similar to: Overlaying lattice graphs

Dear R Users, I recently posted an email on this list about the use of data.frame and overlaying multiple plots. Deepayan kindly indicated to me the panel.superposition command which worked perfectly in the context of the example I gave. I'd like to go a little bit further on this topic using a more complex dataset structure (actually the one I want to work on). >mydata Plot

Hi all! I would like to import a matlab or gauss code to R. Could you help me? Bye, Sebasti?n. 2007/6/23, r-help-request en stat.math.ethz.ch <r-help-request en stat.math.ethz.ch>: > Send R-help mailing list submissions to > r-help en stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit >

Hillary, Richard M wrote: > Morning chaps, I have a little question for your capable minds... Say > I have an observed and predicted set of quants (in my case, length > frequencies by year and age/length), is there a way to use lattice > plots to plot the observed and predicted data together, panel by panel? > Obrigado/gracias (thankyou in Galego is?...) > Rich Hi Richard, If

Hillary, Richard M wrote: > Morning chaps, I have a little question for your capable minds... Say > I have an observed and predicted set of quants (in my case, length > frequencies by year and age/length), is there a way to use lattice > plots to plot the observed and predicted data together, panel by panel? > Obrigado/gracias (thankyou in Galego is?...) > Rich Hi Richard, If

Hello R experts, what follows is my reproducible example: mydata<-structure(list(ped.avg = c(335.9, 110.8, 645.7, 638.9, 1468.1, 126.4, 4811.1, 88.5, 868.5, 656.6, 723.6, 654, 2.8, 15, 14.2, 17.5, 15.4, 112.1, 424.7, 18.3, 19.9, 28.6, 25.6, 23.5, 15.4, 27, 62.1, 15.6, 74.6), ped.erst = c(96, 53.2, 615.2, 616.5, 512.9, 56.2, 1851.8, 57.1, 579.5, 613.2, 601.1, 613.6, 1.3, 6.3, 6.5, 6.1,

#Dear All, #I'm having a bit of a trouble here, please help me... #I have this data set.seed(4) mydata <- data.frame(var = rnorm(100), temp = rnorm(100), subj = as.factor(rep(c(1:10),5)), trt = rep(c("A","B"), 50)) #and this model that fits them lm <- lm(var ~ temp * subj, data = mydata) #i want to

Dear R-users, I have found this not-so-recent post in the archives - http://tolstoy.newcastle.edu.au/R/devel/00a/0291.html - while I was looking for a particular way to reorder factor levels. The question addressed by the author was to know if the read.table function could be modified to order the levels of newly created factors "according to the order that they appear in the data

Dear R-users, I have written the following code to generate some trellis plots. It works perfectly fine except that it is quite slow when it is apply to my typical datasets (over several thousands of lines). I believe the problem comes from the loops I am using to subset my data.frame. I read in the archives that the tapply function is often more efficient than a loop in R. Unfortunately ,

Dear R-users, By default, the xyplot function automatically defines the axis ranges based upon the content of y and x variables. However, when one includes some calls to other panel.<something> functions in the panel argument, the results might be out of range and not show up in the final graphs (see lower ends of the loess line in the following example). Is there a way one can capture the

Hello, I've been looking for a solution to this problem for some time now but I seem unable to solve it.. so this is the case: I want to plot 4 time series in the same graph using xyplot(). When I do this with xyplot(mydata[,2]+mydata[,3]+mydata[,4]+mydata[,5] ~ mydata[,1], data = mydata, type = "l", auto.key = list(space="right", lines = T, points = F),

Dear R-users, I was wondering if there was a way to adjust the placement of the axis titles for the last page of a multi-page lattice plot (see example below). Depending on the total number of panels, the placement of these titles might look strange on the last page, if the layout is not adjusted (e.g. in some template code). Any thought on this issue would be welcome. Sebastien

Hello Let's say I have a multivariate zoo timeseries (synchronised automatic loggers at different places): library(zoo) library(lattice) library(latticeExtra) x<-zoo(data.frame(a=rnorm(100), c=rnorm(100), b=rnorm(100)), seq(from=as.Date("2010-01-01"), by="day", length.out=100)) and a dataframe with manual control points at variable dates:

Hi All, I am trying to understand panel functions. Let's use this example. library(lattice) time<-c(rep(1:10,5)) y <-time+rnorm(50,5,2) group<-c(rep('A',30),rep('B',20)) subject<-c(rep('a',10),rep('b',10),rep('c',10),rep('d',10),rep('e',10)) myData <-data.frame(subject,group,time,y) head(myData) Plot 1 xyplot(y ~ time

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

Hello, I need to plot the means of some outcome for two groups (control vs intervention) over time (discrete) on the same plot, for various subsets such as gender and grade level. What I have been doing is creating all possible subsets first, using the aggregate function to create the means over time, then plotting the means over time (as a simple line plot with both control & intervention

I have a number of x, y observations (Time, Conc) for a number of Subjects (with subject number Subj) and Doses. I can plot the individual points with xyplot fine: xyplot(Conc ~ Time | Subj, Groups=Dose, data=myData, panel = function(x,y) { panel.xyplot(x, y) panel.superpose(???) # Needs more here } ) I also like to plot on

After a long and winding road (sorry but I'm a novice) I get to a final result which is quite close to what I need; nevertheless I would like to tweak a little further the xyplot so that I can get ***for each single panel defined by variable z*** a finer control over: -the x and y the limits: I would like to be the same for both axes; -the number of thick marks: again I would like to be same

Dear all, here is a simple problem that surely you already come across, but is giving me a big headache... I have a dataframe like this: set.seed(3) mydata <- data.frame(var = rnorm(100,20,1), temp = sin(sort(rep(c(1:10),10))), subj = as.factor(rep(c(1:10),5))) and I need to make a scatter plot for each subj, not a problem, but... what i want is to

Hi R experts, I'm using the xyplot function in lattice to draw a multipanel plot consiting of 5x6 scatterplots. Now I need to link single points in each of those scatterplots (=panel),but the points, that need linking are different for each panel. I tried to use the panel.segments function for that, but I can't address each panel separately. Links right for panel 1, show up in all other

What is the way to add an arbitrary fit from a model to a lattice conditioning plot ? For example xyplot(v1 ~v2 | v3,data=mydata, panel=function(...){ panel.xyplot(...) panel.loess(...,col.line="red") } ) Will add a loess smoother. Instead, I want to put a fit from lm (but not a simple straight line) and the fit has to be done for each panel