similar to: Adding groups to regression line panel function in Lattice

I wish to create a multipanel plot (map) from several datasets ("d" and "q" in the example below). I can condition the main xyplot statement on the "site" variable, but I don't know how to pass a conditioning variable to panel.xyplot plot so that the x-y coordinates from dataset q are only plotted at the appropriate site. library(lattice) d <-

I have a moderately complex graph with three panels. There are data points plotted, and fitted lines are added using a panel function, which includes "with(alt.data[[which.packet()[1]]]" statements. It all graphs out beautifully, but none of the usual tricks to get the proper legend to plot are working, i.e., using auto.key, key, etc. One message I keep getting is Error in

I am wanting to generate panels showing scatterplots with the linear fitted line for two groups within each panel superimposed. I have two conditioning factors, "variable" and "Group" and I want separate panels for each level of "variable" with different symbols and "lmline"s for each level of "Group". However all observations for a group are

Dear R-sians! I am trying generate a bunch of xyplots library(lattice) myPanel <- function(x,y,xl=range(x),yl=range(y),...) { panel.xyplot(x,y, pch=20,col='blue',cex=0.7,xlim=xl,ylim=yl,...) panel.abline(v=0, col='gray30',lty=2,lwd=1.5,...) panel.loess(x,y, span=2/3,family='gaussian',normalize=T,col='red',lwd=1.5,...) #

Dear All, I wonderif there is a simple way to draw a regression line in the xyplot: more specifically, let: age <- c(20:30, 31:40 ) age.cut <- cut(age, breaks = 2 ) y<- rnorm(20) x <- rnorm(20,4,1) xyplot(y ~ x| age.cut, xlab="x", ylab="y") How to draw (in the plot given by xyplot) the two regression lines (y ~ x) corresponding to the two category

I wish to identify groups representing different treatments, but to plot them and do a regression using a continuous variable ("cover") ignoring the groupings. d$year <- NA d$year <-c(rep(2007,12), rep(2008,12)) d$treatment <- c(rep("A",4),rep("B",4),rep("C",4), rep("A",4), rep("B",4), rep("C",4)) d$cover <-

Hi, I have a data.frame df with names(df) = c("Var1", "Var2", "Var3", "Var4") and I plot data with xyplot(Var1+Var2~Var3|Var4, data=df) I want to use different panel functions for Var1 and Var2. How can I do ? Something like : panel.mypanel = function(x, y, ...) { if (Var1) panel.Var1Panel(x, y, ...) else panel.Var2Panel(x, y, ...) }

Dear R-users, A while ago, Deepayan Sarkar suggested some code that uses the group argument in bwplot to create some 'side-by-side' boxplots (https://stat.ethz.ch/pipermail/r-help/2010-February/230065.html). The example he gave was relatively specific and I wanted to generalize his approach into a function. Unfortunately, I seem to have some issues passing the correct arguments to the

Dear R Users, I have an xyplot() where different plotting symbols are used for subgroups (originally used within S-Plus, but hopefully it?s also applicable to R users). How can I fit separate regression lines for every subgroup? So far, I can only plot the overall fitted line. The code looks like this: trellis.device() sps<-trellis.par.get("superpose.symbol") sps$pch<-1:7

Dear friends - please give me a hand. I have a dataset of 40 patients in two groups observed on three occasions. I only want to plot a line for each patient in the two groups. I use the ltext function to put the patient number but fail to make lattice understand the numbers as unique since apparently it starts all over with 1:20 for each panel instead of respecting my desires to have 1:20

Dear r-help, I need to draw xyplot() graphs with several regression lines: one line for the whole range of x (the variable on the horizontal axis) and two additional lines for subranges of x. Is it possible to make first regression line (panel.lmline(x,y,...);) to be drawn on the whole graph and regression lines of the subsets to be drawn only over their subsets? I have defined

I think the subject says it all. I want to make a simple lattice plot, using xyplot with the argument type=c("l","a"). The problem then is that in the resulting plot it is difficult/impossible to see which plot corresponds to the average and which to the individual profiles. I triedthings like extra arguments lwd=c(1,3) or col=c("blue","red") hoping

Dear expeRts, I would like to colorize the backgrounds of a pairs plot according to the respective panel number. Here is what I tried (without success): count <- 0 mypanel <- function(x, y, ...){ count <<- count+1 bg. <- if(count %in% c(1,4,9,12)) "#FDFF65" else NA points(x, y, cex=0.5, bg=bg) } U <- matrix(runif(4*500), ncol=4) pairs(U, panel=mypanel) I

Hi, is it possible to reverse the order in which panel.lmline() or panel.smooth() operation in xyplot() ? This type of situation might occur when plotting some variable with depth, but the relation we want to describe is variable ~ depth, and not depth ~ variable, as the plotting formula would suggest. # an example: d <- 1:100 v <- d * rnorm(100) xyplot(d ~ v, ylim=c(100,0),

I'm trying to use plotting character to encode the variable "block" from my dataset in a conditioned lattice graphic (R 1.9.0 on Mac OS 10.3.3). The data I'm using is the dataframe "dryoutcover" which is here (4k): http://anthony.darrouzet-nardi.net/downloads/dryoutcover.Rdata The code that generates my graphic almost correctly is as follows: xyplot(coversage ~

I have two data sets with locations, X, Y of houses (df.house) and habitats(df.habitat), respectively. In each dataset, there are 3 replicates (Repeat). Because each replicate has different locations of houses and habitats, I would like to plot them in panels. I wrote something like this: mypanel<-function(x,y,subscripts,...){ panel.xyplot(x,y,pch=20) panel.xyplot(

panel.lmline returns intercept and slope of y ~ x subsetted to the combination of conditioning factors given to xyplot in lattice. for instance: xyplot(Xvalues ~ log(Qvalues)|Tfac, data = df7, panel = panel.lmline) I am looking to find the equivalent formulation for lm() proper. If I do this: lmcal <- lm(Xvalues ~ log(Qvalues):Tfac, data = df7) Only one value of the intercept is returned.

Using R v. 1.7.0 on Windows 2000 I would like to plot the fitted values of a model as a function of a continuous covariate, augmented with data (e.g., augPred) grouping by combinations of fixed effects. I have not been able to use augPred effectively, and am wondering if it does not handle unbalanced data (3 out of 192 missing). I include below the model and an xyplot that almost does the

I would like to use lattice graphics to plot multiple functions (or groups or subpopulations) on the same plot region, using different line types "lty" or colors "col" to distinguish the functions (or groups). In traditional graphics, this seems straightforward: First plot all the data using 'type="n"', and subsequently execute a series of "points"

Dear r-help, Sleep-deprivation from having 2 youngsters under 2 around the house is fuzzing my brain, so please be gentle if the answer to this query is obvious! In the example below, I'm trying to use grid.rect to add grey rectangles to the panels of a lattice plot to indicate which months spawning occurred of a (very cute) native Tasmanian fish. The fish in the two lakes spawned at