# Extracts a dataframe with a row for each kinship coeficient in the kinship matrix 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. ```r kinshipMatrixToKValues(kinshipMatrix) ``` ## Arguments - `kinshipMatrix`: square kinship matrix. May or may not have named rows and columns. ## Returns data.frame object with columns `id_1`, `id_2`, and `kinship` where the first two columns contain the IDs of the individuals in the kinship matrix provided to the function and the `kinship` columm contains the corresponding kinship coefficient. 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 3. ## Examples ```r 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) ] } simPed <- makeSimPed(ped, allSimParents) simKinship <- kinship( simPed$id, simPed$sire, simPed$dam, simPed$gen ) kValues <- kinshipMatrixToKValues(simKinship) ```

kinshipMatrixToKValues function