Forms kValue matrix from list of kinship matrices

A kValue matrix has one row for each pair of individuals in the kinship matrix and one column for each kinship matrix. A kValue matrix has one row for each pair of individuals in the kinship matrix and one column for each kinship matrix. Thus, in a kinship matrix with 20 individuals the kinship matrix will have 20 rows by 20 columns but only the upper or lower triangle has unique information as the diagonal values are by definition all 1.0 and the upper triangle has the same values as the lower triangle. The kValue table will have 210 rows. The calculation for the number or row in the kValue table is \(20 + (20 * 19) / 2\) rows with the 20 values from the kinship coeficient matrix diagonal and \((20 * 19) / 2\) elements from one of either of the two triangles.

Usage

kinshipMatricesToKValues(kinshipMatrices)

Arguments

kinshipMatrices

list of square matrices of kinship values. May or may not have named rows and columns.

Value

Dataframe object with columns id_1, id_2, and one kinship column for each kinship matrix in kinshipMatricies where the first two columns contain the IDs of the individuals in the kinship matrix provided to the function and the kinship columms contain the corresponding kinship coefficients. In contrast to the kinship matrix. Each possible pairing of IDs appears once.

Details

The kValue matrix for 1 kinship matrix for 20 individuals will have 210 rows and 3 columns. The first two columns are dedicated to the ID pairs and the third column contains the pair's kinship coefficient.

Thus, the number of rows in the kValues matrix will be \(n + n(n-1) / 2\) and the number of columns will be 2 plus one additional column for each kinship matrix (\(2 + n\)).

Examples

library(nprcgenekeepr)
ped <- nprcgenekeepr::smallPed
simParent_1 <- list(id = "A",
                    sires = c("s1_1", "s1_2", "s1_3"),
                    dams = c("d1_1", "d1_2", "d1_3", "d1_4"))
simParent_2 <- list(id = "B",
                    sires = c("s1_1", "s1_2", "s1_3"),
                    dams = c("d1_1", "d1_2", "d1_3", "d1_4"))
simParent_3 <- list(id = "E",
                    sires = c("A", "C", "s1_1"),
                    dams = c("d3_1", "B"))
simParent_4 <- list(id = "J",
                    sires = c("A", "C", "s1_1"),
                    dams = c("d3_1", "B"))
simParent_5 <- list(id = "K",
                    sires = c("A", "C", "s1_1"),
                    dams = c("d3_1", "B"))
simParent_6 <- list(id = "N",
                    sires = c("A", "C", "s1_1"),
                    dams = c("d3_1", "B"))
allSimParents <- list(simParent_1, simParent_2, simParent_3,
                      simParent_4, simParent_5, simParent_6)

extractKinship <- function(simKinships, id1, id2, simulation) {
  ids <- dimnames(simKinships[[simulation]])[[1]]
  simKinships[[simulation]][seq_along(ids)[ids == id1],
                            seq_along(ids)[ids == id2]]
}

extractKValue <- function(kValue, id1, id2, simulation) {
  kValue[kValue$id_1 ==  id1 & kValue$id_2 == id2, paste0("sim_",
         simulation)]
}

n <- 10
simKinships <- createSimKinships(ped, allSimParents, pop = ped$id, n = n)
kValue <- kinshipMatricesToKValues(simKinships)
extractKValue(kValue, id1 = "A", id2 = "F", simulation = 1:n)
#>  [1] "sim_1"  "sim_2"  "sim_3"  "sim_4"  "sim_5"  "sim_6"  "sim_7"  "sim_8" 
#>  [9] "sim_9"  "sim_10"