Iteratively find minimum set of samples by shuffling rows

Because rpv_indices() is deterministic, it may not present the minimum set that represents all variables. This procedure automates the process of randomly sampling the rows in the incoming matrix without replacement to find a minimum set.

rpv_find(tab, n = 10, sort = TRUE, cut = FALSE, progress = TRUE)

Arguments

tab	a numeric matrix with rownames
n	the number of permutations to perform
sort	when TRUE (default), the returned list will be sorted in order of number of samples.
cut	when TRUE, only the results with the minimum number of samples will be returned.
progress	when TRUE, a progress bar will be displayed.

Value

a list of character vectors

Examples

data(monilinia)
# Iterate over the data 100 times and return only the minimum values
set.seed(2018)
rpv_find(monilinia, n = 100, cut = TRUE, progress = FALSE)
#> [[1]]
#>  [1] "A233" "A610" "A154" "A603" "A666" "A163" "A293" "A339" "A590" "A071"
#> [11] "A085" "A218" "A269" "A074" "A182" "A417" "A681" "A176" "A366" "A489"
#> [21] "A216" "A172" "A488" "A406" "A390" "A039" "A010" "A016" "A692" "A129"
#> 

# This is a random process and will not always return the same values
set.seed(201)
rpv_find(monilinia, n = 100, cut = TRUE, progress = FALSE)
#> [[1]]
#>  [1] "A233" "A610" "A154" "A603" "A666" "A163" "A293" "A339" "A590" "A071"
#> [11] "A085" "A218" "A269" "A074" "A182" "A417" "A681" "A176" "A366" "A522"
#> [21] "A216" "A172" "A488" "A408" "A390" "A547" "A385" "A303" "A523" "A571"
#> [31] "A642"
#> 
#> [[2]]
#>  [1] "A233" "A610" "A154" "A603" "A666" "A163" "A293" "A339" "A590" "A071"
#> [11] "A085" "A218" "A269" "A074" "A182" "A417" "A681" "A176" "A366" "A522"
#> [21] "A191" "A172" "A488" "A408" "A390" "A547" "A387" "A016" "A034" "A129"
#> [31] "A165"
#>