uni_em

A vector of weights. Each element of <code>weight_vec</code> corresponds
to a column of <code>lmat</code>.

weight_vec

A matrix of inner weights. The columns are the "individuals" and the rows are the "classes."

lmat

The initial values of <code>pivec</code>. Each element of <code>pi_init</code>
corresponds to a row of <code>lmat</code>.

pi_init

The penalty on the pi's. Should be greater than 0 and really really small.

lambda

The maximum number of EM iterations to take.

itermax

obj_tol

Solves the following optimization problem
$$\max_{\pi} \sum_k w_k \log(\sum_j \pi_j \ell_jk).$$
It does this using a weighted EM algorithm.

internal

Implements empirical Bayes approaches to genotype
polyploids from next generation sequencing data while
accounting for allelic bias, overdispersion, and sequencing
error. The main function is flexdog(), which allows the specification
of many different genotype distributions. An experimental
function that takes into account varying levels of relatedness
is implemented in mupdog(). Also provided are functions to
simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
functions to calculate oracle genotyping error rates, oracle_mis(), and
correlation with the true genotypes, oracle_cor(). These latter two
functions are useful for read depth calculations. Run
browseVignettes(package = "updog") in R for example usage. See
Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
Gerard and Ferrao (2019) <doi:10.1093/bioinformatics/btz852> for details
on the implemented methods.

uni_em: EM algorithm to fit weighted ash objective.

Description

Usage

Arguments

Value