R help - Jun 2004 - Developing functions

Without trying to understand your code in detail let me just 
assume you are trying to create a matrix, ret, whose i,j-th 
entry is some function, f, of row i of X and row j of X.

In that case this should do it:

	apply(X,1,function(x)apply(X,1,function(y)f(x,y)))


Date:   Wed, 30 Jun 2004 15:28:47 -0300 (ART) 
From:   <daniel at sintesys.com.ar>
To:   <r-help at stat.math.ethz.ch> 
Subject:   [R] Developing functions 

 
Hi,
I伌m new in R. I伌m working with similarity coefficients for clustering
items. I created one function (coef), to calculate the coefficients from
two pairs of vectors and then, as an example, the function
simple_matching,
taking a data.frame(X) and using coef in a for cicle.
It works, but I believe it is a bad way to do so (I believe the for cicle
is not necessary). Somebody can suggest anything better.
Thanks
Daniel Rozengardt

coef<-function(x1,x2){a<-sum(ifelse(x1==1&x2==1,1,0));
b<-sum(ifelse(x1==1&x2==0,1,0));
c<-sum(ifelse(x1==0&x2==1,1,0));
d<-sum(ifelse(x1==0&x2==0,1,0));
ret<-cbind(a,b,c,d);
ret
}

simple_matching<-function(X) {
ret<-matrix(ncol=dim(X)[1],nrow=dim(X)[1]);
diag(ret)<-1;
for (i in 2:length(X[,1])) {
     for (j in i:length(X[,1])) {
     vec<-coef(X[i-1,],X[j,]);
     result<-(vec[1]+vec[3])/sum(vec);
     ret[i-1,j]<-result;
     ret[j,i-1]<-result}};
ret}

> From: daniel at sintesys.com.ar
> 
> Hi,
> I??m new in R. I??m working with similarity coefficients for clustering
> items. I created one function (coef), to calculate the 
> coefficients from
> two pairs of vectors and then, as an example, the function
> simple_matching,
> taking a data.frame(X) and using coef in a for cicle.
> It works, but I believe it is a bad way to do so (I believe 
> the for cicle
> is not necessary). Somebody can suggest anything better.
> Thanks
> Daniel Rozengardt
> 
> coef<-function(x1,x2){a<-sum(ifelse(x1==1&x2==1,1,0));
> b<-sum(ifelse(x1==1&x2==0,1,0));
> c<-sum(ifelse(x1==0&x2==1,1,0));
> d<-sum(ifelse(x1==0&x2==0,1,0));
> ret<-cbind(a,b,c,d);
> ret
> }
> 
> simple_matching<-function(X) {
> ret<-matrix(ncol=dim(X)[1],nrow=dim(X)[1]);
> diag(ret)<-1;
> for (i in 2:length(X[,1])) {
> 	for (j in i:length(X[,1])) {
> 	vec<-coef(X[i-1,],X[j,]);
> 	result<-(vec[1]+vec[3])/sum(vec);
> 	ret[i-1,j]<-result;
> 	ret[j,i-1]<-result}};
> ret}
A few comments first:

1. Unless you are putting multiple statements on the same line, there's no
need to use ";".

2. In `coef' (which is a bad choice for a function name: There's a
built-in
generic function by that name in R, for extracting coefficients from fitted
model objects), a, b, c and d are scalars.  You don't need to cbind() them;
c() works just fine.

3. One of the best strategies for efficiency is to vectorize.  Try to
formulate the problem in matrix/vector operations as much as possible.

4. The computation looks a bit odd to me.  Assuming the data are binary
(i.e., all 0s and 1s), you are computing (N11 + N01) / N, where N is the
length of the vectors, N11 is the number of 1-1 matches and N01 is the
number of 0-1 matches.  Are you sure that's what you want to compute?

Here's what I'd do (assuming the input matrix contains all 0s and 1s):

simple_matching <- function(X) {
    N11 <- crossprod(t(X))
    N01 <- crossprod(t(X), t(1-X))
    ans <- (N11 + N01) / ncol(X)
    diag(ans) <- 1
    ans
}

HTH,
Andy