similar to: glht after lmer with "$S4class-" and "missing model.matrix-" errors with DATA

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Hi, I'd like to unsubscribe from the list. Thanks Neyra ________________________________ De: "r-help-request@r-project.org" <r-help-request@r-project.org> Para: r-help@r-project.org Enviado: sáb, octubre 30, 2010 5:30:07 AM Asunto: R-help Digest, Vol 92, Issue 31 Send R-help mailing list submissions to r-help@r-project.org To subscribe or unsubscribe via the

Hello everybody. In my case, calculating multiple comparisons (Tukey) after lmer produced the following two errors: > sv.mc <- glht(model.sv,linfct=mcp(comp="Tukey")) Error in x$terms : $ operator not defined for this S4 class Error in factor_contrasts(model) : no 'model.matrix' method for 'model' found! What I have done before: > sv.growth <-

Fellow R Users: I'm not extremely familiar with lda or R programming, but a recent editorial review of a manuscript submission has prompted a crash cousre. I am on this forum hoping I could solicit some much needed advice for deriving a classification equation. I have used three basic measurements in lda to predict two groups: male and female. I have a working model, low Wilk's lambda,

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

Dear all; First of all, this is probably a more conceptual question than a R-related one, but still want to give it a try. When working with the interpolation function "interp" from the package akima and the triangulation function "tri.mesh" from package tripack I've got NA's for the interpolation and "error" for the triangulation. The data is arranged in a

Dear R user, I want to do sparse principal component analysis (spca). I am using elastic net package for this and spca() and the code is following from the example. My question is How can I decide the *K =? *and *para=c(7,4,4,1,1,1)) . So, here k=6 i.e the no of Principal Components. and each pcs say , * ** pc1 number of non zero loading is 7 pc2 number of non zero loading

hi, I rewrote escape, unescape, and query_parse in a C extension. http://s3.amazonaws.com/four.livejournal/20070922/http_helpers.tar.bz2 Here are some sample benchmarks (the benchmark script is included in the package) user system total real escape: Single long Mongrel: 1.680000 0.020000 1.700000 ( 1.837793) HttpHlp: 0.030000 0.010000 0.040000 ( 0.036590)

----- Forwarded message from Adam Ottley <aottley@shaw.ca> ----- Date: Wed, 30 Jul 2003 19:48:16 -0600 From: Adam Ottley <aottley@shaw.ca> Resent-From: Adam Ottley <aottley@shaw.ca> To: Debian Bug Tracking System <submit@bugs.debian.org> Subject: Bug#203581: xmms-flac: Plugin misplays certain valid files Package: xmms-flac Version: 1.1.0-9 Severity: normal Certain

I cannot understand the following error printed out when I try to get the extrema of my time series. I would appreciate some suggestion as I really cannot interpret the error. I might not be using a proper set of parameters in calling such functions. I am learning by doing ... > aa.peak <- wavCWTPeaks (aa.tree) Error in `row.names<-.data.frame`(`*tmp*`, value = c("1",

I am trying to create a messaging system for my users but I''m having a hard time designing my db. This is what i have in mind, but I am not sure if its the best approach. user has_one inbox user has_one outbox inbox has_many messages outbox has_many messages inbox table user_id outbox table user_id messages table box_id (refers to either inbox or outbox - how?) from_id to_id

Hi all, I have a large dataset with ~8600 observations that I want to compress to weekly means. There are 9 variables (columns), and I have already added a "week" column with 51 weeks. I have been looking at the functions: aggregate, tapply, apply, etc. and I am just not savvy enough with R to figure this out on my own, though I'm sure it's fairly easy. I also have the Dates

I got sidetracked while benchmarking ruby factorial code: http://rosettacode.org/wiki/Factorial#Ruby I put all that code into test_fact.rb and ran with: > ruby test_fact.rb > rails runner test_fact.rb #from a fresh rails app > rails runner test_fact.rb #from a mature rails app here are the respective results: user system total real #ruby recursive:

I have a problem with including extra data in a lattice graphic. I am trying to put some calculated values in an extra column of a boxplot. A minimal example should show what I am trying to do: foo <- data.frame( Treatment=rnorm(1:12,2),

I have a couple of quick questions that I do not see answered on the wiki or the Asterisk archives. I dropped an note to the Dev list and got an "awating moderator" notice. I realized I had left out some important info, so I cancelled the message and resubmitted it, but have not gotten another "awaiting moderator" message. I suspect it was too similar to the original that I

Dear all, I have a matrix with positive and negative values. >From this I would like to produce 2 matrices: 1st - retaining positives and putting NA in other positions 2nd - retaining negatives and putting NA in other positions and then apply rowMeans for both. I am trying to use the function ifelse in the exemplified form: ifelse(A>0,A,NA) but by putting A as a 2nd parameter it

Dear Colleagues, I have a dataframe with variables: [1] "ID" "category" "a11" "a12" "a13" "a21" [7] "a22" "a23" "a31" "a32" "b11" "b12" [13] "b13" "b21"

Dear List, My colleague has been having a problem with the following data and plotting commands. The example below is part of a larger set of plots, but I've isolated the problem to this example using this small dataset (below), which kills rgui consistently. My version info > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status

On Thu, 18 Dec 2003 18:02:07 +0000, Gavin Simpson <gavin.simpson@ucl.ac.uk> wrote : >Dear List, > >My colleague has been having a problem with the following data and >plotting commands. The example below is part of a larger set of plots, >but I've isolated the problem to this example using this small dataset >(below), which kills rgui consistently. My version info

Dear all, Before I can get into serious Fourier analysis of Radon time-series I am practising with 24hour and 24.8hour sinusoids to assist with my interpretation of signals ittributed to tidal input to Radon time series. I am stuck. I have tried researching this to no avail. I am awating a book that should describe fourier transforms in detail and another one that should discuss the R