similar to: Adding points on top of lines in xyplot

All, On p.52 of Deepayan Sarkar's Lattice book there is a nice plot of showing residuals with median lines superimposed or various groups: library("lattice") stripplot(sqrt(abs(residuals(lm(yield~variety+year+site)))) ~ site, data = barley, groups = year, jitter.data = TRUE, type = c("p", "a"), fun = median) Suppose we wanted to make a similar plot for a

All, How does one replace plot symbols with say subject IDs when using xyplot? Or superimpose them next to plot symbols? I searched the archives under various key words but haven't had much. Any suggestions or links much appreciated. Sample code below. David junk.frm = data.frame(ID = rep(1:16, each = 2), x, y, z = rep(c("D", "P"), 16)) y = c( 0.4, 0.6, -0.1,

All, I've coded a function and it works manually if I copy it line by line into R. However, when I try to "load" (copy and paste) the entire function into R, I get the following error after the listed line of code: + N.j.list = lapply(rej.hyp, length) Error: symbol print-name too long Does anyone you know what this error means? Strangely, when I copy the same line verbatim

All, I have data across 5 time points that I am graphing via xyplot, along with error bars. For one of the variables I have missing data for two of the time points. The code below is okay but I can't seem to get the lines to connect across the missing time points. Does anyone now how to rectify this? Cheers, David Afshartous library(lattice) ## the data junk = data.frame( Visit =

All, Below is an xyplot plot with multiple panels and a title produced via main: library("lattic") data.ex = data.frame(y = rnorm(10), t = rep(1:5, 2), group = rep(c(0,1), each = 5)) xyplot(y ~ t | as.factor(group), data = data.ex, main = list("Put figure caption here xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx want this line left justified" )) I must be mis-interpreting the

All, I'm having problems w/ a simple attempt to subset an xyplot. The first plot below is a plot of y versus x for certain values of a third categorical variable z. Now I'd like to further restrict this to certain values of variable y. Neither of the two attempts below work. Any suggestions much appreciated. (note: I don't want to merely use ylim since I have a loess plot and I

All, I have a simple lattice plot where I have set log = TRUE for the y scale. When I attempt to change the tick locations via the "at" argument within scales, the supplied numeric vector is not followed. Any suggestions much appreciated for the example below: y = c(10^1.5, 10^2, 10^3, 10^2) t = c(1,2,3,4) xyplot(y ~ t, type = "b", scales = list(y = list(log = TRUE))) ##

All, When saving plots to a pdf file via the pdf function, I would like to be able to automatically expand the graphics device to achieve the same result as when one does this manually (e.g., clicking the green expand button on the upper left of the graph on Mac OS). Consider simple example below: library("lattice") foo.frm = data.frame(Subject = rep(c(1:4), each = 9), Y =

All, Sorry for overly simplistic question, but I can't seem to remember how to create the basic plot shown in Figure 1.1 of Pinheiro & Bates (2004; p.4). The y-axis delineates a factor (Rail) while the x-axis displays the distribution of a continuous variable (time) according to each level of the factor. Didn't see it in archives but perhaps I'm not searching on correct key words,

All, I can't seem to get auto.key to work properly in an xyplot that is employing panel.text. Specifically, I often change the default grouping colors then use auto.key accordingly, but for some reason the same functionality isn't working for this different type of plot. Any help much appreciated. Cheers, David library("lattice") dat = data.frame( Y = c(rnorm(18,1),

All, The code below produces a color 3D graph. I'd like to make it black and white shading. I tried setting col.regions to FALSE but this just made it completely white. I want the graph to look exactly as is, except black (or grey) and white shading. Is this possible? Cheers, David p.list = c(.01, .1, .25, .5, .75, .9) cov.list = c(.2, .1, .05) ### leave out .01 X = rep(p.list, 3)

All, I'm witnessing some strange behavior when checking the values of one of my variables. My guess is that it has something to do with the number of significant digits being represented, but perhaps not as my variable was created from other variables that only had one decimal place. See below. I'm sure this is something basic, any suggestions much appreciated. Cheers, David >

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

All, I'd like to plot the main relationship of a grouped data object for all levels of a factor in a single panel. The sample code below creates a separate panel for each level of the factor. I realize that this could be done in other ways, but I'd like to do it via plotting the grouped data object. thanks! dave z = rnorm(18, mean=0, sd=1) x = rep(1:6, 3) y =

All, When I take a subset of a factor the reduced factor still maintains all the original levels of the factor when say forming the key in a plot. The data is correct, but the variable still "remembers" the original levels. See below for reproducible code. Does anyone know how to fix this? cheers, dave fact = as.factor(c(rep("A", 3),rep("B", 3), rep("C",

All, In the function below I have 24 individuals and 6 calculations per individual. The 6 calculations are collected each time in a 1:24 loop when calculating "delta". I'd like to collect all 144 = 24*6 calculations in one vector ("delta.patient.comb"). The function works as is via indexing, but is there an easier way to collect the measurements via appendinng the 6

All, For data consisting of serial measurements on subjects, one may use the aggregate function to say compute the peak response for each subject for each design condition. Is there a way to alter this or another one-liner to also retain the time at which the peak occurred and thus avoid writing a doing this via a loop? I suppose one could attempt to employ the split function but that's

All, I'm trying to plot an all.effects() object, as shown in the help for all.effects and also Crawley's R book (p.178, 2007). The data has a repeated measures structure, but I'm using all.effects for the simple lm() fit here. Below is a reproducible example that yields the error message. fm.ex = lm(dv ~ time.num*drug*X, data = dat.new) fm.effects = all.effects(fm.ex, xlevels =

Hello all, I have matrices V.i of dimension n.i x n.i, where i = 1, ..., J, and the sum of n.i equals N. (and n.i ! = n.j) goal: create one large matrix V, where V has matrices V.i on diagonal. I create each matrix V.i in a for loop (1 to J), so each time I'd like to augment V with the most recently calculated V.i, such that I'll have V after the final iteration of the for loop.

All, I have a repeated measures data frame and was wondering if the covariance matrix can be calculated via some created indexing or built-in R function. Specifically, say there are 3 variables, where potassium concentration is measured 6 times on each patient. Patient number (discrete) Time (1 to 6, discrete) Potassium (continuous variable) I want the covariance/correlation matrix for the