summary.updog: Summary method for class "'updog'".

In dcgerard/updogAlpha: Using Parental Data for Offspring Genotyping

Description

Summary method for class "updog".

Usage

## S3 method for class 'updog'
summary(object, ..., use_discrete_geno = TRUE)

Arguments

object	An updog object. Usually what has been returned from updog.
...	Not used.
use_discrete_geno	How should we get the empirical distribution for the GOF test? By a table of the counts of MAP estimate (TRUE) or by the sum of the posterior probabilities (FALSE)? I am currently just experimenting with this.

Value

A list with three elements:

genotypes: Counts for how many of each estimated genotype are observed.

summ_prob: A data frame with summaries on two variables: The maximum posterior probability of a genotype that each observation has (maxpostprob), and the posterior probability of not being an outlier (prob_ok)

gof_pvalue: A Pearson goodness of fit p-value testing if the empirically estimated genotypes are close in frequency to the theoretical genotype distribution.

Author(s)

David Gerard

dcgerard/updogAlpha documentation built on May 14, 2019, 3:10 a.m.