similar to: Goodness of fit for MLE?

Hi I have been using a Zero-Inflated negative binomial model fitted using the pscl zeroinfl command but I would like to extract a goodness of fit measure are there any suitable pseudo R^2 measures available for this type of analysis to try and assess the amount of variation in the data explained by the model? I have tried with the pR2 command in pscl (for computing various pseudo R2

Dear all, I am facing a problem that seems to me more tricky now that it did at first sight. I have a collection of data which consist in frequency distributions: 6 patches had been proposed to female insects (for oviposition), 3 of them corresponding to one treatment (A), the other 3 to another treatment (B). The results are the number of patches that have been chosen for oviposition by

Hi Can anybody please suggest me about the documentation of "factanal.fit.mle()" (Not factanal()------ searching factanal.fit.mle() in R always leads to factanal()). Is there any function for doing principal component factor analysis in R. Regards Souvik Bandyopadhyay JRF, Dept Of Statistics Calcutta University [[alternative HTML version deleted]]

Hello, I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic from the returned from the "resid(fit,

Dear R users, I am a master student in Mathematics and I am writing my thesis in statistics. I need to use R and unfortunately I do not have any experience with a computer program. Could you please help me about chi squared goodness of fit test with R? In R-help website I saw a message about how to do that but I do not know how to cut the data into bins and calculate the expected numbers in each

>Y [,1] [,2] [,3] [1,] 0 1 0 [2,] 0 1 0 [3,] 0 0 1 [4,] 1 0 0 [5,] 0 0 1 [6,] 0 0 1 [7,] 1 0 0 [8,] 1 0 0 [9,] 0 0 1 [10,] 1 0 0 >X pri82 pan82 1 0 0 2 0 0 3 1 0 4 1 0 5 0 1 6 0 0 7 1 0 8 1 0 9 0 0 10

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

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

Hi R-Helpers , am working on nnet package.Multinom() has an option for finding the goodness of fit by giving the AIC value. Does nnet also gives some value to determine the accuracy. If not, can you guide me with some procedure to figure out the accuracy/goodness of fit of nnet model? Thanks in advance. -- View this message in context:

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all, I am reading the MASS book but it doesn't give examples about the diagnosis and model checking for rlm... My data is highly non-Gaussian so I am using rlm instead of lm. My questions are: 0. Are goodness-of-fit and model-checking using rlm completely the same as usual regression? 1. Please give me some pointers about how to do goodness-of-fit and residual diagnosis for

Hi, I'm using the power.law.fit function from the igraph package to fit a power law distribution to some data. This function returns the power law exponent as it's only result. I would like to have some sort of goodness-of-fit and/or error estimate of the exponent returned. This paper: http://www.edpsciences.org/articles/epjb/pdf/2004/18/b04111.pdf suggests using the

Say I have a formula Y ~ 1 + X, where X is a categorical variable. A previous thread showed how to evaluate this model using the mle package from "stats4" (see below). But, the user had to create the data matrix, X, including the column of one's for the regression constant. Is there a way to nest the linear formula in the code below, so the data matrix doesn't explicitly

Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works? Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335) analyzed some simple clinical trials data using a logistic random effects model. Several packages and methods MIXOR, SAS NLMIXED were employed. They reported obtaining very different parameter estimates and P

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

Hi there, I am trying to write a function to perform GOF test of data to a zero-truncated Poisson distribution. I am facing 2 problems. 1) How can I obtain a frequency table for all values within the range of observed values? For instance if the observations are obs <- c("A", "A", "A", "A", "B", "C", "C", "D",

Hi, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l<- function(m1,m2,b) -sum( d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) ) here d and t are given, "sum" means sum over these two vairables. the parameters

Hi there, I'm trying to solve a ML problem where the likelihood function is a function of two numerical procedures and I'm having some problems figuring out how to do this. The log-likelihood function is of the form L(c,psi) = 1/T sum [log (f(c, psi)) - log(g(c,psi))], where c is a 2xT matrix of data and psi is the parameter vector. f(c, psi) is the transition density which can be

Hello list: I generate by simulation (using different procedures) two sample vectors of size N, each corresponding to a discrete variable and I want to text if these samples can be considered as having the same probability distribution (which is unknown). What is the best test for that? I've read that Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous data