similar to: Omnibus test for main effects in the face of an interaction containing the main effects.

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

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Hello, I apologize for the post as I am certainly overlooking a simple solution to my difficulties with getting a legend to print on a survplot from the rms package. I am plotting the following: survplot(survest(fita), n.risk=T, conf='none', cex.n.risk=.85, dots=T, col='gray10', lty=2) survplot(survest(fit), n.risk=F, conf='none', add=T) survplot(survest(fitb), n.risk=F,

I've been attempting to calculate the predictions from a poisson glm object, along these lines: predict(foo.glm, type = "response") and predict(foo.glm, type = "response", se.fit = TRUE) foo.glm is arrived at this way: foo.glm <- glm(Insects ~ Dad * Mum + Location, offset = log(MM), family = "poisson", data = model.df) There are two

Windows XP R2.8.1 I am running an balanced aov, in which Time has 4 levels, Group has 2 levels: fit2<-aov(Post ~ as.factor(Time) +as.factor(Group)+as.factor(Group*Time)+Error(SS), alldata) I would like to get a p value for each level of time, each level of Group and each level of Group*Time. How can I do this? I get this, which appears to be omnibus p values for the factors: Error: SS

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

Greetings, I am working on a logistic regression model in R and I am struggling with the code, as it is a relatively new program for me. In searching Google for 'logistic regression diagnostics' I came Elizabeth Brown's Lecture 14 from her Winter 2004 Biostatistics 515 course (http://courses.washington.edu/b515/l14.pdf) . I found most of the code to be very helpful, but I am

I am trying to duplicate the example by Spencer Graves in the wiki, using lmer with the Nozzle data. http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests However the Chisq value and the fitAB values that are calculated are different compared to those in the example. I also get a warning message when I attempt the fitAB. Does anyone have any guidance as to why this might happen and how

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

Hello, I have a simple question about using the aov function syntax (ie. * + or :) for the interaction of 2 factors. I have read the help files, and researched other sites, and have included my source files. My goal is to measure the signifigance of the interaction between population and condition (aka. population:condition). I can't seem to figure it out. 1. In the first example the

Hi all, I'm just a beginner with R but I have not been able to search for any relevant answer to my problem. I apologize if it has in fact been asked before. Recently I've realized that I need to combine hundreds of pairs of data frames. The filenames of the frames I need to combine have unique strings. This is my best guess as to the approach to take: filenames<-list.files()

All, For example, I have a dataset named "ABC" loaded into R > ABC [,1] [,2] [,3] [1,] 1 4 7 [2,] 2 5 8 [3,] 3 6 9 and I also have a variable datasetname > datasetname [1] "ABC" and I want to add this "ABC" dataset to an existing list "alldata" > alldata <-NULL > alldata <- as.list(alldata) since I will

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

Hello, I have a question regarding how to subset/select parts of a data frame (matrix) in order to plot data associated only with this subset. Specifically I have a large data frame in which one column contains ID values (dates), and other columns contain data I would like to plot (temperature, light, etc.). I would like to break up this large matrix so as to plot data associated

Hi R list, I would like to automate, or speed up the process from which I take several separate datasets, stored in .csv formate, import and merge them by a common variable. So far I have greatly sped up the loading process but cannot think of a way to automate the merger of all datasets into a common data.frame. My apologies if this has been covered, any R search suggestions are

Dear all, I've come across a problem using strptime, can anyone explain what's going on? I'm using version 2.7.0 on Windows XP. Thank you Caroline First read in a data file using read.table alldata = read.table(file, header=F, skip=4, colClasses = c("character","numeric")) dim(alldata) [1] 223960 2 # inefficient, safe way of sorting out missing or dodgy

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string