similar to: split-plot analysis with lme()

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

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, I'm analysing a split-plot experiment, where there are sometimes one or two values missing. I realized that if the data is slightly unbalanced, the effect of the subplot-treatment will also appear and be tested against the mainplot-error term. I replicated this with the Oats dataset from Yates (1935), contained in the nlme package, where Variety is on mainplot, and nitro on

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,

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, ...) { /

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, 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 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

Dear all, I have a problem. I have m variables each of which has n observations. I want to calculate pairwise correlation among the m variables and store the values in a m x m matrix. It is extremely slow to use nested 'for' loops if m and n are large. Is there any efficient alternative to do this? Many thanks for your suggestions!! Bill

I'm using R 1.7.0 on linux. With this version of R the package modreg is automatically loaded at start of session. However attempting to use predict.smooth.spline() produces Error: couldn't find function predict.smooth.spline. The function smooth.spline() is OK. What am I missing? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West

Can anyone explain why with latest version of R (2.0.1) on FC3, installed from R-2.0.1-0.fdr.2.fc3.i386.rpm, update.packages() produces the message /usr/lib/R/bin/Rcmd exec: INSTALL: not found. Indeed /usr/lib/R/bin seems to lack various shell scripts (INSTALL, REMOVE, etc). ====================================== I.White University of Edinburgh Ashworth Laboratories, West Mains Road Edinburgh

https://bugs.freedesktop.org/show_bug.cgi?id=103689 Bug ID: 103689 Summary: there is an exploitable page fault that can be reliably triggered from the chromium sandbox can possibly lead to remote attackers causing a denial of service condition or possibly running system code. Product: xorg

At the top of page 283 of Pinheiro and Bates, a covariance structure for the indomethicin example is specified as random = pdDiag(A1 + lrc1 + A2 + lrc2 ~ 1) The argument to pdDiag() looks like a two-sided formula, and I'm struggling to reconcile this with the syntax described in Ch4 of the book and online. Further down page 283 the formula is translated into list(A1 ~ 1, lrc1 ~ 1, A2 ~ 1,

After a one-way anova, se.contrast computes the standard error of a contrast, but not the value of the contrast itself. Wouldn't this be useful? Am I missing something? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West Mains Road Edinburgh EH9 3JT Fax: 0131 650 6564 Tel: 0131 650 5490 E-mail: iwhite at staffmail.ed.ac.uk

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

To create a more end-user readable table captions for modeld, I would like to get rid of the I(...) construct in formulae ("what's the hell does the I(..) mean in the contrast table") Example: effect ~ I(sqrt(nitro))*treat + I(nitro^2) should giv effect ~ sqrt(nitro)*treat + nitro^2 In know, this is a dumb model, just my test case. As far I remember, finding matching parentheses

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

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### # The last line of following code produces a segmentation fault: x <- 1:10 f <- gl(5,2)

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