similar to: Using 'dimname names' in aperm() and apply()

Hi, R seems to have a feature that isn't used much, which I don't really now how to call. But, the dimnames function, can in addition to giving names to rows/columns/dim 3 rows/dim 4 rows... can also give labels to the dimensions themselves. Thus, I can do: A = matrix(1:9,3,3) dimnames(A) = list(from=c(), to=c() ) and now, printing a prints these dimension labels nicely: > A

R version 1.0.1 OS RedHat Linux 6.1 In attempting to test for numeric vectors in a data frame, I tried: apply(dataframe,2,is.numeric) and found that it returned FALSE for all vectors whether they were numeric or not. I tracked this to the fact that as.array() was converting the data frame to character vectors, and thought I could solve it by using array(), which preserved the mode of the

I'm trying to convert an S-Plus program to R.  Since I'm a SAS programmer I'm not facile is either S-Plus or R, so I need some help.  All I did was convert the underscores in S-Plus to the assignment operator <-.  Here are the first few lines of the S-Plus file:   sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2),

Hello everyone I'm a beginner in Stats and R, I'm using R 2.10.1. I need to create a multivariate qq plot, there is 8 variable group with each has 55 number of input. An example of what I did so far, just to get my point out: > data=read.csv(file.choose(),header=T) > data country village group av_expen P2ary_ed no_fisher 1 Cook Islands Aitutaki D

Hi everyone I've been playing with do.call() but I'm having problems understanding it. I have a list of "n" elements, each one of which is "d" dimensional [actually an n-by-n-by ... by-n array]. Neither n nor d is known in advance. I want to bind the elements together in a higher-dimensional array. Toy example follows with d=n=3. f <-

aperm() was designed for multidimensional arrays, but is also useful for table objects, particularly with the lattice, vcd and vcdExtra packages. But aperm() was designed and implemented before other related object classes were conceived, and I propose a small tune-up to make it more generally useful. The problem is that aperm() always returns an object of class 'array', which

Hi everyone. I'm playing with aperm(): a <- 1:24 dim(a) <- c(2,3,2,2) permutation <- c(1,2,4,3) b <- aperm(a,permutation) So if my understanding is right, a[1,3,2,1] == b[c(1,3,2,1)[permutation] ] but this isn't what I want because the RHS evaluates to a vector, and I am trying to identify a single element of b. How do I modify the RHS to give what I want? Following

I have been tinkering with mosaicplot() and friends as a way of learning R. As part of this, I've written a pair.table() method for mosaic matrices, and would like to extend mosaicplot to work with loglin and logln (MASS) objects. I'm using R 1.4.0 on Win 98. I've been trying to figure out the formula interface, and think there's a bug, but not sure how to find it, yet alone fix

Hi, I wonder whether the mechanism of parsing parameters to functions has changed between 0.63.1 and 0.63.3? The following code yeils different results in R 0.63.1 (Version 0.63.1 (Dec 5, 1998)) and R 0.63.3. cave<-function(x,a,b) { return(c(mean(x[a],na.rm=T),mean(x[b],na.rm=T))) } datx <- data.frame(rbind(c(1,2,3,4),c(4,5,6,7)))

Hi, I wonder whether the mechanism of parsing parameters to functions has changed between 0.63.1 and 0.63.3? The following code yeils different results in R 0.63.1 (Version 0.63.1 (Dec 5, 1998)) and R 0.63.3. cave<-function(x,a,b) { return(c(mean(x[a],na.rm=T),mean(x[b],na.rm=T))) } datx <- data.frame(rbind(c(1,2,3,4),c(4,5,6,7)))

Hi, I have noticed that aperm is very slow, and I wondered if there was a way of speeding it up. Let me tell you a bit about the context of my problem, because perhaps I shouldn't be using aperm at all. The context is probabilistic inference in graphical models. One of the most fundamental operations is two compute an element-wise multiplication of two arrays of different sizes, say A and B.

Hello All, I am new to R. I am trying to process this huge data set of matrix containing four columns, say x1, x2, x3, x4 and n number of rows. I want to aggregate the matrix by x1 and perform statistic based on columns x2, x3, x4. I tried aggregate function but it gave me memory allocation error (which I am not surprised), so I ended up performing a for loop based on x1 and

Hi there, Could somebody help me disect this legacy R script I inherited at work, I have two questions: 1. I've tried to upgrade our R version from 1.6.2 (yeah, I know), to R 2.0, but some of the lines in this script are not compatible with R 2.0, could someone help me figure out where the problem is? 2. the jpeg generated (attached) seems to be off on some of the data, is there a better way

Hi, Is this method broken in R? I am using it to find roots of the following function: f(x) = 2.5*exp(-0.5*(2*0.045 - x)) + 2.5*exp(-0.045) + 2.5*exp(-1.5*x) - 100 It is giving an answer of -38.4762403 which is not even close (f(x) = 2.903809e+25 for x=-38.4762403). The answer should be around 0.01-0.1. This function should converge.. Even for a simple function like f(x) = exp(-x) * x, it gives

I have a classification tree model similar to the following (slightly simplified here): > treemod<-tree(y~x) where y is a factor and x is a matrix of numeric predictors. They have dimensions: > length(y) [1] 1163 > dim(x) [1] 1163 75 I?ve evaluated the tree model and am happy with the fit. I also have a matrix of cases that I want to use the tree model to classify. Call it

I'd like to compare two models which were fitted using gls, however I'm having trouble interpreting the results of gls. If any of you could offer me some advice, I'd greatly appreciate it. Short explanation of models: These two models have the same fixed-effects structure (two independent, linear effects), and differ only in that the second model includes a corExp structure for

Hello, How can I label a secondary axis in R? At the moment it's labelled as c(-100,200). Obviously I would like it to be more sensible. Here is the code I am using newx = -100+37.5*((1:9)-1) axis(4,at=newx,labels=(newx+100)/3750) Thanks, Rob -- View this message in context: http://www.nabble.com/Labelling-a-secondary-axis-in-R-tp16807708p16807708.html Sent from the R help mailing list

Hi, I was trying to have R read files faster with readChar(). That was before I noticed that readChar() is not that bad! In any case, below I suggest a few simple changes that will make readChar slightly faster. I followed readChar(useBytes=T), and tried to identify all O(N) operations, where N is the size of the file. The assumption is that for LARGE files we want to avoid any O(N) operations,

I am fitting a regression model with a bs term and then making predictions based on the model. According to some info on the internet at http://www.stat.auckland.ac.nz/~yee/smartpred/DummiesGuide.txt there are some problems with using predict.lm when you have a model with terms such as bs,ns,or poly. However when I used one of the examples they said would illustrate the problems I get virtually

Dear R- helpers Following a draft structure of the R script for which I am facing problem Step 1 x <- of type array with original values y <- of type array with original values Step 2 for (ctr in 1:10) { # my problem here the both x and y still show the original values from step 1 # in spite of making changes to the old values of the arrays x and y in the function function