similar to: ggplot2 legend for vertical lines

Hi list, This is a very basic question about lattice: I wish to add some vertical lines in each panel of a xyplot as demonstrated in this example: > library(lattice) > > xx <- seq(1, 10, length=100) > x <- rep(xx, 4) > y <- c(cos(xx), sin(xx), xx, xx^2/10) > fact <- factor(rep(c("cos", "sin", "id", "square"), each=100))

Dear list, I've been trying this for a few hours and I just don't understand how lattice works with groups and subscripts. Consider the following example, > xx <- seq(1, 10, length=100) > x <- rep(xx, 4) > y <- c(cos(xx), sin(xx), xx, xx^2/10) > fact <- factor(rep(c("cos", "sin", "id", "square"), each=100)) > fact2

Hello, I am plotting many histograms together using facet_grid in ggplot2. However, I want to then add a vertical line to each histogram, or facet, each of which vertical lines are at different x-values. The following example adds all vertical lines to each facet: ggplot(data,aes(values)) + geom_histogram() + facet_grid(.~variable) + geom_vline(xintercept=c(5,10,15)) How can I add a vertical

Dear R list, I'm having difficulties in choosing between a list or a data.frame, or an array for the storage and manipulation of my data (example follows). I've been using the three for different purposes but I would rather like to know which is more adapted to what task. Here is the data I'm currently working on: 200 observations, each observation being a vector of length

Dear R helpers, I'm trying to write a numerical scheme for a boundary integral method to solve an electromagnetic problem. This requires the computation of the distance between points at the surface of an object (a sphere, in my example). Here is my code, > require(rgl) > r<-1 > size<-10 > theta<-seq(0,2*pi,length=size*2) > phi<-seq(0,pi,length=size) > pc

DeaR list, I happily use eigen() to compute the eigenvalues and eigenvectors of a fairly large matrix (200x200, say), but it seems over-killed as its rank is limited to typically 2 or 3. I sort of remember being taught that numerical techniques can find iteratively decreasing eigenvalues and corresponding orthogonal eigenvectors, which would provide a nice alternative (once I have the

Hello R list, I have two matrices of identical dimensions, and I want to fit a straight line for each pair of columns and plot the resulting lines. I got it to work with a for loop, but there must be a better way, > n<-5 > N<-10 > > data.x<-matrix(1:(n*N),ncol=n) > data.y<-matrix(1:(n*N) + rnorm(n*N,sd=1),ncol=n) > >

Hi, I have two small issues with my R code, no big deal but curiosity really. Here is a sample code, > > x <- rnorm(1:10) > > foo <- function(a = 1, b = list(x = c(1:10), y = c(1:10))){ > > for (ii in seq(along=b$y)){ > > print(x[ii] + b$x[ii]) > } > > > } > > foo() # default OK > > foo(b=list(x=1, y=c(1:10))) # only the first

Hi, I need to create a text file in the following format, > 1 100.0 0 > 0 0 > 1 1 > 0 0 > 1 1 > # > 1 100.0 0 > 0 0 > 0 1 > 1 0 > 1 1 ... where # is part of the format and not a R comment. Each block (delimited by #) consists of a first line with three values, call it dose, and a list of (x,y) coordinates which are a matrix or data.frame, >

Dear R-users, consider the two following outputs, ## 1 and ## 2 \begin{Scode}{Setup, echo = FALSE, print = FALSE, eval = TRUE} with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2)) ## 1 xtable(with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2))) ## 2 \end{Scode} The first line with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2))

Hi, I've started my own (first) package, part of which consists in listing common physical constants (Planck's constant, the speed of light in vacuum, etc). I'm wondering what would be a good way of dealing with pairs of value/unit. > constants <- list( cel = 2.99792458e8 , #m/s > Z0 = 376.730313461, #ohm > eps0 = 8.854187817e-12,#F/m > mu0 = 4*pi*1e-7,#N/A^2

I am trying to construct a two-way table where, instead of printing the two-way frequencies in the table, I would like to print the values of a third variable that correspond to the frequencies. For example, the following is easily constructed in R > fact1 <- factor(sample(LETTERS[1:3],10,replace=TRUE)) > fact2 <- factor(sample(LETTERS[25:26],10,replace=TRUE)) > fact3

DeaR list, I'm a bit lost in the behavior of substitute and co. I often use fairly long axis labels in my graphs (long to write, that is). Typically, they would contain some greek letters and units with exponents, as in: > xlab=expression(paste("text ", alpha, " / ", V,".", m^{-3}, ".", > kg^{-2}, ".", l^{4})) To make this a

DeaR list, Has anyone tried to mix the Sweave paradigm with the Markdown[*] (and co.) syntax? Would this be hard to implement? My tiny understanding of Sweave is that one can define new drivers for the text part, while some functions that deal with the R code would not require any modification. Here's the reason I'm interested in Mardown for a driver. I've been orbiting

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

Dear R users, I'm porting a piece of Matlab code to R, but I'm now stuck with the following: I need an equivalent of besselJ(x, nu) that can handle a complex argument x. I couldn't find any R implementation. I did find a possible fortran solution in SLATEC (< http://www.netlib.org/slatec/ > , CBESJ-C), however I've never tried to use external C or Fortran code

Dear R users, I'm porting a piece of Matlab code to R, but I'm now stuck with the following: I need an equivalent of besselJ(x, nu) that can handle a complex argument x. I couldn't find any R implementation. I did find a possible fortran solution in SLATEC (< http://www.netlib.org/slatec/ > , CBESJ-C), however I've never tried to use external C or Fortran code

DeaR list, Pardon the stupidity of this question but I've been trying this for a while now without success. I've followed the example given in the green book "programming with data", and I now have a working example of a S4 class with a few methods (plot, summary, as.data.frame). It's all very nice in one file, but I cannot find the way to put this information in a

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.