inbr_diag | R Documentation |

The usual kinship matrix contains self-kinship values along their diagonal given by `diag(kinship) = ( 1 + inbr ) / 2`

, where `inbr`

is the vector of inbreeding coefficient.
This function returns a modified kinship matrix with diagonal values replaced with `inbr`

(off-diagonal values stay the same).
The resulting matrix is better for visualization, but is often not appropriate for modeling (e.g. in mixed-effects models for association or heritability estimation).

inbr_diag(kinship)

`kinship` |
A kinship matrix with self-kinship values along the diagonal.
Can pass multiple kinship matrices contained in a list.
If |

The modified kinship matrix, with inbreeding coefficients along the diagonal, preserving column and row names.
If the input was a list of kinship matrices, the output is the corresponding list of transformed matrices.
`NULL`

inputs are preserved without causing errors.

The inverse function is given by `bnpsd::coanc_to_kinship()`

.

`inbr()`

to extract the vector of inbreeding values from a kinship matrix.

######### # illustrate the main transformation on a 2x2 kinship matrix: # same inbreeding values for both individuals inbr <- 0.2 # corresponding self kinship (diagonal values) for both individuals kinship_self <- (1 + inbr)/2 # kinship between the two individuals kinship_between <- 0.1 # actual kinship matrix kinship <- matrix(c(kinship_self, kinship_between, kinship_between, kinship_self), nrow=2) # expected output of inbr_diag (replaces self kinship with inbreeding) kinship_inbr_diag_exp <- matrix(c(inbr, kinship_between, kinship_between, inbr), nrow=2) # actual output from this function kinship_inbr_diag_obs <- inbr_diag(kinship) # verify that they match (up to machine precision) stopifnot( all( abs(kinship_inbr_diag_obs - kinship_inbr_diag_exp) < .Machine$double.eps ) ) # for a list of matrices, returns list of transformed matrices: inbr_diag( list(kinship, kinship) ) # a list with NULL values also works inbr_diag( list(kinship, NULL, kinship) ) ######### # Construct toy data (to more closely resemble real data analysis) X <- matrix(c(0,1,2,1,0,1,1,0,2), nrow=3, byrow=TRUE) # genotype matrix subpops <- c(1,1,2) # subpopulation assignments for individuals # NOTE: for BED-formatted input, use BEDMatrix! # "file" is path to BED file (excluding .bed extension) ## library(BEDMatrix) ## X <- BEDMatrix(file) # load genotype matrix object # estimate the kinship matrix from the genotypes "X"! kinship <- popkin(X, subpops) # calculate kinship from X and optional subpop labels # lastly, replace diagonal of kinship matrix with inbreeding coefficients kinship_inbr_diag <- inbr_diag(kinship)

popkin documentation built on Jan. 7, 2023, 1:26 a.m.

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