similar to: Underdispersion with anova testing methods

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicates at a time)=

Greetings I'm attempting to insert missing data on the smallest size class of cave cricket instars into a data frame. The data involve censusing photoplots (plots) of roosting cave crickets in which we discern in four instars or size classes. I need to insert data on size class one into a data frame that already has data on size classes two through four. The data can be merged by their

Hi, I have been trying to do a simple GLM with count data using a poisson distribution. The results show evidence of underdispersion. I have only ever encountered overdispersion. Am I still able to use family=quasipoisson to correct for underdispersion? Thank you, Karla -- View this message in context:

Hello, Anyone who knows a fast and accurate algorithm for generating draws from an underdispersed Poisson distribution. Or even better, if there is a package containing such an implementation. Thanks Rene

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which

Dear R users I would like to simulate underdispersed Poisson and binomial distributions somehow. I know you can do this for overdispersed counterparts - using rnbinom() for Poisson and rbetabinom() for binomial. Could anyone share functions to do this? Or please share some tips for modifying existing functions to achieve this. Thank you very much for your help and time Shinichi

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit

We are pleased to announce the following book: Title: Beginner's Guide to Zero-Inflated Models with R Authors: Zuur, Ieno Book website: http://www.highstat.com/BGZIM.htm Paperback or EBook can be order (exclusively) from: http://www.highstat.com/bookorder.htm TOC: http://www.highstat.com/BGS/ZIM/pdfs/TOCOnly.pdf Keywords: 430 pages. Zero inflated count data. Zero inflated continuous data.

Hello all, I'm trying to use zoo.read but can't figure out how to deal with the time format. (example below) would be nice if someone could help. best regards, Immanuel --------------------------- L <- "Date,Time,Open,High,Low,Close,Up,Down 05.02.2001,00:30,421.20,421.20,421.20,421.20,11,0 05.02.2001,01:30,421.20,421.40,421.20,421.40,7,0

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

I have data consisting of binary responses from a large number of subjects on seven similar items. I have been using lmer with (crossed) random effects for subject and item. These effects are almost always (in the case of subject, always) significant additions to the model, testing this with anova. Including them also increases the Somers' Dxy value substantially. Even without those

The documentation states : An alternative parametrization (often used in ecology) is by the mean ?mu?, and ?size?, the dispersion parameter. However, this fails : > rnbinom(10, mu = 100, size = 0) [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Warning message: In rnbinom(10, mu = 100, size = 0) : NAs produced For dispersion set to 0, it should work like drawing from a Poisson distribution.

Hi all, Hope someone could help me. I am trying to run automatically the conversion of an Rwn file to a tex file. I am using windows 7, and cygwin. I tried to run automatically the Sweave.sh script, in its the most recent version available at R webpage: http://cran.r-project.org/contrib/extra/scripts/Sweave.sh Unfortunately, I got this error message: =========================== Raquel at

Greetings all, I am using lmer (lme4 package) to analyze data where the response is a proportion (0 to 1). It appears to work, but I am wondering if the analysis is treating the response appropriately - i.e. can lmer do this? I have used both family=binomial and quasibinomial - is one more appropriate when the response is a proportion? The coefficient estimates are identical, but the standard

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not

v2: - Change the OCaml code in the daemon to use PCRE instead of Str. - Call pcre_compile2 so we can capture the error code on failure. - Extend the test suite. - Some other cleanups, but very minor. Rich.

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend