mean and sd of each serial position

I think "apply" is what you're looking for ...

apply(rbind(a1,a2,a3),2,mean)
apply(rbind(a1,a2,a3),2,sd)


or making it into a function:

tmpf _ function(...) {
  x _ rbind(...)
  list(mean=apply(x,2,mean),sd=apply(x,2,sd))
}


  

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I want to do something like this in R. If I have three vectors

a1

[1] 1 2 3

a2

[1] 4 5 6

a3

[1] 9 10 7

I want to compute
1. A vector that is the mean at each serial position of a1, a2, and a3.
so in this example it would have the contents
4.667, 5.667, 5.333333

2. A vector that is the SD at each serial position of a1, a2, and a3.
so in this example it would have the contents
 4.041452, 4.041452, 2.081666

Can anyone tell me a good way to do this in R? The only thing I can come
up with is

Use apply():

cbind(a1,a2,a3) makes matrix

apply(cbind(a1,a2,a3),1,mean) computes the mean of each row of the matrix

In general you can do

    meanvec<-function (...) 
     	apply(cbind(...), 1, mean)

and then 
R> a1<-1:10  
R> a2<-2:11
R> a3<-3:12
R> meanvec(a1,a2,a3)
 [1]  2  3  4  5  6  7  8  9 10 11

This doesn't check that all the vectors are the same length. A more
sophisticated version is 
meanvec<-function (...) {
 if (length(unique(sapply(list(...),length)))!=1)
        stop("not all same length")
 apply(cbind(...), 1, mean)
}

R> meanvec(a1,a2,a3)     
 [1]  2  3  4  5  6  7  8  9 10 11
R> a4<-1:3
R> meanvec(a1,a2,a3,a4)
Error: not all same length


Thomas Lumley
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Thanks very much everyone!  Yes apply() is what I needed.
Thomas, thanks very much for your function.

Bill

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"Bill" == Bill Simpson <wsimpson at uwinnipeg.ca> writes:

:    I want to do something like this in R. If I have three vectors
:    > a1
:    [1] 1 2 3
:    > a2
:    [1] 4 5 6
:    > a3
:    [1] 9 10 7
:    
:    I want to compute
:    1. A vector that is the mean at each serial position of a1, a2, and a3.
:    so in this example it would have the contents
:    4.667, 5.667, 5.333333
:    
:    2. A vector that is the SD at each serial position of a1, a2, and a3.
:    so in this example it would have the contents
:     4.041452, 4.041452, 2.081666

These are the "parallel mean" and "parallel standard deviation"
functions that should be part of standard R.  There is already a
"parallel maximum" function, pmax(...), for example.  In fact
there should be quite a few of these available, such as psum,
pprod, pcumsum, pdiff, ..., so if anyone has the time, feel free.

Here are two versions of pmean().  The first should be good
enough if the entire structure fits into memory; the second,
pmean.large() might be better when dealing with truly huge
vectors.  pstddev() is left as an exercise...

"pmean" <- function (..., na.rm = FALSE)  {
  args <- do.call("cbind", list(...))
  nargs <- ncol(args)
  if (na.rm) {
    OK <- !is.na(args)
    args[!OK] <- 0
    n <- as.vector(matrix(as.numeric(OK), nrow(args), nargs) %*% 
                   rep(1, nargs))
  }
  else n <- nargs
  as.vector(args %*% rep(1, nargs))/n
}

"pmean.large" <- function (..., na.rm = FALSE) {
  elts <- list(...)
  k <- max(unlist(lapply(elts, length)))
  total <- rep(as.vector(elts[[1]]), length = k)
  if (na.rm) {
    n <- !is.na(total)
    total[!n] <- 0
  }
  else {
    n <- length(elts)
  }
  for (each in elts[-1]) {
    work <- rep(as.vector(each), length = k)
    if (na.rm) {
      m <- is.na(work)
      work[m] <- 0
      n <- n + !m
    }
    total <- total + work
  }
  total/n
}

_________________________________________________________________
Bill Venables, Head, Dep't of Statistics,   Tel.: +61 8 8303 5418
The University of Adelaide,                 Fax.: +61 8 8303 3696
South AUSTRALIA.     5005.   Email: Bill.Venables at adelaide.edu.au
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Thanks very much for your help, Bill.

Bill

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