similar to: When to use quasipoisson instead of poisson family

Dear all, I've discovered the possibility to do the Nemenyi-Damico-Wolfe-Dunn test in the library(coin); oneway_test() With the given example I am unfortunately not able to reproduce the test. What does trafo and contrMat mean? I have a dataframe with 176 elements in 7 classes. It may be a problem, that my model isn't balanced? In Class1 I happen to have 4 elements, while there are 90 in

Dear fellow R users, Keywords: Kruskal-Wallis, Post-Hoc, pair-wise comparisons, Nemenyi-Damico-Wolfe-Dunn test, coin package, oneway_test I am using the "oneway_test" function in the R package "coin" and I am obtaining results which I cannot believe are accurate. I do not wish to waste anyone's time and so if the following problem is rather trivial, I apologize, however I

Is it possible to implement the Jonckheere-Terpstra test for ordered alternatives using the coin package: Conditional Inference Procedures in a Permutation Test Framework? I found jonckheere.test{clinfun}, but it uses a normal approximation when ties are present in the data. To make this concrete, I've include a small dataset. Thanks. --Dale Hollander and Wolfe, 1999 Table 6.6, pg 205

List, Would someone be so kind as to point me to a function that will calculate simultaneous confidence intervals of regression coefficients based upon their distribution as (under the assumption of normal errors, with \mathbf{X} as the design matrix): $\hat{\mathbf{\beta}} \sim N(\mathbf{\beta}, \sigma^2(\mathbf{X}^T\mathbf{X})^{-1})$. 'confint' calculates individual coefficients so

Hi, I'm trying to use expression() to write a text to a graphic in the margin. Using: mtext(expression(beta),side=1,line=2) writes a perfect beta greek character, but I need to add a subindex "max", and I'm trying: mtext(paste(expression(beta),"max"),side=1,line=2) simply writes "beta max" in the plot. Please, Could you tell me what I'm doing wrong?

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

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

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

Using anova of a glm with test = "Chisq", I get this: Analysis of Deviance Table Model: poisson, link: log Response: Days Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL 373 370.56 Block 3 71.05 370 299.51 2.543e-15 Variety 1 94.04 369

Greetings. I've written a convenience function for multicollinearity diagnosis. I'd like to report to the user the formula that is used in a regression. I get output like this: > mcDiagnose(m1) [1] "The following auxiliary models are being estimated and returned in a list:" [1] "`x1` ~ ." formula(fmla)() [1] "`x2` ~ ." I'd like to fill in the period

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

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

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

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

Hello I am currently working on forecasting hourly electricity prices where I am required to do wavelet transformation. First I need to calculate decomposition coefficients of the wavelet transform, then I need to select wavelet function using multiresolution technique. I would be really grateful if I can find any help in this matter. Thanking You with regards, -- Name : Tarun Trivedi Senior

Hello I am currently working on forecasting hourly electricity prices where I am required to do wavelet transformation. First I need to calculate decomposition coefficients of the wavelet transform, then I need to select wavelet function using multiresolution technique. I would be really grateful if I can find any help in this matter. Thanking You with regards, -- Name : Tarun Trivedi Senior

I'm trying to select a subset of a dataframe while dropping some factors. While the dataset gets smaller all Factor levels remain and I need to get rid of them. Strangely enough, I am almost certain that the same code on the same data worked OK earlier today - and it is not the first time that I'm not able to replicate earlier results with this command (I know, I might just be going

Dear R users, I am trying to apply the analysis processed in a paper, on the data I'm working with. The data is: 80 patients for which I have survival data (time - days, and event - binary), and microarray expression data for 200 genes (predictor continuous variables). My data matrix "data.test" has ncol: 202 and nrow: 80. What I want to do is: - run recursive partitioning on

Dear Researchers, Sorry for this email but I am not a statistician, and for this I have this problem to understand. Thanks in Advance for help and suggestions. Gianni I have 21 classes (00, 01, 02, 04, ....,020) with different length. I did a kruskal wall test in R with the following code kruskal.test(m.class.l, m.class.length.lf) Kruskal-Wallis rank sum test data: m.class.l and