similar to: biplots for PCA

I am using bagging to perform Bagged Regression Trees on count data (bird abundance in Britain and Ireland, in relation to climate and land cover variables). Predictions from the final model are visually believable but I would really like a diagnostic equivalent to classification success that can be used to decide if a model is adequate. Whereas with classification data an error rate is returned,

Good morning, I use SAS and R/S-Plus as my primary tools so I have a lot of experience with these programs. By far and away, SAS is superior for handling the "messy" datasets, but also the very large ones. I work at times with datasets in the hundreds of thousands (and on occasion, millions) of records. SAS, and especially PROC SQL, are invaluable for this. But once I get to

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

R-users, Is there a function for ceiling to the nearest ten? a <- 1:10*4 a [1] 4 8 12 16 20 24 28 32 36 40 The resulting vector should look like this ("ceiling to the nearest ten") [1] 10 10 20 20 20 30 30 40 40 40 Thanks in advance Lauri

Hi there I have an interesting problem: My csv file is of array dimensions [12,50], but it was saved the wrong way: there should be only 11 colums. What happens now if I read it into R is that the whole data set is shifted ( in the first row, the last column contains already the first value of the supposed second row and so on...) how can I tell R to switch after 11 read values to the next row,

Has anyone any experience of applying observational weights in bagging? I am performing regression trees (continuous data on bird abundance) and need to account for sampling intensity. In a single tree, i.e. a call of rpart, I can specify weights either by having a separate vector called weights, or by a variable called weights in the dataframe under analysis. Both produce sensible (and identical)

Hi all, I've used R for basic programming and data management for a few years now. One of the things that I think could be improved is the data editor. Its a great feature and I use it alot by calling edit(data.frame); very useful to see if what you tried to do actually worked. However, one of the annoying things about it is that when you scroll down the window it doesnt show you all the

Hi all, This could be naivety/stupidity on my part rather than a problem with model output, but here goes.... I have fitted a fairly simple model m1<-glm(count~siteall+yrs+yrs:district,family=quasipoisson,weights=weight,data=m[x[[i]],]) I want to know if yrs (a continuous variable) has a significant unique effect in the model, so I fit a simplified model with the main effect ommitted...

hi all, I often have a data frame like this example data.frame(sq=c(1,1,1,2,2,3,3,3,3),area=c(1,2,3,1,2,3,1,2,3),habitat=c("garden","garden","pond","field","garden","river","garden","field","field")) for each "sq" I have multiple "habitat"s each with an associated "area". I

Hi all, I have been trying to use zeroinfl() with the pscl package with R version 2.1.1. and with the newest versions of the contrib packages compatible with R 2.1.1. I have read the examples, the vignette and all the posts relating to zeroinfl() but I am still confused as to how to structure the model. Here is a small example; the error message is the same for big data sets

Hi all, I recently discovered how great win.metafile is for getting high resolution graphics into word. Having problems with specifying families though... #pdf works fine pdf("test.pdf",width=14,height=9) par(family="Helvetica") plot(1:10) text(4,4,"trial") dev.off() windows 2 #metafile doesnt like helvetica family

Is rsync supposed to replicate empty files? Jacob

Hi all, Is it possible to do a db name change during the course of the project and then propagate the effect all across the project? E.g. I started of doing a sample program with db name as "tests". As the project evolved I decided to change its name to "biodatas". " create_table "tests", :force => true do |t| t.column

Hi all, This is partly a statistical question as well as a question about R, but I am stumped! I have count data from various sites across years. (Not all of the sites in the study appear in all years). Each site has its own habitat score "habitat" that remains constant across all years. I want to know if counts declined faster on sites with high "habitat" scores. I can

Hi all, I would like to 1. Read in an arcmap image into R (I can export pretty much any type of image jpeg, bitmap etc from arcmap) 2. Use R to create a nice colour legend in the plot First of all, Is this possible? So far I'm stuck on point 1. I have tried read.pnm() from pixmap and read.jpeg() from rgl. the pnm example provided works fine x <-

Does anybody know of a script of a simple method of converting mbox mail stores to maildirs? I'm moving from an old pop3 system to imap and after installing about half our office with mboxes have found that mbox does not support subfolders which some people like to use. Is there any simple way of converting? I can go to each individual account and "manually" convert swaping data

Hello, we have a large data set of water quality data and want to identify influence variables or *why* different groups of data are different (in an explorative sense). We consider to use biplots, however there are only the common PCA-Biplots available in R (biplot.princomp). We consider to use transformations (log-type, x^a, Box-Cox), but are seeking for alternatives. Are there

Dear R I'm trying to plot the lda means onto a 2 D plot of discriminant scores. Preferably I'd like these to be in a larger font compared to the discriminant scores. I tried skull.mean.pred <- predict(skulls.lda, as.data.frame(skulls.lda$means), dimen=2) from which I got skull.mean.pred $class [1] 1 2 3 4 5 Levels: 1 2 3 4 5 $posterior 1 2 3 4

I want to plot biplots. I have seen the function biplot, but there are some extra features I would like, that I do not know how to achieve. 1. My observations, and my variables, fall into groups. Is there a way to, say, plot the observations in several different colours, according to which group the observation falls into? Similarly with the variables? I am used to the lattice idiom, in which one

Hello - I am having a problem with the function 'integrate'. I am running R on OSX (R 2.5.1). I am trying to suppress the error message when 'integrate' attempts to integrate across a parameter set giving a non-finite function value. I'm using it in a MCMC / simulated annealing algorithm, and it is entirely possible that some parameter sets will give non-