search for: danbebb

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 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

...t 11 64bit. Thanks, Dan ________ Dr Dan Bebber Head of Climate Change Research, Earthwatch Institute, 256 Banbury Road, Oxford OX2 7DE, UK Research Fellow in Biology, St. Peter's College, University of Oxford Tel. +44 (0)1865 318842, Mob. +44 (0)7729 167502, Fax. +44 (0)1865 318824, skype danbebber, email dbebber at earthwatch.org.uk, web http://www.earthwatch.org/europe This e-mail (and any attachments) is confidential and may contain personal views, which are not the views of Earthwatch Institute Europe unless specifically stated. If you have received it in error, please delete it from...

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, I wrote a wrapper for symbols() that produces a bivariate bubble plot, for use when plot(x,y) hides multiple occurrences of the same x,y combination (e.g. if x,y are integers). Circle area ~ counts per bin, and circle size is controlled by 'scale'. Question: how can I automatically make the smallest circle the same size as a standard plot character, rather than having to

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

Dear List, I have circular data comprising direction and size for a number of observations. I wish to calculate statistics such as the mean direction and concentration for these data. I note that the CircStats package only deals with angular data (i.e. "Each observation is treated as a unit vector or a point on the unit circle". There appears to be no facility for weighting

Could someone please give a brief explanation, or pointer to an explanation, of the following error: > arima(ts.growth, order = c(1,0,0),include.mean=T) Error in arima(ts.growth, order = c(1, 0, 0), include.mean = T) : non-stationary AR part from CSS and why it does not arise with > arima0(ts.growth, order = c(1,0,0)) Many thanks ____________________________ Dr. Daniel P. Bebber

Motivated by the discovery of 'loglet analysis' (http://phe.rockefeller.edu/LogletLab/) that allows one to decompose growth curves into a series of logistic equations, I attempted to do the same thing in R. SIMULATED DATA Time <- 1:200 pop.size <- SSlogis(Time,10,20,5) + SSlogis(Time,20,100,20) + rnorm(length(Time)) MY ANALYSIS results <- nls(size ~ SSlogis(Time, Asym1, xmid1,

Dear list, Apologies, I have sent this message before but received no replies so I'm trying again just in case... Motivated by the discovery of 'loglet analysis' (http://phe.rockefeller.edu/LogletLab/) that allows decomposition of growth curves into a series of logistic equations, I attempted to do the same thing in R. #SIMULATED DATA Time <- 1:200 pop.size <-

Hello, I have a model glm(Y ~ X, family = quasipoisson(link = "identity")) I would like to vary the coefficient for X and observe the effect on the deviance. Is this possible? Many thanks, Dan Bebber Department of Plant Sciences University of Oxford

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

I received the following error when I attempted to update my packages: updating HTML package descriptions Warning messages: 1: DESCRIPTION file of package 'file28862' missing or broken in: packageDescription(p, lib = lib, fields = pkgFlds) 2: number of columns of result not a multiple of vector length (arg 2) in: rbind(retval, c(p, lib, desc)) 3: DESCRIPTION file of package

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:

Hello, I am having trouble specifying a suitable nlme model. My data structure is described by gd <- groupedData(ppath ~ lcut | exp, outer = ~ bait, inner = ~ weight, data = d) i.e. the response (ppath) of several subjects (sub) was measured at levels of a continuous variable (lcut). Subjects were given either of one level of a factor (bait), and all subjects were measured at two levels

glm(S ~ -1 + Mdif, family=quasipoisson(link=identity), start=strt, sdat) gives error: > Error in glm.fit(x = X, y = Y, weights = weights, start = start, etastart > = > etastart, : > cannot find valid starting values: please specify some strt is set to be the coefficient for a similar fit glm(S ~ -1 + I(Mdif + 1),... i.e. (Mdif + 1) is a vector similar to Mdif. The error