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Find informative individuals

Source: R/is_informative.R
is_informative.Rd

Select the ids of the informative individuals.

Usage

# S4 method for class 'character_OR_integer'
is_informative(obj, avail, affected, informative = "AvAf")

# S4 method for class 'Ped'
is_informative(obj, informative = "AvAf", reset = FALSE)

# S4 method for class 'Pedigree'
is_informative(obj, col_aff = NULL, informative = "AvAf", reset = FALSE)

Arguments

obj

A character vector with the id of the individuals or a data.frame with all the informations in corresponding columns.

avail

A logical vector with the availability status of the individuals (i.e. FALSE = not available, TRUE = available, NA = unknown).

affected

A logical vector with the affection status of the individuals (i.e. FALSE = unaffected, TRUE = affected, NA = unknown).

informative

Informative individuals selection can take 5 values:

  • 'AvAf' (available and affected),

  • 'AvOrAf' (available or affected),

  • 'Av' (available only),

  • 'Af' (affected only),

  • 'All' (all individuals)

  • A numeric/character vector of individuals id

  • A boolean

reset

If TRUE, the isinf slot is reset

col_aff

A character vector with the name of the column to be used for the affection status.

Value

When obj is a vector

A vector of individuals informative identifiers.

When obj is a Pedigree

The Pedigree object with its isinf slot updated.

Details

Depending on the informative parameter, the function will extract the ids of the informative individuals. In the case of a numeric vector, the function will return the same vector. In the case of a boolean, the function will return the ids of the individuals if TRUE, NA otherwise. In the case of a string, the function will return the ids of the corresponding informative individuals based on the avail and affected columns.

Examples

is_informative(c("A", "B", "C", "D", "E"), informative = c("A", "B"))
#> [1] "A" "B"
is_informative(c("A", "B", "C", "D", "E"), informative = c(1, 2))
#> [1] "A" "B"
is_informative(c("A", "B", "C", "D", "E"), informative = c("A", "B"))
#> [1] "A" "B"
is_informative(c("A", "B", "C", "D", "E"), avail = c(1, 0, 0, 1, 1),
    affected = c(0, 1, 0, 1, 1), informative = "AvAf")
#> [1] "D" "E"
is_informative(c("A", "B", "C", "D", "E"), avail = c(1, 0, 0, 1, 1),
    affected = c(0, 1, 0, 1, 1), informative = "AvOrAf")
#> [1] "A" "B" "D" "E"
is_informative(c("A", "B", "C", "D", "E"),
    informative = c(TRUE, FALSE, TRUE, FALSE, TRUE))
#> [1] "A" "C" "E"

data("sampleped")
ped <- Pedigree(sampleped)
ped <- is_informative(ped, col_aff = "affection_mods")
isinf(ped(ped))
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [13] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [25] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

data("sampleped")
ped <- Pedigree(sampleped)
ped <- is_informative(ped, col_aff = "affection_mods")
isinf(ped(ped))
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> [13] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [25] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE
#> [49] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE