similar to: omnibus LR in multinomial model

At the end of the 'Details:' section of the help on Anova (car package), it states: The standard R `anova' function calculates sequential (type-I) tests. These rarely test meaningful hypotheses. So I thought I'd try it. However, I was perplexed to get this message: Browse[1]> Anova.glm(leaf.glm1, type ="II") Error in terms.formula(formula, data = data) :

R 2.9.1 Windows XP I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model:

I''ve noticed that the copy of Mongrel installed by the camping-omnibus gem doesn''t work with Ruby 1.8.6. Or to be more specific, cgi_multipart_eof_fix (which Mongrel is dependent upon) doesn''t work: > $ sudo gem install mongrel --source http://code.whytheluckystiff.net > > ERROR: Error installing mongrel: > cgi_multipart_eof_fix requires Ruby version

gem install camping-omnibus --source code.whytheluckystiff.net Considering the number of optional dependencies now in Camping, I''ve put together an eigengem which will load all of the recommended libraries that most devoted campers will want: ActiveRecord, Mongrel, SQLite3, RedCloth and the acts_as_versioned gem. (Those last two are used in the examples.) Stuff like mosquito and

I am looking for an R package with which one can calculate an effect size for a set of contrasts given an omnibus chi-square test statistic (more than 1 degree of freedom). Is there such a package? Presumably, it would implement the procedure (or something like it) described by Roznow and Rosenthal 1996 Psychological Methods 1: 331-340. many thanks! Steven Orzack Fresh Pond Research Institute

Hi, Is there an implementation of the D'agostino K2 omnibus test in an R package. I've found the D test in the fBasics package, but this doesn't give the same answer as my algorithm. I've already coded a version of the K2 and need to check the results. I have a set of spectra with gaussian distributed noise and I want a statistical test to separate those with no signal (so the

This is very possibly not a question on R. I was under the impression that the argument that gives rise to Fisher's LSD method in ANOVA works in other situations with three-way comparisons too, given that formal logic works the same ("if the omnibus test rejects, only two of the three groups may be equal, and therefore only one hypothesis can be rejected falsely"). However, when I

Hello, I've been running some HLMs using the lme function quite happily; it does what I want and I'm pretty sure I understand it. The issue is that I'm currently trying to estimate a model with a 14-level "nusiance" factor as an independent variable...which makes the output quite ugly. All I'm really interested in is the question of whether these factor as a whole

Daniel, When Group is entered as a factor, and the factor has two levels, the ANOVA table gives a p value for each level of the factor. What I am looking for is the omnibus p value for the factor, i.e. the test that the factor (with all its levels) improves the prediction of the outcome. You are correct that normally one could rely on the fact that the model

Dear All, I am new to ordinal logistic regression. Using ordinal regression within the R Commander GUI, I have generated an independent variable that is significant, but whose 95% confidence intervals slightly crosses "1". Is this possible? Here is the syntax and output: polr(formula = CDIcat ~ Employment, data = CDIallvariables, Hess = TRUE, method = "logistic")

hello vorlon, got notified of your patch, will apply next days upstream unless some critiques are voiced on ml. thanks. -- maks ----- Forwarded message from Steve Langasek <steve.langasek at canonical.com> ----- Date: Wed, 16 Feb 2011 22:05:42 -0000 From: Steve Langasek <steve.langasek at canonical.com> Subject: [Bug 527720] Re: thumb2 porting issues identified: klibc uses

Hi, P-values in Durbin-Watson test obtained through the use of functions available in packages "lmtest" and "car" are different. The difference is quite significant. function "dwtest" in "lmtest" is much faster than "burbinwatson" in "car". Actually, you can take a nap while the latter trying to calculated Durbin-Watson test. My question

Is there any way in R to do a linear regression with positive coefficients only? Thanks for any help! Sandor Lehoczky -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

I have Q3 sales for consecutive years 2002-2007 that I'm using to predict buying (yes/no) in Q3 in 2008. My data are arranged in counting process format where each customer has 6 rows of data, one for each year. However, there is no variability within the strata at the customer level: if someone bought in 2008, then all 6 records will be flagged as buy=1. I don't think this will work

when i try to validate my logistic regression model: fit<-glm(y~x,binomial,data=dataname,x=TRUE,y=TRUE) validate(fit,method="boot",B=150,...) i get the following error message: Error in UseMethod("validate") : no applicable method for "validate" any insight would be appreciated. many thanks! -- View this message in context:

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

I have numerous replicated goodness-of-fit experiments (observed compared to expected counts in categories) and these replicates are nested within a factor. The expected counts in each cell are external (from a scientific model being tested). The calculations I need within each level of the nesting factor are a heterogeneity G test, with the total G and the pooled G across replicates. Then I

We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate

During context switch is_running is set for the next vcpu before the gic state is actually saved. This leads to possible nasty races when interrupts need to be injected after is_running is set to the next vcpu but before the currently running gic state has been saved from the previous vcpu. Use current instead of is_running to check which one is the currently running vcpu: set_current is called

Dear all, I don't get how llvm handles functions with __attribute__((noreturn)). It seems that LR register is backed up on the stack whilst it will never be used to return from a 'noreturn' function. I have this problem with a home-made backend but it seems that ARM flavour of clang has same behaviour. By the way, SP is also saved, I don't understand why. Is there a syntax error