The following example illustrate use of 3 dimentional array with the
function `apply', useful in doing bootstrap with censored data. 
(since censored data itself is two dim).   -Mai Zhou

> # first create a 3 dim array by paste 2 regular 2x75 matricis, mat1 and mat2
> mat1
   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] 
x1 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09   2.1  2.11  2.12  2.13  2.14
d1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00   1.0  1.00  1.00  1.00  1.00
   [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] 
x1  2.15  2.16  2.17  2.18  2.19   2.2  2.21  2.22  2.23  2.24  2.25  2.26
d1  1.00  1.00  1.00  1.00  1.00   1.0  1.00  1.00  1.00  1.00  1.00  1.00
   [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] 
x1  2.27  2.28  2.29   2.3  2.31  2.32  2.33  2.34  2.35  2.36  2.37  2.38
d1  1.00  1.00  1.00   1.0  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00
   [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] 
x1  2.39   2.4  2.41  2.42  2.43  2.44  2.45  2.46  2.47  2.48  2.49   2.5
d1  1.00   1.0  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00   1.0
   [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] 
x1  2.51  2.52  2.53  2.54  2.55  2.56  2.57  2.58  2.59   2.6  2.61  2.62
d1  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00   1.0  1.00  1.00
   [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] 
x1  2.63  2.64  2.65  2.66  2.67  2.68  2.69   2.7  2.71  2.72  2.73  2.74
d1  1.00  1.00  1.00  1.00  1.00  1.00  1.00   1.0  1.00  1.00  1.00  1.00
   [,75] 
x1  2.75
d1  1.00

> # mat2 looks similar, with same dimentions, but entries are different
> mat2
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] 
[1,] 2.76 2.77 2.78 2.79  2.8 2.81 2.82 2.83 2.84  2.85  2.86  2.87  2.88
[2,] 1.00 0.00 0.00 1.00  0.0 1.00 1.00 1.00 0.00  1.00  1.00  0.00  1.00
............the rest of mat2 omitted 
> dim(mat2)
[1]  2 75
> dim(mat1)
[1]  2 75

> # Now combine mat1 and mat2 to create a 3 dim array
> mat3 <- array( c(mat1, mat2), dim = c(2,75,2) )
> dim(mat3)
[1]  2 75  2
> mat3

, , 1
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] 
[1,] 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09   2.1  2.11  2.12  2.13
[2,] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00   1.0  1.00  1.00  1.00

......... I omit the rest of mat3, but you should try to convince yourself
that you want `apply' to work with the depth, the 3rd dim of mat3.
Because at each fixed depth, mat3 is a matrix of 2 rows and 75 col,
with the first row as observed times and second row as censoring status. 
You can check this by try look at mat3[,,1] and mat3[,,2] 
> # Next wrap the function survfit to take a 2 row matrix as input 
> # the first row as the time and second row as the status 
> mysurvfit<-function( mat ) { 
+ temp<-survfit(Surv(mat[1,],mat[2,]),se.fit=F,type="fleming",conf.type="none")
+ temp$n.event
+ }
> mysurvfit
function(mat)
{
        temp <- survfit(Surv(mat[1,  ], mat[2,  ]), se.fit = F, type = 
                "fleming", conf.type = "none")
        temp$n.event
}

> # I only output the n.event of survfit. (may be temp$median make more sense) 
> # When each output is more then a vector, the output of apply can be 
> # hard to read (eg. an array of many dim) so you want to simplify the output.
> # Test the function
> mysurvfit(mat1)
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
> mysurvfit(mat2)
 [1] 1 0 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0
[39] 0 1 1 1 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1

> # looks OK on regular matrix. Now on to 3 dim array
> apply(mat3, 3, mysurvfit)  # notice I apply on the 3rd dim (depth) of mat3
      [,1] [,2] 
 [1,]    1    1
 [2,]    1    0
 [3,]    1    0
...................... the rest omitted, a matrix of 75 row and 2 col. 
Looks OK too.