Hi:

The idea is as follows:
    * string your four matrices into vectors, cbinding them so that the
columns correspond to what you want as the (1,1), (1, 2), (2, 1) and (2, 2)
elements, respectively, of the matrix/table to be used for the Fisher test;
   *  Operate row-wise on the constructed matrix, running fisher.test() in
each row, producing a list of model objects;
  * use the ldply() function in package plyr to pick off pieces of output
(or alternatively, do.call(rbind, <function applied to list>))

Before I give an example, note that an object returned by fisher.test() has
the following components, each of which can be extracted individually; w is
the output object of a single run of fisher.test():
names(w)
[1] "p.value"     "conf.int"    "estimate"    "null.value"  "alternative"
[6] "method"      "data.name"

#-----------------
library(plyr)

# generate some matrices:
m1 <- matrix(rpois(100, 7), nrow = 10)
m2 <- matrix(rpois(100, 7), nrow = 10)
m3 <- matrix(rpois(100, 7), nrow = 10)
m4 <- matrix(rpois(100, 7), nrow = 10)

# I want m1 to represent the (1, 1) elements, m2 the (1, 2) elements,
# m3 the (2, 1) elements and m4 the (2, 2) elements of the table used as
# input to fisher.test. The idea is to string each of the matrices into
vectors
# and then to recombine them with cbind() into a 4-column matrix => each
# row is then ready to be reshaped into a 2 x 2 matrix for input into
fisher.test()

v <- cbind(as.vector(m1), as.vector(m2), as.vector(m3), as.vector(m4))

# Each row of v is reshaped into a 2 x 2 matrix and fisher.test() is
# applied. The result is a list of model objects from fisher.test(), one
component
# for each row of v. [In this example, 100 model objects are generated.]

ll <- vector('list', nrow(v))
for(i in seq_along(nrow(v))) ll[[i]] <- fisher.test(matrix(v[i, ], nrow =
2))

# Now ll can be used to extract individual pieces of output for each run;
e.g.,
# to get all the p-values and confidence intervals,

pvals <- ldply(ll, function(x) x$p.value)
confints <- ldply(ll, function(x) x$conf.int)

The time it takes to generate the data and produce the 1000000 model objects
is:
> system.time( {
+   m1 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m2 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m3 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m4 <- matrix(rpois(1000000, 7), nrow = 1000)
+   v <- cbind(as.vector(m1), as.vector(m2), as.vector(m3), as.vector(m4))
+   ll <- vector('list', nrow(v))
+   for(i in seq_along(nrow(v))) ll[[i]] <- fisher.test(matrix(v[i, ], nrow
= 2))
+
+   } )
   user  system elapsed
   0.43    0.00    0.43
> length(ll)
[1] 1000000

A second run in a fresh R session took 0.73 seconds elapsed.

When running a lot of tests like this, it's important to try out different
alternatives. My initial approach to this problem was to use apply() on the
rows of v. Testing it on my system when the input matrices were 100 x 100,
it took 11.23 seconds, so for the 1000 x 1000 input matrices you're using,
use of apply() would have taken around 20 minutes. In this particular case,
use of a loop to fill in the components of a pre-allocated list object with
the output of fisher.test() was much more efficient; in fact, the actual
data generation and reshaping took up a significant amount of the time!

Data generation and reshaping:
> system.time( {
+   m1 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m2 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m3 <- matrix(rpois(1000000, 7), nrow = 1000)
+   m4 <- matrix(rpois(1000000, 7), nrow = 1000)
+
+   v <- cbind(as.vector(m1), as.vector(m2), as.vector(m3), as.vector(m4))
+   })
   user  system elapsed
   0.27    0.00    0.27

HTH,
Dennis


On Mon, Jan 3, 2011 at 6:57 AM, zhaoxing731 <zhaoxing731@yahoo.com.cn>wrote:

> Hello
>
> I have 4 1000*1000 matrix A,B,C,D. I want to use the corresponding element
> of the 4 matrices. Using the "for loop" as follow:
>
> E<-o
> for (i in 1:1000)
>    {for (j in 1:1000)
>  {
>         E<-fisher.test(matrix(c(A[i][j],B[i][j],C[i][j],D[i][j]),2))#call
> fisher.test for every element
>  }
>    }
>
> It is so time-consuming
> Need vectorization
>
> Yours sincerely
>
>
>
>
> ZhaoXing
> Department of Health Statistics
> West China School of Public Health
> Sichuan University
> No.17 Section 3, South Renmin Road
> Chengdu, Sichuan 610041
> P.R.China
>
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>
>
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>
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