similar to: aov p values for levels of a factor.

Hi, i'm trying to simplify some R code but i got stucked in this: test<-data.frame(cbind(id,x1,x2,x3,x4,x5,x6,x7)) test > test id x1 x2 x3 x4 x5 x6 x7 1 1 36 26 21 32 31 27 31 2 2 45 21 46 50 22 36 29 3 3 49 47 35 44 33 31 46 4 4 42 32 38 28 39 45 32 5 5 29 42 39 48 25 35 34 6 6 39 31 30 37 46 43 44 7 7 41 40 25 23 42 40 24 8 8 27 29 47 34 26 38 28 9 9 25 35 29 36

Greetings, I am having trouble getting the function reformulate_ATSP_as_TSP to work for me. I have provided a simple example of some of the code I've been using. In particular, I'm not sure why I'm getting the error "Error in dimnames(tsp) <- list(lab, lab) : length of 'dimnames' [1] not equal to array extent" since I created the object ATSP with a valid

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

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

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

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

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

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

On Wed, Jan 11, 2017 at 8:13 PM, Mehdi Amini <mehdi.amini at apple.com> wrote: > > Can you elaborate why? I’m curious. > The con of proposal c was that many passes would need to learn about many region intrinsics. With tokens, you only need to teach all passes about tokens, which they should already know about because WinEH and other things use them. With tokens, we can add as

Dear R-ers, I am using getGEO to download expression data from the Gene Expression Omnibus. With default settings, when a file is downloaded and parsed, lots of dotted lines are printed in the terminal, like this: .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... ..........

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

Dear list members, I am looking for a goodness of test that will tell me if a sample is likely to have come from a standard normal distribution. I can find plenty of omnibus tests for normality in the nor.test package, but none of them appear to allow me to test against the specific alternative that the data are not standard normal. My back up option is to use a Kolmogorov-Smirnov test, but my

Dear R-experts, my goal is to visualize the following polynomial regression as a 3D-surface: Z = b0 + b1*X + b2*Y + b3*XY + b4*X^2 + b5*Y^2 I believe that a solution to this problem may be of interest to a wider range of scientists because the problem is a derivative of a more general problem, i.e.: how to describe the relationship between one dependent variable and the DIFFERENCE between two

Dear WizaRds, I would like to fit a curve to ten points with nls() for one unknown parameter gamma in the Kahnemann/ Tversky function, but somehow it won't work and I am unable to locate my mistake. p.kum <- seq(0.1,1, by=0.1) felt.prob.kum <- c(0.16, 0.23, 0.36, 0.49, 0.61, 0.71, 0.85, 0.89, 0.95, 1) ## how to find a function that fits these points nicely? plot(p.kum,

Hi all, Henrik Bengtsson has done some fantastic work with {future} and, more importantly, greatly improved constructing and deconstructing a parallelized environment within R. It was with great joy that I saw Henrik slowly split off some functionality of {future} into {parallelly} package. Reading over the package?s README, he states: > The functions and features added to this package are

> On Jan 12, 2017, at 5:02 PM, Hal Finkel <hfinkel at anl.gov> wrote: > On 01/12/2017 06:20 PM, Reid Kleckner via llvm-dev wrote: > >> On Wed, Jan 11, 2017 at 8:13 PM, Mehdi Amini <mehdi.amini at apple.com <mailto:mehdi.amini at apple.com>> wrote: >> Can you elaborate why? I’m curious. >> >> The con of proposal c was that many passes would need

Just a few minor changes released before I add gem support. * R() now accepts a Hash in its arguments which can be used to build a query string. R(Topic, @topic.id, :page => 24) #=> "/topic/14?page=24" * Base#to_a, discussed here.[1] Can be used to forward requests. Both the camping and camping-omnibus gems are fresh. gem install camping-omnibus --source

Hello, I am starting to use R for various analyses, for example I use the ca package to do Correspondence Analysis. I am also looking to use packages such as: pls Partial Least Squares plspm Partial Least Squares Path Modelling However, although I can use packages such as these on un-weighted data there does not appear to be a facility to take account of weighted data. I am a