similar to: [Fwd: RE: maximum likelihood problem]

I am trying to figure out how to run maximum likelihood in R. Here is my situation: I have the following equation: equation<-(1/LR-(exp(-k*T)*LM)*(1-exp(-k))) LR, T, and LM are vectors of data. I want to R to change the value of k to maximize the value of equation. My attempts at optim and optimize have been unsuccessful. Are these the recommended functions that I should use to maximize

I calculated a large vector. Unfortunately, I have some measurement error in my data and some of the values in the vector are erroneous. I ended up wih some Infs and NaNs in the vector. I would like to filter out the Inf and NaN values and only keep the values in my vector that range from 1 to 20. Is there a way to filter out Infs and NaNs in R and end up with a clean vector? Mike

I have R randomly sampling my array made up of 2 columns of data. Here is my code randomly sampling 5 different rows from my dataset to create a new dataset of 8 rows of data: testdat<-growth[sample(5,8,replace=T),] Now I want to tell R to repeat this function 50 times and give me the output. I have been searching the internet and have been unable to figure this out. Any advice

I am trying to get R to randomly select values from my dataset (i.e. bootstrapping) with replacement. However, my attempts at this have been unsuccessful. Here is a basic example of what I am doing: I have a data vector of 8 values (i.e. data= 2,5,9,4,5,6,7,8). I used the sample function and it worked. However, it only repeated my values in the exact same order as the dataset. It did not

It turns out I didn't have to filter out the 1-20 values. The code of x[is.finite(x)] did the trick. Thanks!!! Mike ---------------------------- Original Message ---------------------------- Subject: Re: [R] cleaning up a vector From: "Henrique Dallazuanna" <wwwhsd at gmail.com> Date: Fri, October 1, 2010 1:55 pm To: mlarkin at rsmas.miami.edu Cc: r-help

I am trying to get R to resample my dataset of two columns of age and length data for fish. I got it to work, but it is not resampling every replicate. Instead, it resamples my data once and then repeated it 5 times. Here is my dataset of 9 fish samples with an age and length for each one: Age Length 2 200 5 450 6 600 7 702 8 798 5 453 4 399 1 120 2 202 Here is my code which resamples my

I am trying to get R to pick random integers from my dataset (i.e. bootstrapping) with replacement. However, my attempts at this have been unsuccessful. Here is a basic example of what I am doing: I have a data vector of 8 integers (data= 2,5,9,4,5,6,7,8). I used the sample function and it worked but it only repeated my values in the exact same order. It did not randomly sample them. Here

Thanks to the people on this list I was able to fix my code for randomly sampling. Thanks. Now, I am moving on to the next step and I ran into another snag. I have a large dataset but I am starting with a small made-up dataset until I figure it out. I have two columns of data (age and length). I got R to read my data called growth which is the age and length for 10 fish: >

Hello, I am trying to plot confidence bands on the mean and prediction bands for the following nonlinear regression, using maximum likelihood via optim. A toy example with data and code of what I am trying to accomplish is: VOL<-c(0.01591475, 1.19147935 ,6.34102460, 53.68809287, 91.90143074, 116.21397007, 146.41843056, 215.64535337, 256.53149673, 315.73609232) Age <-c(1.622222, 2.833333

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

In trying to streamline various optimization functions, I would like to have a scratch pad of working data that is shared across a number of functions. These can be called from different levels within some wrapper functions for maximum likelihood and other such computations. I'm sure there are other applications that could benefit from this. Below are two approaches. One uses the <<-

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1

The University of Miami's Rosenstiel School of Marine and Atmospheric Sciences (RSMAS) is seeking a research associate to work with hydrographic data. The objective of the project is to develop statistical models for estimating salinity and density from observations of temperature together with knowledge of location and time. Because the relationship between salinity and temperature can be

The University of Miami's Rosenstiel School of Marine and Atmospheric Sciences (RSMAS) is seeking a research associate to work with hydrographic data. The objective of the project is to develop statistical models for estimating salinity and density from observations of temperature together with knowledge of location and time. Because the relationship between salinity and temperature can be

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

Dear R users, I am trying to figure out the control parameter in "optim," especially, "fnscale" and "parscale." In the R docu., ------------------------------------------------------ fnscale An overall scaling to be applied to the value of fn and gr during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on

Hello, i know that i have to use optim, but i'm confused how its possible maximize the sum over all l[i] and get the optimized max(LL), r and alpha? LL <- function(trans,time){ for(i in 1:length(trans){ l[i] <- log(lgamma(r+trans[i] - gamma(r+1)*(alpha/alpha+t[i]))**r)*(t[i]/alpha+t[i]))**trans[i] } return(sum(l)) } i'm confused how i have to set r and alpha and i

Hello All, I am trying to estimate the parameters of a stochastic differential equation (SDE) using quasi-maximum likelihood methods but I am having trouble with the 'optim' function that I am using to optimise the log-likelihood function. After simulating the SDE I generated samples of the simulated data of varying size (I want to see what effect adding more observations has on the

Hi all, I need to check for a difference between treatment groups in the parameter of the geometric distribution, but with a cut-off (i.e. right censored). In my experiment I stimulated animals to see whether I got a response, and stopped stimulating if the animal responded OR if I had stimulated 10 times. Since the response could only be to a stimulation, the distribution of response times

Hello, I've just a little problem using the function optim. Here is the function I want to optimize : test_function(x){(exp(-0.06751 + 0.25473*((x[1]-350)/150) + 0.04455*((x[2]-40)/20) + 0.09399*((x[3]-400)/100) - 0.17238*((x[4]-250)/50)- 0.45984*((x[5]-550)/150)-0.39508*((x[1]-350)/150)* ((x[1]-350)/150) - 0.05116*((x[2]-40)/20)* ((x[2]-40)/20) - 0.27735*((x[3]-400)/100)*((x[3]-400)/100) -