pollinate_ML: Generate seed values for EM algorithm

In emery: Accuracy Statistic Estimation for Imperfect Gold Standards

Generate seed values for EM algorithm

Description

pollinate_ML() is a general helper function which can be used to generate starting values, i.e. seeds, for the estimate_ML() function from a multi-method data set.

Usage

pollinate_ML(type = c("binary", "ordinal", "continuous"), data, ...)

pollinate_ML_binary(data, ...)

pollinate_ML_ordinal(
  data,
  n_level = NULL,
  threshold_level = ceiling(n_level/2),
  level_names = NULL,
  ...
)

pollinate_ML_continuous(
  data,
  prev = 0.5,
  q_seeds = c((1 - prev)/2, 1 - (prev/2)),
  high_pos = TRUE,
  ...
)

Arguments

type	A string specifying the data type of the methods under evaluation.
data	An n_obs by n_method matrix containing the observed values for each method. If the dimensions are named, row names will be used to name each observation (obs_names) and column names will be used to name each measurement method (method_names).
...	Additional arguments
n_level	Used for ordinal methods. Integer number of levels each method contains
threshold_level	Used for ordinal methods. A value from 1 to n_level which indicates the initial threshold used to define positive and negative disease states.
level_names	Used for ordinal methods. Optional vector of length n_level containing names for each level.
prev	A double between 0-1 representing the proportion of positives in the population
q_seeds	Used for continuous methods. A vector of length 2 representing the quantiles at which the two groups are assumed to be centered
high_pos	Used for continuous methods. A logical indicating whether larger values are considered "positive"

Value

a list of EM algorithm initialization values

emery documentation built on June 9, 2025, 5:09 p.m.