similar to: lattice overlay

Hi Dieter and R community: I tried both of these three versions with ylim as suggested, none work: I am getting only single (pch = 16) not overlayed (pch =3) everytime. *vs 1* require(lattice) xyplot(Sepal.Length ~ Sepal.Width | Species , data= iris, panel= function(x, y, subscripts) { panel.xyplot(x, y, pch=16, col = "green4", ylim = c(0, 10)) panel.lmline(x, y, lty=4, col =

Dear all, Consider this plot xyplot(Sepal.Length + Sepal.Width ~ Petal.Length | Species, data = iris, allow.multiple=T, outer=F, panel = function(x,y,...) { panel.xyplot(x,y,...) } ) I want to *add* some things to each panel and what I want to add involves using the data for each panel, so I try to take this subset of data out with subscripts:

Hallo, I'd like to add grid lines to a lattice graph having 2 series of Y data. See these 2 examples: data(iris) [1] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales = "same",type="l", ) [2] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales =

Hello R-helpers, I'm trying to customize a graphic in lattice using the 'legend' argument to add labels on my plot but in the process I'm losing the legend drawn by 'auto.key', despite the fact that I'm actually not sticking these on the same sides of the graphic. I worked up a quick and simple example with the iris data : ### here's the basic graph

R-helpers, I'm having difficulty with customizing strip names for a lattice graphic. Here is an example using the iris data set : xyplot(Sepal.Length+Sepal.Width~Petal.Length,groups=Species,data=iris) ## I'd like to change the 2 strip names to "Length" and "Width" for example, this is what I've tried :

How can I insert mathematical expressions for variable names in a lattice parallel plot? I tried to implement mathematical expressions in varnames, however, without success. For example, neither parallel(~iris[1:4] | Species, iris, varnames=c("P[Width]", "Petal[length]", "alpha[Width]", "Sepal[Length]")) nor parallel(~iris[1:4] | Species,

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

Hello, I'll use part of the iris dataset for an example of what I want to do. > data(iris) > iris<-iris[1:10,1:4] > iris Sepal.Length Sepal.Width Petal.Length Petal.Width 1 5.1 3.5 1.4 0.2 2 4.9 3.0 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5

Dear all The trouble with the current duplicated() function in is that it can report duplicates while searching fromFirst _or_ fromLast, but not both ways. Often users will want to identify and extract all the copies of the item that has duplicates, not only the duplicates themselves. To take the example from the man page: > data(iris) > iris[duplicated(iris), ] ##duplicates while

Dear all What is the easy way to drop a variable by using its name (and not its number)? Example: > data(iris) > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1

Hello, I am puzzled by the behavior of hist() when generating multiple plots per page on the pdf device. In the following example two pdf files are generated. The first results in 4 plots on one pdf page as expected. However, the second, which swaps one of the plot() calls for hist(), results in a 4 page pdf with one plot per page. How might I get the histogram with 3 other scatter

Hi, [I sent this to the plyr mailing list (late) last night, but it seems to be lost in the moderation queue, so here's a shot to the broadeR community] Apologies in advance for being more verbose than necessary, but I'm not even sure how to ask this question in the context of plyr, so ... here goes. As meaningless as this might be to do with the `iris` data, the spirit of it is what

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

I am making a battery of levelplots and wireframes for several fitted models. I wrote a function that takes the fitted model object as the sole argument and produces these plots. Various strange behavior ensued, but I have identified one very concrete issue (illustrated below): when my figure-drawing function includes the addition of points/lines to trellis plots, some of the

Hi all, I'm trying to achieve a few things using the lattice package but am failing miserably. I am plotting side by side box plots and using a grouping variable, e.g. cols <- c("Sepal.Width", "Petal.Length", "Petal.Width") stackedData <- stack(iris[, cols]) df <- data.frame(y = stackedData$values, x = rep(iris$Species, 3), which = gl(3,

Dear all I know that the R way of documenting things is to work on your project in package development mode, and document each object (such as data frames) in a *.Rd files. This should work for gurus. What about a simpler way to document things, geared for mere mortals? I was thinking of a label() or tag() function that could store and retrieve an alphanumeric comment for a given object (for

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

In using cor(data.frame), it is annoying that you have to explicitly filter out non-numeric columns, and when you don't, the error message is misleading: > cor(iris) Error in cor(iris) : missing observations in cov/cor In addition: Warning message: In cor(iris) : NAs introduced by coercion It would be nicer if stats:::cor() did the equivalent *itself* of the following for a data.frame:

Hi! Probably a simple question, but I can't get any tick marks in the 3d scatterplot I created using the cloud function. The following works to display the three groups using different symbols: data(iris) cloud(Sepal.Length ~ Petal.Length * Petal.Width, data = iris, cex = 1.2, groups = Species, pch = c(16,1,1), col = c("black","black","red"), subpanel =

I'm looking for something in R to fit a multivariate linear model robustly, using an M-estimator or any of the myriad of other robust methods for linear models implemented in robustbase or methods based on MCD or MVE covariance estimation (package rrcov). E.g., one can fit an mlm for the iris data as: iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~ Species,