similar to: oldlogspline probabilities

I'm wondering if anyone is aware of a way to take what is visible in the R graphics window, pixelate it, and express that pixellation in a numeric matrix. For example, I am using the following command to create a black (present) and white (absent) spatial simulation of grass tussock distribution. > symbols(x=runif(100,-.5,1.5),y=runif(100,-.5,1.5),circles=runif(100)/30,

Hi all, In the (very simple) example below, I have defined a random effect for the residuals in lme(). So the model is equivalent to a FIXED effect model. Could someone explain to me why lme() still gives two standard deviation estimates? I would expect lme() to return either: a) an error or a warning for having an unidentifiable model; b) only one standard deviation estimate. Thank you for your

A couple of my functions that were working last week seem to have been changed over the weekend and no longer work, but I can't understand why not: it seems that objects defined at the start of the function are not located further on in the function, when this worked fine before. An example follows at the end, using the package spatstat, though the problem seems more general. My only guess is

Friends, I changed one line of a package at the source level and then rebuilt it. When I run R CMD check, I get an error: installing R.css in C:/polsplineRS.Rcheck ---------- Making package polsplineRS ------------ adding build stamp to DESCRIPTION making DLL ... making hareall.d from hareall.c making heftall.d from heftall.c making lsdall.d from lsdall.c making lspecall.d from lspecall.c

Thanks it worked for me. I wanted to plot days since planting on x-axis 1 and years on x-axis 3. LAI_simulation$Date <- as.Date( LAI_simulation$Date, '%Y/%m/%d') LAI_simulation$Date <- as.integer(LAI_simulation$Date - as.Date("2009-10-07")) plot(LAI~Date,data=LAI_simulation,xlab="Days since Oct, 7,

Dear all, This is probably more related to statistics than to [R] but I hope someone can give me an idea how to solve it nevertheless: Assume I have a variable y that is a function of x: y=f(x). I know the average value of y for different intervals of x. For example, I know that in the interval[0;x1] the average y is y1, in the interval [x1;x2] the average y is y2 and so forth. I would like to

I am trying to generate two dimensional random coordinates. For randomly distributed data I have simply used >xy<-cbind(runif(100),runif(100)) However I also want to generate coordinates that are more uniformly distributed, and coordinates that are more contagiously distributed than the above. Can anyone make any suggestions Thanks. Dr Terry Beutel Rangeland Scientist Animal

On Solaris x86, does zpool (or anything) support PATA (or SATA) IDE SMART data? With the Predictive Self Healing feature, I assumed that Solaris would have at least some SMART support, but what I''ve googled so far has been discouraging. http://prefetch.net/blog/index.php/2006/10/29/solaris-needs-smart-support-please-help/ Bug ID: 4665068 SMART support in IDE driver

Dear All Does any one know if the following survival analysis exists, and if so, is it available in R? I am trying to parametrically model survival where some events happen at time = 0. I am particularly interested in generating the 95% confidence interval around the survival function over its full range (including time=0). For example, the data below are for time (Time) to component failure