similar to: interaction() -- problem with drop (PR#1003)

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

Matthew Wiener <mcw@ln.nimh.nih.gov> writes: > t1 <- data.frame(matrix(rnorm(16), nc=4)) > t1$X1 <- 1 > t1$X2 <- 2 > print(t1) > Error: dim<- length of dims do not match the length of object Well, it is prototype-compatible. Splus 5.3 does likewise. A way out is t1<-data.frame(unclass(t1)) However, we do seem to have a bug in the area: > t1 <-

Matthew Wiener <mcw@ln.nimh.nih.gov> writes: > t1 <- data.frame(matrix(rnorm(16), nc=4)) > t1$X1 <- 1 > t1$X2 <- 2 > print(t1) > Error: dim<- length of dims do not match the length of object Well, it is prototype-compatible. Splus 5.3 does likewise. A way out is t1<-data.frame(unclass(t1)) However, we do seem to have a bug in the area: > t1 <-

Hi, all. The following does not behave as I think it should, and as it seems to me it has in the past (although I can't check this easily). I know it happens in both R-0.64.2 and R-0.65.0 on an old Power Computing running Linux-PPC 1999, and in R-0.64.2 on an SGI running Irix 6.5. Try the following: t1 <- data.frame(matrix(rnorm(16), nc=4)) > t1 X1 X2 X3 X4 1 -0.7206945

Full_Name: Matthew Wiener Version: 1.1.1 OS: linux Submission from: (NULL) (156.40.248.102) I've come across a glitch in hist. I can reproduce it on linux for Intel, linux for PPC, and Irix 6.5. t1 <- c(41, 42, 42, 43, 43, 43, 44, 44, 45, 46) hist(t1, breaks = 10) (OK) hist(t1/50, breaks = 10) Error in hist.defauilt(t1/50, breaks = 10): some 'x' not counted; maybe

How do we specify a contrast interaction matrix for an ANOVA model? We have a two-factor, repeated measures design, with Cue Direction (2) x Brain Hemisphere(2) Each of these has 2 levels, 'left' and 'right', so it's a simple 2x2 design matrix. We have 8 subjects in each cell (a balanced design) and we want to specify the interaction contrast so that: CueLeft>CueRght

Hi, all. The help file for tapply says that if simplify is true, and the result of the calculation is always a scalar, then tapply will return a vector. Nonetheless: > t1 <- tapply(runif(10), rep(1:5, 2), mean) > is.vector(t1) [1] FALSE > is.array(t1) [1] TRUE > I have found this in version 0.65.1 on an SGI running Irix 6.5, and on a Mac running Linux-PPC. I've also

Dear All user, Hello, I'm a student and I have some trouble with the experimental (columns-experiments) design of my project. I use a randomized block design with 4 treatments including a control. For each treatment, I use 3 replicates and 3 blocks. The treatments are: -T1 = COD (300 mg/Lit) COD=chemical oxygen demand -T2 = COD (200 mg/Lit) -T3 = COD (100 mg/Lit) -T4 = COD (0 mg/Lit) as

Hi all, I'm trying to test a repeated measures model with random effects using the nlme library. Suppose I have two within subjects factors A, B both with two levels. Using aov I can do: aov.1 <- aov(y ~ A*B + Error(S/(A+B)) following Pinheiro and Bates I can acheive the analagous mixed-effects model with: lme.1 <- lme(y~A*B, random=pdBlocked(list(pdIdent(~1),pdIdent(~A-1),

Hi, I have a dummy question. Suppose that I have two explanatory variable, T1 (A, B) and T2 (C, D) and one response variable. > attach(dados) > tapply(Y,list(T1,T2),mean) C D A 2.200000 10.20000 B 2.223333 20.26667 In this case, "A" and "B" inside "C" have no difference, but have differences inside "D" I make this model: > m

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Dear all R users, I want to realize 800 000 ANOVAS and to store Sum of Squares of the effects. Here is an extract of my table data Product attribute subject rep t1 t2 t3 … t101 P1 A1 S1 R1 1 0 0 … 1 I want to realize 1 ANOVA per timepoint and per attribute, there are 101 timepoints and 8 attributes so I want to realize 808 ANOVAS. This will be an ANOVA with two factors : Here is one example:

Dear R-Community! The example "oats" in MASS (2nd edition, 10.3, p.309) is calculated for aov and lme without interaction term and the results are the same. But I have problems to reproduce the example aov with interaction in MASS (10.2, p.301) with lme. Here the script: library(MASS) library(nlme) options(contrasts = c("contr.treatment", "contr.poly")) # aov: Y ~

The code below is intended to analyse a textbook example of a balanced incomplete block design: # # Data taken from pp. 219-230 in # Cox, D.R. (1958) Planning of Experiments. John Wiley and Son, Inc. New York. 308 pp. # day <- factor(rep(1:10, each = 3)) T <-

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

Dear list, I am trying to choose the scale parameter for the cgt transform but I don't know how to do it. In time I would like to be able to separate points 30 samples apart, and in frequency I would like to separate bands 0.04 Hz apart. I tried the two approaches described below and they gave me different results. I would appreciate advise on how to do this. The Rwave Gabor transform uses

Thanks to John Fox, Brian Ripley, and Peter Dalgaard for responding. The short answer (as in Peter Dalgaard's reply, already posted to the list) is that the models I'm concerned with can in fact be compared using ancova. The key fact is that while the parameters may not be nested, the subspaces I'm examining are. An additional note from Prof. Ripley on AIC and BIC (which I quote in

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

dear all, i'm trying to reproduce an spss-anova in R. It is an 2x3x3 repeated measures desingn with repeated contrasts. In R i've coded a contrast matrix for all factors and made a split in the aov summary - but I can't get the repeated interaction contrasts. The output from SPSS looks like this: TaskSw * CongNow * CongBefore: SS df Mean Square F Sig. 1 vs. 2 1 vs. 2 1 vs. 2