Description Usage Arguments Value See Also Examples

View source: R/popkin_A_min_subpops.R

This function averages the values of a square matrix `A`

between every subpopulation pair and returns the minimum of these averages.
If no subpopulation partition is provided, the function returns the minimum value of `A`

excluding the diagonal, to agree when the code treats each individual as a subpopulation.
The return value can be used to adjust an `A`

matrix to yield the kinship matrix.

1 | ```
popkin_A_min_subpops(A, subpops = NULL)
``` |

`A` |
A symmetric |

`subpops` |
A length- |

The minimum of the average between-subpopulation `A`

values, which estimates the minimum expected value of `A`

`popkin_A()`

to generate the `A`

matrix usually inputted into this function (`popkin_A_min_subpops`

).
`popkin()`

is the wrapper function around both of these.

`avg_kinship_subpops()`

for the full matrix of mean kinship values between subpopulations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# Construct toy data
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
# calculate A from genotypes
A <- popkin_A(X)$A
# the recommended form using appropriate subpopulation labels
A_min_est <- popkin_A_min_subpops( A, subpops )
# this recovers the popkin estimate
kinship <- 1 - A / A_min_est
stopifnot( kinship == popkin( X, subpops ) )
# a simple default for exploratory analysis, equals min( A ) for correctly-calculated A
A_min_est <- popkin_A_min_subpops( A )
stopifnot( A_min_est == min( A ) )
``` |

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