similar to: an efficient way to calculate correlation matrix

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 ~

Dear List, I have been using R to create an xyplot using the panel function within lattice libraries. This plot is based on the data supplied in R named 'Oats'. The graph represents oat yield by nitro level with an overlay of each variety of oats for each nitro level. I have three questions regarding this graph: 1) I cannot seem to specify the type of symbol used by the plot, even though

Dear R users, this is a follow up of this message http://tolstoy.newcastle.edu.au/R/e6/help/09/05/13897.html I'm reproducing the core of it for convenience. > // > / data(Oats, package = "MEMSS") / > / tp1.oats <- xyplot(yield ~ nitro | Variety + Block, / > / data = Oats, / > / panel = function(x, y, subscripts, ...) { /

Full_Name: Kevin Wright Version: 1.8.0 OS: Windows 95 Submission from: (NULL) (170.54.59.160) This bug also happens to me using R 1.9.0 on Windows 2000. Took me a long time to create a reproducible bug, but I think I have succeeded. I suspect my test function has a bug, but I don't see anything wrong. Plus, the bug only shows up when selecting certain filenames. Nearest I can speculate,

The following code produces a legend ("key") that mentions the unused levels of Block. library(MEMSS) xyplot(yield~nitro, subset=(Block=="I" | Block=="II"), data=Oats, group=Block, auto.key=T) and adding "drop.unused.levels=T" does not fix it. And in fact even the following does not solve the problem: xyplot(yield~nitro,

Hello, Are there any access functions to the various residual variables that should result from a repeated measures ANOVA ? MyAOVObject$residuals does not exist, and simply printing MyAOVObject gives a very long print of all fields in the result list, many of which I can't see what they are exactly : $error.qr$qraux, for instance. What I would like basically is to inspect those residuals

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

When we call a lattice function such as xyplot, to what extent does the "data" designation cause the function to look inside the "data" for variables? In the examples below, the "subset" argument understands that "Variety" is a variable in the data. But the "scales" argument does not understand that "nitro" is a variable in the data.

Dear all, Consider a completely randomized block design (let's use data(Oats) irrespoctive of the split-plot design it was arranged in). Look: library(nlme) fit <- lme(yield ~ nitro, Oats, random = ~1|Block, method="ML") fit2 <- lm(yield ~ nitro + Block, Oats) anova(fit, fit2) gives this: Model df AIC BIC logLik Test L.Ratio p-value fit 1 4 624.3245

Hello, can anybody tell me how to produce a plot like the one in http://cran.r-project.org/web/packages/lme4/vignettes/Implementation.pdf on page 13, Figure 6? The data is stored in: library(nlme) data(Oats) Cheers -- View this message in context: http://r.789695.n4.nabble.com/Help-needed-in-reproducing-a-plot-tp4119603p4119603.html Sent from the R help mailing list archive at

Dear R-supporters, I have results of lme similar to those shown in Fig. 1.21, p.51 of Pinheiro/Bates. However, In my data set, one of the panels is missing, leading to an ugly frame shift of the following panels. How can I replace one of the panels by an empty one to restore the raster? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Hi use Rs, I have a csv file: "1989-90","1990-91" Barley,23,34 Oats,15,16 Which I want to turn into: year, Barley, Oats 1 "1989-90", 23, 15 2 "1990-91",34,16 Transpose doesn't quite do it, is there a standard way? Cheers, Geoff Russell

hello, I'd like know how to sort a data frame in R for example how I should do to sort by Catholic with swiss data frame like below thanks Fertility Agriculture Examination Education Catholic Infant.Mortality Courtelary 80.2 17.0 15 12 9.96 22.2 Delemont 83.1 45.1 6 9 84.84 22.2

You''ll need to install the ''openid'' gem for this, and require it in your camping app: class Login < R ''/login'' def get this_url = ''http:'' + URL(''/login'').to_s unless input.finish.to_s == ''1'' # start doing the auth here begin

Hi all, I have a problem in that when I plot points that have a high degree of precision, some significant rounding seems to occur, resulting in uneccessary overlap of my points. Is there a way to specify the resolution or precision in plotting functions? Is there an underlying grid I have to modify somehow? Many Thanks, Jon -- Mares eat oats and does eat oats and little lambs eat ivy. A

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

hello, I'd like know how to do to extract data from a frame for example how can I do to extract only the data where variety=victory or variety=golden rain thanks. > Oats Block Variety nitro yield 1 I Victory 0.0 111 2 I Victory 0.2 130 3 I Victory 0.4 157 4 I Victory 0.6 174 5 I Golden Rain 0.0 117 6 I Golden Rain

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

The following output results from fitting models using lmer and lm to data arising from a split-plot experiment (#320 from "Small Data Sets" by Hand et al. 1994). The data is given at the bottom of this message. My question is why is the sum of squares for variety (V) different in the ANOVA table generated from the lmer model fit from that generated by the lm model fit. The