Description Usage Arguments Value See Also Examples

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

, where `inbr_j`

is the inbreeding coefficients of individual `j`

.
This function returns a modified kinship matrix with diagonal values replaced with `inbr_j`

values (off-diagonal `j != k`

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).

1 | ```
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 `\link[bnpsd]{coanc_to_kinship}`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ```
#########
# 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)
``` |

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