similar to: cannot assign dimnames

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.

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

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

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:

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

I have a data set in which I have 5000 repeated measures on 6 subjects over time (varying intervals, but measurements for all individuals are at the same times). There are two states, a "resting" state (the majority of the time), and a perturbed state. I have a continuous measurement at each time point for each of the individuals. I would like to determine the "state"

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

> > I am looking for a more elegant way to write below code. > > #Simulation results have different dimensions > mysim <- lapply(1:10, function(y) { > two.mat <- matrix(rnorm(4), nrow = 2) > four.mat <- matrix(rnorm(16), nrow = 4) > list(two.mat = two.mat, four.mat = four.mat) #results with different dimensions > }) > > #Collect different

I would use replicate() to do an operation with random numbers repeatedly: ``` mysim <- replicate(10, { two.mat <- matrix(rnorm(4), 2, 2) four.mat <- matrix(rnorm(16), 4, 4) list(two.mat = two.mat, four.mat = four.mat) }) ``` which should give you a matrix-list. You can slice this matrix-list just like normal, then cbind it in one step: ``` two.mat <-

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

Dear list-members I am new to R and a statistics beginner. I really like the ease with which I can extract and manipulate data in R, and would like to use it primarily. I've been learning by checking analyses that have already been run in SAS. In an experiment with Y being a response variable, and group a 2-level between-subject factor, and time a 5-level within-subject factor. 2

I have a matrix which I wanted to convert to a data frame. As I could not succeed and resorted to export to csv and reimport it again. Why did I fail in the attempt and how can I achieve what I wanted without this roundabouts? The original matrix: > str(comb_model0) num [1:90, 1:4] 3.5938 0.0274 0.0342 0.0135 0.0207 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:90]

Hello Everyone, My name is Thomas and I have been using R for one week. I recently found your site and have been able to search the archives of posts. This has given me some great information that has allowed me to craft an initial design to an inquiry I would like to make into the breakdown of McNemar's test. I have read an intro to R manual and the posting guides and hope I am not violating

Is there a package, or does anyone have code they are willing to share, that would allow me to simulate sampling of dead wood pieces across an area? I am specifically looking for code to simulate the dead wood distribution as small line segments across an extent, and then I will "sample" the dead wood using different sampling techniques including line transects, fixed area plots, etc.

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

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

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 <-

I would like to buy a basic statistics book (experimental design, sampling, ANOVA, regression, etc.) with examples in R. Or download it in PDF or html format. I went to the CRAN contributed documentation, but there were only R textbooks, that is, textbooks where R is the focus, not the statistics. And I would like to find the opposite. Other text I am trying to find is multivariate data

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