search for: hufkens

...eem to solve my problem because I clearly want 1 x axis for all the graphs so not just a panel with 3 full graphs. Something like this: http://www.ats.ucla.edu/stat/sas/faq/InfluencePlots6.gif (copyright to whom copyright is due...) but with the top and bottom graphs merged. Best regards, Koen Hufkens

...If so, what is the implication of using a smaller and smaller sampling window (increasing your time resolution say going from year level to month to day level)? Any input on how to compare two time series would be appreciated... Sorry for the rather theoretical/technical post. Best regards, Koen Hufkens

Hi list, I would like to normalize a matrix (two actually for comparison) using iterative proportional fitting. Using ipf() would be the easiest way to do this, however I can't get my head around the use of the function. More specifically, the margins settings... for a matrix: mat <- matrix(c(65,4,22,24,6,81,5,8,0,11,85,19,4,7,3,90),4,4) using fit <-

Dear list, I've been messing around with coding functions in R and it just won't make sense to me. Running my analysis by hand on command line is fine and works but because of the repetitive nature of the job I would like to code a function for it. My problem: I would like to read in data from a file in my current working dir. so my code would look like: myanalysis <-

Hi list, Is there a package that covers the evaluation of spatial statistics on images and not on point data? I've converted an image matrix to x, y coordinates and a measurement value but evaluation with the package spdep (not really designed for image data I suppose) is unworkable. Any suggestions? Regards, Koen

Hi list, I have a little curve fitting problem. I would like to fit a sigmoid curve to my data using the following equation: f(x) = 1/(1 + exp(-(x-c)*b)) (or any other form for that matter) Where x is the distance/location within the dataframe, c is the shift of the curve across the dataframe and b is the steepness of the curve. I've been playing with glm() and glm.fit() but without

I've written a piece of code (see below) to do a wavelet image decomposition, during the evaluation of this code I would like to write the results of some calculations back to the R root directory. I used assign() to do so because the names should vary when going thrue a while() loop. For some unknown reason I get an error that says: Error in assign(varname[i], imwrImage) :

...Brian Ripley) > 18. Re: Calculating DIC from MCMC output (Duncan > Murdoch) > 19. Coding for contrasts in unbalanced designs > (Guillaume Brutel) > 20. Re: Coding for contrasts in unbalanced designs > (Peter Dalgaard) > 21. two-way anova nested fix effects issues > (Hufkens Koen) > 22. Coding for contrasts in unbalanced designs > (Guillaume Brutel) > 23. Re: Referencing function name from within > function > (Alberto Monteiro) > 24. Strange labels on plot with dates (Henrik > Andersson) > 25. Re: Referencing function name from wit...

Hi list, I've been trying to install the LS2W package in R. It's originally a S+plus package so things don't work out as planned. Has anyone succesfully installed this package in R (2.2.0)? Cheers, Koen

Hi list, Is there a way to process R commands in the background other then using R in batch mode? I'm looking for the equivalent of the & operator on the *nix commandline. Cheers, Koen

Hi list, I was wondering how I should go about fitting a sigmoid curve to a dataset. More specifically how I estimate parameters a and b in the following equation: 1 / 1+exp(-(x-a)*b) with b the steepness of the sigmoid curve and a the shift of the center of the sigmoid curve relative to the center of your dataframe. The fit is in function of x, the location within the input vector and y, an

Hi list, I have some anova issues... I have a dataset comprised of two factor values and a dependent value. The factors are patchiness and within patchiness I have 4 treatments. So factor treatment is nested within factor patchiness. The dependent variable is a lenght. If I'm not mistaken my model would be: model <- aov(length ~ patchiness + patchiness/treatments) However there are

Hi list, Is there a function in R to calculate the omega squared (an estimate of the dependent variance accounted for by the independent variable in the population for a fixed effects model), or does anyone have some code to let me calculate this fairly easy out of aov() or anova() output? Kind regards, Koen -- Checked by AVG Free Edition. 13:09 [[alternative HTML version deleted]]

Hi list, I'm trying to generate fractional brownian surfaces in R. [A detailed description with respect to various techniques to generating these neutral landscapes has been described by Timothy Keitt (spectral representation of neutral landscapes, Landscape Ecology, 2000) and probably various other authours.] Are there any packages that deal with this or related items? I've been

Hi list, I have a question regarding post-hoc extraction of data from a two-way categorical anova. I have a categorical anova of this form: width ~ steepness + patchiness (4 steepness levels, 4 patchiness levels) This simple setup answers if for the widths I collected across different levels of steepness and patchiness significant differences can be found. Is there a way to look at these