similar to: MLE: Question

Hi R-Help! My question: I have lifetime/failure data of machines with different stress levels and i think an weibull/extreme value distribution would fit this data. So I did: model1 <- survreg(Surv(lfailure)~stress,data=steel,dist="extreme") (where lfailure=log(failure)) Now I would like to do a likelihood ratio test to test the hypothesis H0: location parameters of the

Hi I am sure there is a function out there already but I couldn't find it. I have SNP data, that is, a matrix which contains in each row two characters (they are different in each row) and I would like to convert this matrix to a binary one according to the minor allele frequency. For non-geneticists: I want to have a binary matrix for which in each row the 0 stands for the less frequent

So, I've embarked on my threatened modifications to the mle subset of the stats4 package. Most of what I've done so far has *not* been adding the slick formula interface, but rather making it work properly and reasonably robustly with real mle problems -- especially ones involving reasonably complex fixed and default parameter sets. Some of what I've done breaks backward

Hello R users, before asking my question I'd like to stress that I'm an (absolute) beginner in using R, but enthused about the incredible possibilities of it. So I hope my questions are not too stupid. Here's my problem: I have a dataset with skewed distributions. In order to obtain approx multivariate normality by a Box-Cox-Transformation, I used the "Rfwdmv Package" to

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =

Hi I was wondering if there was a way to run system when there is e.g. a C program which is not totally command-line driven. That is, I have a C program which requires manual input of options into the shell during the run. I would like to run this program from R and as far as I have seen system() does not handle manual options (?). Is there thus a way to use these C programs anyway through R

Hi I have sent a previous email "Error in dyn.load()" for which, shame on me, I later found a partial answer. I have been trying to look into what I exactly need to include into Makevars and where it needs to be located and have not found a satisfying answer yet. Maybe the following questions are helpful for other people as well. Again, I am trying to include a C function tools.c into

Hi I would like to produce a red shading I figured the easiest way to do that would be to use rgb in the following way: a <- seq(0,0.9,by=0.1) redshade <- rgb(red=1,green=a, blue=a) However, I don't really know how to plot things using hexadecimal colors. I used a function which tries to find the closest color to the rgb shades but it didn't work very well. Any

Dear R users! I used lme to fit a mixed model with random intercept and spatial Gaussian correlation i.e. I fitted a model of the following form: Y = X*beta + error and error = U + W(t) + Z where U is the random intercept (normally distributed), W(t) the stationary Gaussian process and Z also a normally distributed (the residual) rv. Each of these three random variables have a variance which

Hi, I am aware that this question might be a basic one. I did browse the help and archives but I still haven't understood how to do the following: I run R locally and would like to read in data from a server for which there is a username and password. That is, how do I open a connection to a server with a password? Thanks, Hadassa -- Hadassa Brunschwig PhD Student Department of Statistics

Hi R-Help! I am trying to use R2WinBUGS but I get the following error message in WinBUGS (and there must be something wrong with my R statement as I tried it directly in WinBUGS and it worked): display(log) check(C:/Documents and Settings/Daikon/Roche/pop_model.txt) model is syntactically correct data(C:/Documents and Settings/Daikon/Roche/data.txt) expected key word structure compile(7) ...(and

Hi there, I ran the following code: vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption vols.csv" , header=TRUE, sep=",") X<-ts(vols[,2]) #X dcOU<-function(x,t,x0,theta,log=FALSE){ Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) }

I'm trying to figure out the best way of fitting the same negative log-likelihood function to more than one set of data, using mle() from the stats4 package. Here's what I would have thought would work: -------------- library(stats4) ## simulate values r = rnorm(1000,mean=2) ## very basic neg. log likelihood function mll <- function(mu,logsigma) {

I learned R & MLE in the last few days. It is great! I wrote up my explorations as http://www.mayin.org/ajayshah/KB/R/mle/mle.html I will be most happy if R gurus will look at this and comment on how it can be improved. I have a few specific questions: * Should one use optim() or should one use stats4::mle()? I felt that mle() wasn't adding much value compared with optim, and

So I am trying to use the mle command (from stats4 package) to estimate a number of parameters using data but it keeps throwing up this error message: Error in solve.default(oout$hessian) : Lapack routine dgesv: system is exactly singular: U[1,1] = 0 This error sometimes indicates that the list of starting values is too far from optimum but this is unlikely since I picked values close to where

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

Good Afternoon, I am relatively new to R and have been trying to figure out how to estimate regression coefficients using the MLE method. Some background: I am trying to examine scenarios in which certain estimators might be preferred to others, starting with MLE. I understand that MLE will (should) produce the same results as Ordinary Least Squares if the assumption of normality holds. That

Hello, I've the following setting: (1) Data from a source without truncation (x) (2) Data from an other source with left-truncation at threshold u (xu) I have to fit a model on these these two sources, thereby I assume that both are "drawn" from the same distribution (eg lognormal). In a MLE I would sum the densities and maximize. The R-Function could be:

Hi, I am looking for some help regarding the use of the mle function. I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that have been defined in the the log-likelihood equation as theta0=theta[1], theta1=theta[2] and theta2=theta[3]. My R code for mle is: mle(Poisson.lik, start=list(theta=c(20,1,1), method="Nelder-Mead", fixed=list(w=w, t1=t1, t2=t2)) But I keep