Identify rows with a minimum required proportion of non-missing values

Helper function to identify rows in x which have a proportion of non-missing values (i.e. valid measurements) larger than prop. Parameter f allows to define groups of columns and hence to perform the test separately in each group. Parameter condiction allows to combine these per-group tests to a single logical value per row, i.e. the default condition = any returns TRUE if in at least one group defined by f the proportion of non-missing values is > prop. With condition = all more than prop non-missing values have to be present in all groups.

moreAreValidThan(x, f = rep(1, ncol(x)), prop = 0.3, condition = any)

Arguments

x	numeric matrix.
f	optional factor defining sample groups (columns) of x.
prop	numeric(1) defining the minimal required proportion of non-missing values. Has to be a value between 0 and 1.
condition	optional function to be used as condition. With condition = any TRUE is reported for all rows in which at least prop values are non-NA within any of the sample groups. With condition = all this has to be the case for all sample groups.

Value

logical vector, same length than nrow(x), TRUE for rows that pass the criteria.

Author

Johannes Rainer

Examples

x <- rbind(
    c(NA, 3, 4, 1, 3, NA, 4, NA),
    c(4, 2, 3, 4, 5, 5, 2, NA),
    c(NA, NA, NA, NA, NA, 3, 4, 5))

## which rows have more than 50% non-missing values
moreAreValidThan(x, prop = 0.5)
#> [1]  TRUE  TRUE FALSE

## Same but with a grouping of columns
moreAreValidThan(x, prop = 0.5, f = c(1, 1, 1, 1, 1, 2, 2, 2))
#> [1] TRUE TRUE TRUE

## Same, but require it to be true in all groups
moreAreValidThan(x, prop = 0.5, f = c(1, 1, 1, 1, 1, 2, 2, 2), condition = all)
#> [1] FALSE  TRUE FALSE