Try this.
check.na <- function(mat){
nas <- NULL
for(st in seq.int(ncol(mat)))
if(sum(is.na(mat[, st])) == nrow(mat) - 1) nas <- c(nas, st)
if(length(nas)){
mat <- mat[, -nas]
mat <- mat[-nas, ]
}
mat
}
check.na(df1)
file1 file3
file1 1.0 0.8
file3 0.8 1.0
Note that you must remove both the columns and the rows, it's a correlation
matrix.
And that's also why the 'ncol(mat) minus 1', the diagonal value need not be
NA.
Rui Barradas
jeff6868 wrote
>
> Hello Rui,
>
> Thanks for your answer too.
> I tried your proposition too, but by giving the value 0 for this file, it
> still wants to make a calculation with it. As it is looking for the best
> correlation, and then the 2nd best correlation, giving only 0 seems to be
> a problem for the 2nd best correlation at least.
> Maybe the best way to solve the problem would be to introduce in the
> function get.max.cor a line which would delete all the colums containing
> only NAs in my correlation matrix?
> For example if my calculated correlation matrix is (imagine that the
> numeric values are correlation coefficients for the example):
>
> x <- data.frame(a = 1:10, b = c(1:5,NA,7:9, NA), c = 21:30, d = NA)
>
> Maybe is it possible in my function to delete only columns containing 100%
> of NA, in order to have a matrix like this:
>
> x <- data.frame(a = 1:10, b = c(1:5,NA,7:9, NA), c = 21:30)
>
> and to keep other columns even if there're some NAs (the calculation is
> still possible as they're numeric coefficients in the column).
> Actually, it cannot look for the best or the second best correlation
> coefficient in a column if it contains only NA.
> I think that a correlation matrix like this would allow the calculation
> for the next function and the rest of my script.
>
--
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