similar to: correlation matricies: getting p-values?

Is there a simple way to run cor.test on for a matrix of correlations? Of course, cor on a data frame produces a correlation matrix, but cor.test will only take two variables at a time. Is there a way to get behavior similar to that of cor with cor.test? I suppose the programming alternative would be to run two for loops with the number of items and cor test embedded accessing the columns of

Hi, I have two matricies a and x: a<-matrix(c(3,4,5,2,3,4,1,1,2), nrow=3, ncol=3) [,1] [,2] [,3] [1,] 3 2 1 [2,] 4 3 1 [3,] 5 4 2 x<-matrix(c(3, NA, NA, NA, 2, 5, NA, 2, 2), nrow=3, ncol=3) [,1] [,2] [,3] [1,] 3 NA NA [2,] NA 2 2 [3,] NA 5 2 I wish to combine these two into one matrix using the values from x where x has values, and

I would like to run a logistic regression on some factor variables (main effects and eventually an interaction) that are very sparse. I have a moderately large dataset, ~100k observations with 1500 factor levels for one variable (x1) and 600 for another (X2), creating ~19000 levels for the interaction (X1:X2). I would like to take advantage of the sparseness in these factors to avoid using GLM.

I hope you can help with this one. I have two matricies: 1. A species abundance matrix: <http://r.789695.n4.nabble.com/file/n4643979/2species_matrix.jpg> 2. A species trait score matrix: <http://r.789695.n4.nabble.com/file/n4643979/2trait_matrix.jpg> The trait matrix lists trait scores for each species as listed in the species abundance matrix. I would like to create a script

Hi, I've been reading through the Theora format spec and API reference trying to find an answer to this but haven't come across anything certain. I'm wondering whether it would be theoretically possible to change the encoded bit rate (or target video qiality) during a live stream without a standard theora decoder having any problems. I had assumed that this must be possible, given

Hi. I know matrices that use distances between places works fine when using cmdscale. However, what about matricies such as: A B C D E A 0 1 23 12 9 B 1 0 10 12 3 C 23 10 0 23 4 D 12 12 23 0 21 E 9 3 4 21 0 i.e. matrices which do not represent physical distances between places (as they would not make sense for real distances such as the one above)

Dear list Has anyone got a recipie at hand to import .bil files into R? From what I understand the .bil files I got contain layered matricies which I would lke to make available in R as an array or list. GIS people seem to be familiar with the .bil format but I am not using any GIS software and would prefer to deal with the data in R. I use the latest version of R on Mac OSX 10.5.8.

Hi Folks, I've been bumping my head against the 4GB limit for 32-bit R. I can't go to 64-bit R due to package compatibility issues (ROBDC - possible but painful, xlsReadWrite - not possible, and others). I have a number of big dataframes whose columns all sorts of data types - factor, character, integer, etc. I run and save models that keep copies of the modeled data inside the model

Hi Everybody I need to "transport" some data and results to use another application (Sweave via LyX - where debugging is very difficult) in order to build a report. Is it possible to store a collection of variables of different types (like named integers, matricies, data frames and two lists) all in one list and save it and then simply load it again later and "unpack" the

I have tried a lot of ways around this, but I can't find a way to make apply work in a generalized way because it causes a failure whenever reduces the dimensions of its output. The following example is easier to understand than the question. I wish it had a "drop=TRUE/FALSE" option like the "[" (and I wish I had found the drop option a year ago, and I wish that I had 1e6

Hi, I have this sample data below and would like to create a list of matricies. setseed(1254) id <- c(1,1,1,1 ,2,2,2) o <- as.factor(c(1:4, 1, 3, 4)) r <- rep(.5, 7) v <- rnorm(7) s <- rnorm(7) dat <-data.frame(id, o, r, v, s) dat #> dat # id o r v s # 1 1 0.5 0.7024631 2.0813672 # 1 2 0.5 -0.5541955 0.1095156 # 1 3 0.5 -1.0418167 0.4164930 # 1

Hi I have calculated lots (>1000) of linear models and would like to store each single result as an element of a 3D matrix or a similar storage: something like glm[i][j][k] = lm(...) Since I read that results are lists: Is it possible to define a matrix of type list? Or what do you recommend? Many thanks Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

I am sorry if this is documented in apply's dcumentation or completely obvious, I could not find or work it out. Given an matricies Q: 2x3, R:1x3 and S:1x2 apply(Q, 1, '-', R) is 3x2 and apply(Q, 2, '-', S) is 2x3 Why? cheers Worik > Q [,1] [,2] [,3] [1,] 1 2 3 [2,] 10 11 12 > R [1] 1 4 7 > S [1] 1 4 > apply(Q, 1, '-', R) [,1]

Hi, I would like to estimate a VAR of the form: Ay_t = By_t-1 + Cy_t-2 + ... + Dx_t + e_t Where A is a non-diagonal matrix of coefficients, B and C are matricies of coefficients and D is a matrix of coefficients for the exogenous variables. I don't think the package {vars} can do this because I want to include contemporaneous cross-variable impacts. So I want y1_t to affect y2_t and I

I am sure this si a simple problem but the solution is evading me. I have a list of matrices all with the same number of columns but different number of rows. The first two columns label the row. The labels are allways the same for the same row numbers, just some matricies have more rows. For example using 3 column matrices... > q.1 <- function(r){return(cbind(seq(0, 10, by=1)[1:r],

Hi all, I'm searching for a little clarification on partial mantel tests (ecodist package) I've a distance matrix (x,y), and several others containing environmental/chemical variables. Based on the help file, and the package instructions I've managed to implement the tests as; var1 ~ env1 + space to partial out the effect of space and test the relationship between the

Dear R users, I am putting together reading and resources lists for spatial statistics and spatial econometrics and am looking for some pointers from more experienced practitioners. In particular, I find two "camps" in spatial modelling, and am wondering which approach is better suitied to which situation. The first camp is along the lines of Venables and Ripley's Chapter 14

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

Dear members, I have started to work with R recently and there is one thing which I could not solve so far. I don't know how to define macros in R. The problem at hand is the following: I want R to go through a list of 1:54 and create the matrices input1, input2, input3 up to input54. I have tried the following: for ( i in 1:54) { input[i] = matrix(nrow = 1, ncol = 107)

Is there anyway to make the lines in the dispersion command come forward in a plot and allow the fill in the dispersion parameter be transparent so all of the lines I am using to note confidence intervals are shown? ------------------------------------------- Joe King, M.A. Ph.D. Student University of Washington - Seattle 206-913-2912 jp@joepking.com