similar to: re: help with lattice plot

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, ...) { /

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,

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

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

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

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 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

Ben, I would like to test the sulfur on the clover field, nitrogen on the clover field and then test for the presence of interaction. Sorry about the last email, seems it really screwed itself over, here it is again, hopefully nicer: Nitrogen(0) Nitrogen(20) Sulfur(0) 4.54 5.73 Sulfur(3) 4.64

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

Hi, There's probably some simple way of doing this, but I'm just not seeing it - How do I get the background to be white instead of grey when I have a cloud plot (using the lattices package)? par(bg="white") isn't working. I'm assuming par commands won't work on lattice plots. What should I use instead? Thanks, Adrienne [[alternative HTML version deleted]]

Hi all, I'm having a problem creating png images with trellis.device. I would like to create many plots with a white background using a sequence number in the file argument (i.e. "%02d"). The first plot is as expected with a white background. However, the second and all subsequent plots have a gray background. I would like all plots to have a white (or transparent)

Hi I use sweave and have a problem with the following figure, but not with other figures: tt <- data.frame(c("a", "b", "c"), c(1.2, 3, 4.5)) names(tt) <- c("x1", "x2") bwplot(x2 ~x1, data = tt) ok now in sweave: \begin{figure}[H] \begin{center} <<echo=FALSE, fig=TRUE, height=5, width=10>>= lset(col.whitebg()) bwplot(x2 ~x1,

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

Dear R users,, I'm having difficulties when i use the gstat extensions in R especially with the graphical interface, as the variograms plots are depicted with gray background and cyan points for the number of pairs, Consequently the fitted variogram is drawn in cyan, How can i change that so i could have, white background and black lines for the fitted variogram, for the complete plot.

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

At one time (version 1.7), the code below used to work for converting and extracting based on the Date Time. In version 1.8.1, something changed I know, but I cannot for the life of me figure out what... Data: UserName,RequestDate,PO,OrderDate,ExpDelivDate,Vendor,Total "Woody, Jim",12/19/2002,AP15063,1/7/2003,2/10/2003,Ames ,8570 "Harrold,

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 ~

Apologies if this is a naive beginners question. I am trying to create a dotplot with the lattice dotplot function in which my dots are colored differently depending on if positive or negative and sized by sp.nc.bdrs.data$mwZ I have tried... dotplot(sporder ~ cvarorder | direct, data=sp.nc.bdrs.data, cex=abs(sp.nc.bdrs.data$mwZ * 0.05), xlab="climate variables",