similar to: maximum likelihood, 1st and 2nd derivative

Hi Guys, it would be great if you could help me with a MLE problem in R. I am trying to evaluate the maximum likelihood estimates of theta = (a1, b1, a2, b2, P) which defines a mixture of a Poisson distribution and two gamma prior distributions (where the Poisson means have a gamma distribution, actually 2 gammas and P is the mixing factor). The likelihood function for theta is L(theta) = Pi,j{P

Hi all, Thanks to all who offered advice on using F95 in R. Now I'm trying to compile a test package using gfortran, Linux 2.4.21 and R 2.1.0. I was able to successfully compile and use a test F95 routine by setting my environment variables as follows in bash: export PATH=~/bin/:$PATH export F77=gfortran export LD_LIBRARY_PATH=~/bin/irun/lib export GFORTRAN_STDIN_UNIT=-1 Now I'm

Suppose I have a mixed-effects model where yij is the jth sample for the ith subject: yij= beta0 + beta1(age) + beta2(age^2) + beta3(age^3) + beta4(IQ) + beta5(IQ^2) + beta6(age*IQ) + beta7(age^2*IQ) + beta8(age^3 *IQ) +random intercepti + eij In R how can I get an F test against the null hypothesis of beta6=beta7=beta8=0? In SAS I can run something like contrast age*IQ 1,

Hi all, I have a relative large amount (several thousand rows, but a small amount of unique objects) of data in a format like this: 1 text_string 1 text_string 1 text_string 2 text_string 2 text_string 3 text_string 3 text_string 3 text_string 3 text_string 3 text_string . . . n text_string I want to create an n x p matrix, n objects (=40) and p unique text strings. Nij is number of occurrences

Hi, I would really appreciate if I could get some help here. I'm using nlm to minimize my negative log likelihood function. What I did is as follows: My log likelihood function (it returns negative log likelihood) with 'gradient' attribute defined inside as follows: # ==========Method definition====================== logLikFunc3 <- function(sigma, object, totalTime) { y <-

Hi, 2 questions: Question 1: example of what I currently do: for(i in 1:6){sink("temp.txt",append=TRUE) dput(i+0) sink()} x=scan(file="temp.txt") print(prod(x)) file.remove("C:/R-2.5.0/temp.txt") But how to convert the output of the loop to a vector that I can manipulate (by prod or sum etc), without having to write and append to a file? Question 2: >

Hello, I have a rather complex problem... I will have to explain everything in detail because I cannot solve it by myself...i just ran out of ideas. So here is what I want to do: I take quotes of two indices - S&P500 and DJ. And my first aim is to estimate coefficients of the DCC-GARCH model for them. This is how I do it: library(tseries) p1 = get.hist.quote(instrument =

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

Hi, this is probably quite simple but I can't seem to do it correctly. I have a data frame of counts of infections in different ages; something like: count=c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 7, 8, 8, 9, 9, 10, 11, 15, 17, 17, 17, 17, 19, 19, 19, 19, 20, 20, 20, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 23, 24, 24, 25, 27, 31, 33 ) age=c(3, 8, 9, 7, 56, 58, 10, 13, 53, 55, 11,

Dear helpers, I've looked in the help files (maybe not very thoroughly but I looked) and I couldn't find a straightforward way to permanently set the working directory (where I save and read files). Each time I startup R the working dir is automatically "C:/R/bin" [using:getwd()], and I know I can change it using setwd [for example setwd("C:/R/DATA"), but it's only

I need to find a computationally simple process for the movement of interest rates. In this simplified model, an interest rate can have 3--5 possible values, and its movement is characterized by a matrix of transition probabilities (ie, it is a Markov process). I would like to estimate this process from a given set of data. For example, let the interest rate time series be: 7 3 8 2 5 9 6

>cbn<-as.matrix(expand.grid( rep( list(0:1), 50))) Error in rep.int(rep.int(seq_len(nx), rep.int(rep.fac, nx)), orep) : invalid 'times' value In addition: Warning message: In rep.int(rep.int(seq_len(nx), rep.int(rep.fac, nx)), orep) : NAs introduced by coercion But I'm only interested in cbn matrix rows where: cbn<- cbn[rowSums(cbn)==5,] Is there a way to evaluate it

This below is not solvable with uniroot to find "a": fn=function(a){ b=(0.7/a)-a (1/(a+b+1))-0.0025 } uniroot(fn,c(-500,500)) gives "Error in uniroot(fn, c(-500, 500)) : f() values at end points not of opposite sign" I read R-help posts and someone wrote a function: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/92407.html but it is not very precise. Is there any

Hello I am using the "for (i...)" and a sink() into a file. But the output I am having is not arranged in either a vector or any other good structure. I would like to have the output in a file directly as a vector so that I do not have to edit the [1] and [6] etc and that the values are comma separated. Is there a way? Thanks. Example data: x=read.table(file="pw.power.txt")

This may be a question to R-development but I'm not sure. Symbolic differentiation is implemented in R (maybe not for extremely complex expressions), but it proves that it can be done. I know that in C++ it can be done (symbolic c++), do you think in R it can be programmed just using the R language without resorting to external sources? Does anyone know or can estimate the amount of resources

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

What happens to old R packages? Is there a place where they are stored like an archive? I'm looking for a package called ScanoR that doesn't exist anymore on CRAN, the author doesn't exist anymore... I had made copies of all packages (fearing that some might be "taken off the air" but unfortuantely the ScanoR package was removed before I started making copies in 2007). Any

> x <- runif(100) > y <- runif(100) > ks.test(x,y) Two-sample Kolmogorov-Smirnov test data: x and y D = 0.11, p-value = 0.5806 alternative hypothesis: two-sided ok I expected that, but: > ks.test(runif(100), "runif") One-sample Kolmogorov-Smirnov test data: runif(100) D = 0.9106, p-value < 2.2e-16 alternative hypothesis: two-sided How