A function that takes PMR observations, and (given a prior distribution for degrees of relatedness) returns the posterior probabilities of all pairs of individuals being (a) the same individual/twins, (b) first-degree related, (c) second-degree related or (d) "unrelated" (third-degree or higher). The highest posterior probability degree of relatedness is also returned as a hard classification. Options include setting the background relatedness (or using the sample median), a minimum number of overlapping SNPs if one uses the sample median for background relatedness, and a minimum number of overlapping SNPs for including pairs in the analysis.

## Usage

```
callRelatedness(
pmr_tibble,
class_prior = rep(0.25, 4),
average_relatedness = NULL,
median_co = 500,
filter_n = 1
)
```

## Arguments

- pmr_tibble
a tibble that is the output of the processEigenstrat function.

- class_prior
the prior probabilities for same/twin, 1st-degree, 2nd-degree, unrelated, respectively.

- average_relatedness
a single numeric value, or a vector of numeric values, to use as the average background relatedness. If NULL, the sample median is used.

- median_co
if average_relatedness is left NULL, then the minimum cutoff for the number of overlapping snps to be included in the median calculation is 500.

- filter_n
the minimum number of overlapping SNPs for which pairs are removed from the entire analysis. If NULL, default is 1.

## Value

results_tibble: A tibble containing 13 columns:

row: The row number

pair: the pair of individuals that are compared.

relationship: the highest posterior probability estimate of the degree of relatedness.

pmr: the pairwise mismatch rate (mismatch/nsnps).

sd: the estimated standard deviation of the pmr.

mismatch: the number of sites which did not match for each pair.

nsnps: the number of overlapping snps that were compared for each pair.

ave_re;: the value for the background relatedness used for normalisation.

Same_Twins: the posterior probability associated with a same individual/twins classification.

First_Degree: the posterior probability associated with a first-degree classification.

Second_Degree: the posterior probability associated with a second-degree classification.

Unrelated: the posterior probability associated with an unrelated classification.

BF: A strength of confidence in the Bayes Factor associated with the highest posterior probability classification compared to the 2nd highest. (No longer included)

## Examples

```
callRelatedness(counts_example,
class_prior=rep(0.25,4),
average_relatedness=NULL,
median_co=5e2,filter_n=1
)
#> # A tibble: 15 × 12
#> row pair relationship pmr sd mismatch nsnps ave_rel Same_Twins
#> <int> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Ind1 - In… Unrelated 0.204 0.0103 310 1518 0.218 6.71e- 26
#> 2 2 Ind1 - In… Unrelated 0.222 0.00428 2093 9435 0.218 1.22e-214
#> 3 3 Ind1 - In… Unrelated 0.224 0.00458 1854 8283 0.218 2.00e-194
#> 4 4 Ind1 - In… Unrelated 0.235 0.0116 314 1336 0.218 2.68e- 37
#> 5 5 Ind1 - In… Unrelated 0.215 0.00867 481 2242 0.218 9.82e- 46
#> 6 6 Ind2 - In… Unrelated 0.218 0.00432 1988 9119 0.218 5.06e-195
#> 7 7 Ind2 - In… Unrelated 0.213 0.00458 1699 7984 0.218 4.95e-156
#> 8 8 Ind2 - In… Unrelated 0.229 0.0122 270 1179 0.218 1.80e- 30
#> 9 9 Ind2 - In… Unrelated 0.215 0.00927 423 1965 0.218 1.10e- 40
#> 10 10 Ind3 - In… Same_Twins 0.108 0.00214 2253 20952 0.218 1 e+ 0
#> 11 11 Ind3 - In… Unrelated 0.213 0.00458 1703 7994 0.218 6.83e-157
#> 12 12 Ind3 - In… Unrelated 0.218 0.00394 2398 10994 0.218 2.05e-235
#> 13 13 Ind4 - In… Unrelated 0.210 0.00489 1451 6924 0.218 1.92e-127
#> 14 14 Ind4 - In… Unrelated 0.220 0.00419 2141 9745 0.218 2.95e-214
#> 15 15 Ind5 - In… Unrelated 0.220 0.00994 383 1739 0.218 3.64e- 39
#> # ℹ 3 more variables: First_Degree <dbl>, Second_Degree <dbl>, Unrelated <dbl>
```