that everyone will agree that "7" is an integer, right? but why R shows 7 is not an integer > is.integer(7) [1] FALSE > is(7,"integer") [1] FALSE > is(as.integer(7), "integer") [1] TRUE Thank you very much in advance Chunhao

Hi R users Is there anything wrong in "is" function? (R 2.7.2) I believe that everyone will agree that "7" is an integer, right? but why R shows 7 is not an integer > is.integer(7) [1] FALSE > is(7,"integer") [1] FALSE > is(as.integer(7), "integer") [1] TRUE Thank you very much in advance Chunhao

Hi R users, I have one simple question but I really don't know why I can't get it work. The data was adopted from Efron's An introduction to the bootstrap book. > nlaw LSAT GPA [1,] 576 3.39 [2,] 635 3.30 [3,] 558 2.81 [4,] 578 3.03 [5,] 666 3.44 [6,] 580 3.07 [7,] 555 3.00 [8,] 661 3.43 [9,] 651 3.36 [10,] 605 3.13 [11,] 653 3.12 [12,] 575 2.74

Hi R users, Is is possible for me to use the try function with boot? I would to do the bootstraping with a nonlinear model(it works well when R < 1000). But it does not work very well (when R is large) thus I try to use "try" to resolve. I put the try function in two cases: case1: put the try in front of the boot > c1.try<-try(boot(c1data, statistic = c1.fun,

First thanks for Jinsong's suggestions I would like to do a bootstrap in a nonlinear model. But it fails to converge in most of time. (it did converge if I just use nls without boot). Thus, I use "try" function to resolve my problem. This following code is from Jinsong's suggestion. h1a.nls<-nls(density~nmf(time, alpha, delta, psi, tau, gamma),data=h1a,

Dear R Helpers, I try to write a small package that based on nlme however my code does not work. R always appears this message: Error in eval(expr, envir, enclos) : object "y" not found where y is the response variable. Please help me out! This is my code: require(nlme) AMPmixed<-function(y, x, S1=c("asymptotic","logistic"), random,data,

Dear R experts, Attached you can find the expression of a loglikelihood function which I would like to maximize in R. So far, I have done maximization with the combined use of the mathematical programming language AMPL (www.ampl.com) and the solver SNOPT (http://www.sbsi-sol-optimize.com/manuals/SNOPT%20Manual.pdf). With these tools, maximization is carried out in a few seconds. I wonder if that

Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

Hi R users, I google the website and I found that there are three ways to perform repeated measure ANOVA: aov, lme and lmer. http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg58502.html But the questions are which one is good to use and how to do post-hoc test? I use the example that is provided in the above link and I try > tt<-aov(p.pa~group*time+Error(subject/time),data=P.PA)

The following message is provided by Erik Please provide the reproducible code to do this. Generate a sample data set using the random data generating functions and show us what you'd like, we can then more easily help. ctu at bigred.unl.edu wrote: > Hi, > How about using "subset"? > x1<-tapply(subset(years, length(area)>20), function(x) length(unique(x))) >

Dear R-help, The documentation for systemfit shows that logLik() can be used to obtain loglikelihood values from linear systems estimated by systemfit(). It seems to me that logLik() cannot be used for nlsystemfit(). Does anyone know of any other packages that might let me obtain the loglikelihood of a model estimated with nlsystemfit()? Kind regards, Alex Olssen

Version 1.3.1 (2001-08-31) factorial() is not in R. It is in S-Plus with the definition factorial <- function(n) gamma(n + 1) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

Hi R Users, I was accidentally save R file when I quit R program. I try to delete but I am not sure the R saved file was successfuly delete or not. Now everyt time when I open the R, it always appears [R.app GUI 1.34 (5589) i386-apple-darwin9.8.0] [Workspace restored from /Users/ctu/.RData] I have read google, R help and Nabble but I still can't solve this problem. Could someone please

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

Hello, I am trying to understand a small quirk I came across in R. The following code results in an error: k <- c(2, 1, 1, 5, 5) f <- c(1, 1, 1, 3, 2) loglikelihood <- function(theta,k,f){ if( theta<1 && theta>0 ) return(-1*sum(log(choose(k,f))+f*log(theta)+(k-f)*log(1-theta))) return(NA) } nlm(loglikelihood ,0.5, k, f ) Running this code results in: Error

Hi R users, I have a couple of questions about some problems that I am facing with regard to numerical integration and optimization of likelihood functions. Let me provide a little background information: I am trying to do maximum likelihood estimation of an econometric model that I have developed recently. I estimate the parameters of the model using the monthly US unemployment rate series

Dear R-help gurus (and T.Yee, the VGAM maintainer) - I've been banging my head against the keyboard for too long now, hopefully someone can pick up on the errors of my ways... I am trying to use optim to fit a zero-inflated negative binomial distribution. No matter what I try I can't get optim to recognize my initial parameters. I think the problem is that dnbinom allows either