similar to: statistics for ragged arrays: loop to vector

Thanks to Roger for pointing out the packages spweights and sptests to me. I believe they will be quiet useful in my work. But... I can't get them to work! Firstly, I realised that a newer version of R is available. I have now downloaded and installed it. When I try to 'install packages from CRAN' I get: trying URL `http://cran.r-project.org/bin/windows/contrib/PACKAGES' unable

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

Hello R-users, I am aware that this is a very basic question, but still... Since mode() function is reserved for something else, is there a function to calculate statistical mode for a vector or a matrxi? i.e. mode.function(c(1,2,2,2,3,3))=2 Thanks in advance, Elena Moltchanova Elena MOLTCHANOVA IIASA International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria E-Mail:

Hi, everybody! I am currently dealing with spatial estimation and thus writing some smoothing algorithms. My question concerns the graphical output. I would like my function to produce a picture of input data (X) and of estimation results(Y). Both, X and Y are categorical variables which can take values 1,2,...,Cl. So, basically I write foo<-function(){ ....

Hi All, I'm wanting to convert a ragged list of values into a structured matrix for further analysis later on, i have a solution to this problem (below) but i'm dealing with datasets upto 1GB in size, (i have 24GB of memory so can load it) but it takes a LONG time to run the code on a large dataset. I was wondering if anyone had any tips or tricks that may make this run faster? Below is

How can I add an extra column containing the rank to a ragged array indexed by more than one grouping factors? E.g. with the barley dataset: How can I to add an additional column ``rank'' containing the rank of the ``yield'' of the different varieties in relation to the indices ``year'' and ``site'' to the barley dataframe? I achieved to calculate the ranks with:

Hello, I have a big problem which I’m just not able to solve. I created the following mean value from the following dataset structure: Id |value 1 | 2 1 | 3 1 | 4 2 | 2 2 | 1 3 | 5 4 | 3 etc.|etc. with the command: mean_rating <- tapply(ratok$value, ratok$project_id , mean,simplify = FALSE) this gives me a ragged array: > mean_rating [1] $`14`

I have a dataset that looks like: set.seed(144) sam<-sample(1000,100) dat<-data.frame(id=letters[1:10],value=rnorm(1000),day=c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100))) I want to "normalise" it using the following function (unless you have a better idea...): adj.values<-function(dframe){ value_mean<-mean(dframe$value) value_sd<-sd(dframe$value)

Can anyone please suggest how I can print: a <- matrix(c( "Heading 1", "This is some info\nabout heading 1", "Heading 2", "This is some info\nabout heading 2", ), byrow=T, nrow=2) to look like: Heading 1 This is some info about heading 1 Heading 2 This is some info about heading 2 (if you're not using a fixed width

I wonder what's the right way in R to do the following -- placing objects of the same kind together in subarrays of varying length. Here's what I mean: > word <- c("a","b","c","d","e","f","g","h","i","j") > kind <- c(1,1,1,2,3,4,5,5,7,7) > d <-

Hi, I have a list made up of character strings with each item a different length (each item is between 1and 6 strings long). Is there a way to convert a "ragged" list to a matrix such that each item is its own row? Here is a simple example: a=list(); a[[1]] = c("a", "b", "c"); a[[2]] = c("d", "e"); a[[3]] = c("f",

Hi and thanks in advance, I am fairly new with R so I hope this problem isn't too amateur. I have a vector of count data which correspond to vectors of date (%m/%d/%Y) and time of day (%H:%M:%S). I am trying to compute various statistics (e.g. daily max) by lumping the data together by day. I have been able to utilize tapply() and group the counts together, but with the method I use I end

Hello, I'm running an algorithm for graph structural cohesion that requires a depth-first search of subgraphs of a rather large network. The algorithm will necessarily be redundant in the subgraphs it recurses to, so to speed up the process I implemented a check at each subgraph to see if it's been searched already. This algorithm is very slow, and takes days to complete on a

Hello, everybody! I have subscribed to the list only recently, although I've been actively using R since February. I found it very useful and very flexible and I hope to learn to use it better. My current problem concerns spatial statistics. I need to partition a regular rectangular grid into given number of subsets. For example for a 5*5 grid with 3 subsets I could have: 1 1 1 2 2 1 1 2

I have a large dataset (~1 million rows) of three variables: ID (patient's name), DATE (of appointment) and DIAGNOSIS (given on that date). Patients may have been assigned more than one diagnosis at any one appointment - leading to two rows, same ID and DATE but different DIAGNOSIS. The diagnoses may change between appointments. I want to subset the data in two ways: - define groups

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

Just wondering how come read.table lets you specify fill=TRUE for ragged arrays, but so far as I can tell, no equivalent for write.table? Not a big deal, since I'm perfectly comfortable w/ write and scan and the other file I/O goodies. "A foolish inconsistency..." and all that. Carl

Hi I have data for voting behaviour on two (related) binary votes. I want to examine the second vote, running separate regressions for groups who voted different ways on the first vote. As the votes are not independent, I guess that there is an issue with selection bias. So, I think I would like to fit a heckit style model but with a binary dependent variable - so, in effect, two successive

The following single line of code shows what I am trying to do, and the problem I am having... barplot(c(101,102,103),ylim=c(100,103)) The 'xaxis' is missing, and the grey bars 'fall off' the plot area. This is generally ugly, and I would like to trim the bars (ideally they would have a ragged appearance to show that I am 'zooming in'). I can see why what I am trying to

Ok, I have a new, multipart problem that I need help figuring out. Part 1. I have a three dimensional array (species, sites, repeat counts within sites). Sampling effort per site varies so the array should be ragged. Maximum number of visits at any site = 22 Number of species = 161 Number of sites = 56 I generated the array first by;