hedrick {Biodem}R Documentation

Calculates the Hedrick standardized kinship coefficient


“hedrick”calculates the Hedrick standardized kinship coefficient starting from surname frequencies.





is a surname frequency table where the N rows correspond to the surnames present in the whole population and the M columns are the subpopulations


The use of “hedrick” could be problematic, because different people are likely to arrange isonymy data in different ways on their computers. We decided for a matrix format for the isonymy data; the function would originally accept data in a different format and then convert it internally, but this would be a problem for people with data arranged in a different format. In the end we decided to write a specific function, "sur.freq", to generate surname frequency tables directly from raw marriage data or marriage-like data (the most commonly used sources in bio-demographic studies). For other types of surname data, see the verbose explanation in the info for the dataset "surnames" so it would be clear for the user how “hedrick” works.


Returns a square symmetric standardized kinship matrix.


The Hedrick index was originally conceived as a measure of the probability of genotypic identity between (sub)populations and uses a standardization analogous to that employed when calculating a correlation coefficient. As a consequence, it is equal to 1 if measured on populations with identical surname distribution.


Federico C. F. Calboli and Alessio Boattini alessio.boattini2@unibo.it


Hedrick, P. W. 1971. A new approach to measuring genetic similarity. Evolution 25: 276-280. Weiss, V. 1980. Inbreeding and genetic distance between hierarchically structured populations measured by surname frequencies. Mankind Quarterly 21: 135-149

See Also

sur.freq to generate the input surname frequency table from marriage data, surnames for an explanation on how to generate the correct input table from other surname sources, laskerand uri for other types of inter-population kinship matrices


# starting from a raw marriage records dataset:
tot <- sur.freq(valley,valley$PAR,valley$SURM,valley$SURF)
tot # a frequency table calculated above all the surnames
hed.kin <- hedrick(tot)
hed.kin # a standardized kinship matrix

#starting from a generic surname frequency table
surnames #a made-up dataset
# you can see that the surnames are arranged as the _rows_ and
# the populations are the _columns_
# the use of the function "hedrick" just turns this data into a kinship matrix
hed.kin <- hedrick(surnames)

[Package Biodem version 0.5 Index]