similar to: Finite Mixture Models with logistic regression

Pursing my earlier question, when I tried loading Lindsey's gnlm, I got a message Loading required package: rmutil Warning message: There is no package called 'rmutil' in: library(package, character.only = TRUE, logical = TRUE, warn.conflicts = warn.conflicts, According to the R documentation http://finzi.psych.upenn.edu/R/doc/html/packages.html rmutil is in the standard

Hi, I just installed the odesolve package and ran the check command on it. It failed trying to execute library(gnlm). Sure enough, there is no gnlm library on my system and I could not find it on the CRAN archive either. Am I missing anything obvious or is gnlm some private library that somehow found its way into the example section of odesolve? Thanks in advance, Andy

Hi list, my previous question was obviously too basic to deserve an answer - apologies for that. I'm learning, things can only get better :-) My current problem is with fitting a generalized gamma distribution with an additional "shift" parameter, that represents a shift of the distribution along the X axis. The gnlr3 function (in Jim Lindsey's GNLM package) fits this

Dear List Members, I need a bit help about fitting some theoretical distributions (such as geometric, exponential, lognormal or weibull distribution) to the following *dry spell*, *wet spell*, *cycles (Wet-Dry or Dry-Wet)* from my meteorological (daily rainfall) data http://www.angelfire.com/ab5/get5/R.rainfall.txt only for rainy seasen (july - september) of 14 years only:

Hello I am interested in Poisson or (ideally) Negative Binomial regression with an inflated number of 1 responses I have seen JK Lindsey's fmr function in the gnlm library, which fits zero inflated Poisson (ZIP) or zero inflated negative binomial regression, but the help file states that for ' Poisson or related distributions the mixture involves the zero category'. I had thought

I have some data which I am trying to fit with a negative binomial distribution. I have found the glm.nb function from MASS. I have reason to believe that the mean parameter mu depends on certain factors, and that the shape parameter theta depends on others. If, say, the factors are P and Q, it might be that mu ~ P:Q and theta ~ P (where mu ~ P:Q means that mu is a function of the pair (P,Q))

Can someone show me how to specify zero inflated poisson and zero inflated negative poisson models in R? I would like to replicate the example given in Long (1997: Regression Models for Categorical and Limited Dependent Variables) in Chapter 8.5 (pp. 242-247). TIA Dirk ************************************************* Dr. Dirk Enzmann Criminological Research Institute of Lower Saxony

I want to generate 1,000 random samples of sample size=1,000 from ZINB. I know there is a rnegbin() to generate random samples from NB, and I know I can use the following process: do i=1 to 1000 n=0 do i=1 to 1000 if runi(1)>0.1 then x(i) = 0; else x(i)=rnegbin(); n=n+1; if n>1000 then stop; end; output; end; Anybody can help me out with the R code? Thanks very much ahead of time.

Hello R users, Doing My PhD I collected count data which I believe is zero inflated. I have run a statistical model with lmer and family=poisson and got summary(model)@sigma=1 so I believe there is no overdispertion. I would like to use the fmr function from the 'gnlm' library but I just cannot figure out from the examples in the help page and some forums out there how to convert the lmer

I have would like to use logistic regression to analyze the percentage of correct responses in a 2 alternative forced choice task. The question is whether one needs to take into account the fact expected probabilities for the percentage of correct responses ranges between 0.5 and 1 in this case and how to adjust the link function accordingly in R (see details below). Gabriel Subjects were asked

Dear R-group members, I use: platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 4.1 year 2002 month 01 day 30 language R I try to fit a 2 compartment model. The compartments are open, connected to each other and

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

I attempted to contact Drew Tyre, but the email I have for him is no longer in service. Hopefully someone can help. I'm using obs.error in R to model turtle occupancy in wetlands. I have 4 species and 20 possible patch and landscape variables, which I've been testing in smaller groups. > zib.out<-obs.error(y=painted,m=numvis,bp=zvars,pcovar=7) I get the following error

Dear all, I am interested in correctly testing effects of continuous environmental variables and ordered factors on bacterial abundance. Bacterial abundance is derived from counts and expressed as percentage. My problem is that the abundance data contain many zero values: Bacteria <-

My attempt to test a model using ordglm code is running into problems, and I thought if you have a moment you might illucidate the situation. Here is the data: http://www.people.fas.harvard.edu/~corr/6.4.05.RData Here is the code: # I coerce tcn8 matrix data to a vector, because ordglm will not accept matrix data. y<-as.vector(tcn8[,62]) x<-as.vector(tcn8[,60])

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

Hey, Dose R include some package for Gaussian Mixture Model data generation and parameters estimation? Now I want to assign lots of multivariate data into a GMM model. So just wondering if given sample data, can we use some functions to estimate the compoents' density function. Thanks for your support. Fred -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Dear R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear All, This is a question somewhat off-topic. Say, if I have known the number of components in the mixture, all the estimated parameters, prior probabilities, and so on for a finite mixture model, how might I compute the posterior probabilities of each case for a new dataset without observed response (Y)? I want to know the parametric form of such calculation such that I can calculate it