Dear Murray,
Here is one way: create a function that takes k sample()s from any vector
(e.g., x) and then calculates the number of NA values in it. Then replicate
the procedure as many times as you want.
# The function
foo <- function(x, k = 5){
xsample <- sample(x, k)
sum( is.na(xsample) )
}
# Vector of data
y <- c(1:10, NA, NA)
# The replication (10 replicates are used)
# replicate(10, foo(y) )
# [1] 1 2 1 1 0 0 1 0 1 1
HTH,
Jorge
On Wed, Jul 1, 2009 at 10:15 PM, Murray Cooper <myrm...@earthlink.net>wrote:
> Dear All,
>
> I am new to R and slowly learning how to use the system.
>
> The following code is an exercise I was trying.
> The intent is to generate 10 random samples of size 5 from
> a vector with integers 1:10 and 2 missing values. I then want
> to generate a matrix, for each sample which shows the frequency
> of missing values (NA) in each sample. My solution, using sapply
> is at the end.
>
> If anyone has the time and/or intrest to critique my method I'd
> be very grateful. I'm especially interested in knowing if there is
> a better way to accomplish this problem.
>
> (x<-replicate(10,sample(c(1:10,rep(NA,2)),5)))
>>
> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
> [1,] 3 NA 3 4 2 10 NA 4 5 4
> [2,] 5 7 7 3 9 2 8 NA 7 9
> [3,] NA 8 1 5 NA 7 10 2 NA 6
> [4,] 2 NA 6 10 8 4 4 7 4 7
> [5,] 7 9 10 8 3 6 1 NA 9 NA
>
>> # Since table will return only a single item of vaule FALSE
>> # if there are no missing values (NA) in a sample, sapply
>> # will return a list and not a matrix.
>> # So to get a matrix, the factor function needs to be used
>> # to identify possible results (FALSE, TRUE) for the table
>> # function.
>> sapply(1:10,function(i) table(factor(is.na(x[,i]),c(FALSE,TRUE))))
>>
> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
> FALSE 4 3 5 5 4 5 4 3 4 4
> TRUE 1 2 0 0 1 0 1 2 1 1
>
>>
>>
> Thanks for your thoughts.
>
> Murray M Cooper, Ph.D.
> Richland Statistics
> 9800 N 24th St
> Richland, MI, USA 49083
> Mail: richs...@earthlink.net
>
> ______________________________________________
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