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Dear Mr. or Ms.,   I used the R-software to run the zero-inflatoin negative binomial model (zeroinfl()) .   Firstly, I introduced one dummy variable to the model as an independent variable, and I got the estimators of parameters. But the results are not satisfied to me. So I introduced three dummy variables to the model. but I could not get the results. And the error message is

Hello all, I am Xiongqing Zhang, come from Beijing of China. I know you from the web site: http://finzi.psych.upenn.edu/Rhelp08/2008-February/154627.html. I am not very clear about the R-project software. But I want to estimate the parameters and errors of zero-inflation poisson model. Can?you help me? Data is in the attachement. Thank you. I will be very appreciated if you can help me. Best

Dear Mr. or Ms.,   I would like to use the R-software to run the zero inflated mixed model and hurdle mixed model. But I do not know how to do? Would you please tell me the code and data format?   I will be very appreciated if you can help me. Thank you very much.   Best regards,   Sincerely, Xiongqing Zhang [[alternative HTML version deleted]]

Hi, I try to use the visual studio 2008(VC.net) to call R code, but there is en error:  Exception from HRESULT: 0x80040013 How can I fix the problem? Thanks.   My PC systern is XP, R version is "R-2.14.1" and "R_Scilab_DCOM3.0-1B5.exe".   best regards, Xiongqing [[alternative HTML version deleted]]

Dear all, I am looking for an R package that fits multivariate gaussian or non-gaussian longitudinal outcomes. I am especially interested to non-gaussian outcomes since the outcomes I've got are discrete (some are binomial and some are count data). Many thanks in advance, Abderrahim [[alternative HTML version deleted]]

Hi, I'm a very new user of R and I hope not to be too "basic" (I tried to find the answer to my questions by other ways but I was not able to). I have 12 response variables (species growth rates) and two environmental factors that I want to test to find out a possible relation. The sample size is quite small: (7<n<12, depending on each species-case). I performed a

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

SuppDists is orphaned on CRAN (and has been since 2013). https://cran.r-project.org/web/checks/check_results_.html Oddly, the simulate method for the inverse.gaussian family [inverse.gaussian()$simulate] depends (in a loose sense) on SuppDists (it fails if the SuppDists namespace is not available: if (!requireNamespace("SuppDists", quietly = TRUE)) stop("need CRAN

On 2019-09-25 3:26 a.m., Martin Maechler wrote: >>>>>> Ben Bolker >>>>>> on Tue, 24 Sep 2019 20:09:55 -0400 writes: > > > SuppDists is orphaned on CRAN (and has been since 2013). > > https://cran.r-project.org/web/checks/check_results_.html > > > Oddly, the simulate method for the inverse.gaussian family > >

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

Dear R users, I want to explain binomial data by a serie of fixed effects. My problem is that my binomial data are spatially correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 ,

Ah, I spoke too soon. I started putting the demo code into a test suite and ran one check with valgrind and ? sure enough ? there's def more issues (a cpl functions) than the overt/easy ones (and, I went back to the check results page and, also sure enough, they're there, too). They look to be fairly straightforward to resolve but it's going to take a bit longer than "this

R 2.2.0 windows XP I am beginning to explore the mitools package contributed by Thomas Lumley (thank you Thomas) and I have a few questions: (1) In the examples given in the mitools documentation, the only family argument used is family=binomial. Does the package support family=gaussian and other link functions? I ran the with function with family=gaussian and I obtained results, but I am not

Dear all, Is there any function for calculating confidence limits for coefficients in an lm() object? I know of the confint() function in the MASS library working very well on my binomial GLMs and I have tried it (using glm () , family=gaussian) but it gives NAs according to below. Does the confint() function not accept gaussian GLMs? Could there be convergence problems in the GLM? Note the

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

I'm wondering whether anyone has any insight into why the 'simulate' methods for the built-in glm() families (binomial, Poisson, Gamma ...) extract the prior weights using object$prior.weights rather than weights(object,"prior") ? At first I thought this was so that things work correctly when e.g. subset= and na.action=na.exclude are used. However, the current versions of

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Hello, I use a type III anova ("car" package) to analyse an unbalanced data design. I have two factors and I would have the effect of the interaction. I read that the result could be strongly influenced by the contrasts. I am really not an expert and I am not sure to understand indeed about what it is... Consequently, I failed to properly used the fit.contrast function (gregmisc

Hello! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just