similar to: Dead wood code

Just a quick question. Is there a way to easily specify factor levels in a function definition within nls? For example, I am trying to fit a 3 parameter, nonlinear Weibull function to tree height and I would like to have results by species (or down the road a bit, by plot). I am hoping there is someway to do it easily, similar to the "gas example" in the ANCOVA chapter in Modern

Could someone give me a hand with the format of the gsummary function? Basically, I have a large set of xyz coordinates generated by LiDAR data (>37 million points) and I am trying to derive various summary statistics on the z-coordinates by a grid cell. I wrote a function to do this by creating factors from the x- and y- coordinates and then using gsummary. However, I want the function to

I wish to do a stacked area chart to show how relative proportions of species within a stand have changed over time. I know this is simple, but can someone point me to the right function (if it exists). I have not had any luck finding it in the R-help, but maybe I am searching using the wrong keywords. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department

This is a strange request, but I want to build a scatterplot using different image files (jpegs, gif, etc.) as the plot symbols. I have thought about setting this up using a very large layout matrix, but I thought someone might have a better approach. Furthermore, is there any way to have R paste an image file into a specific coordinate within a scatterplot? Thanks in advance, Mike Mike

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

R community: I have been creating code for plotting nomographs, or multiple, overlain contour plots of z-variables on a common x- and y- variable. My input has been a matrix with observed x, y, and multiple z variables; I then create a trend surface using trmat for each z-variable. So far so good. One application I have for these, requires shading a portion of the nomogram that meets criteria

I am modeling a trend surface using trmat and want to trim the resulting matrix to the area enclosed by my real data (i.e., remove all the extrapolated areas). I was using chull and in.chull to calculate the convex hull and change all the other values created by trmat to NA. However, my real data has portions that are slightly concave so chull would give me slivers that are extrapolations from

Dear list, If anyone knows how to assign dimnames to matrices or arrays I would be most grateful for help. I've tried various permutations of likely-looking code but get error messages every time. I could find no example in the documentation. Many thanks, Dan Bebber Department of Plant Sciences University of Oxford South Parks Road Oxford OX1 3RB UK Tel. 01865 275000

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Some notes on installing rgeos in R 2.14.0 on a Linux Mint 11 (x86_64-pc-linux-gnu) machine 1. rgeos 0.1-15 will not run with GEOS 3.2.0-1, which is the version currently available on Synaptic package manager 2. I installed GEOS 3.3.1 (the latest version) from http://trac.osgeo.org/geos/, but rgeos will not run with this either 3. I then install GEOS 3.2.3, and now rgeos works fine! It seems

Dear list, there are now >400 packages available on CRAN. Would it be useful to classify these packages according to what they do (e.g. classification, graphics, spatial statistics), to assist the user in finding the appropriate package for their problem? Or perhaps the search facility is enough. I would attempt such a classification, but my knowledge of statistical methods isn't good

Dear R list I have an m * n matrix P and a vector V of length n containing indices for rows in P. For each of the m columns I want to extract the value in the row specified by V, and put these values into a new vector W of length n. At present I am doing this with a for.... loop, but I imagine there is a faster way that doesn?t involve loops. If anyone knows the way I would be most grateful.

Hello, My data are numbers of trees in plots sampled in a number of forest stands. Some stands were subjected to a treatment, others not. Several plots were sampled per stand to get a better idea of what the stand means were, but replication is really at the stand level. Therefore I think this is a split-plot design. I would like to know whether the treatment affected the number of trees, so:

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I would like to extract predicted failure times from a coxph model in library(survival). However, none of the prediction options ("lp", "risk", "expected", "terms") seem to bear any relationship to failure time. Perhaps I am asking the wrong question, but can coxph provide predicted failure times? Thanks, Dan Bebber Department of Plant Sciences University

Hello, when I try to use the filled circle (pch = 16) in a Windows metafile, it appears highly pixelated rather than as a smooth vector. The other filled circles (pch = 19 and 20) are vector circles, filled with pixels. Results are the same whether I use windows() and save or copy as a metafile, or win.metafile() Also, some of the other symbols are misshapen, e.g. the diamond pch = 23 is not

Hello, I've just located the illuminating explanation by Douglas Bates on degrees of freedom in mixed models. The take-home message appears to be: don't trust the p-values from lme. Questions: Should I give up hypothesis testing for fixed effects terms in mixed models? Has my time spent reading Pinheiro & Bates been in vain? Is there a publication on this issue? Thanks, Dan Bebber

I am plotting subsets of my data, using ylim. This works fine, but the outer margin line widths of the plot are thin, due to clipping. If I include > trellis.par.set(clip=list(panel = "off")) then the outer margin line widths are fine, but the outlying data is visible. Is there any way of achieving both correct margin line widths and clipping of outlying data? Thanks, Dan Bebber

Do any of you know any simple programming editors for R scripts which offer basic colour-coding and bracket-matching facilities? Dregging through scripts to find a missing comma or parentheses is something I'd rather do less of... Jon Minton [[alternative HTML version deleted]]

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error