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.
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,
...
)
a list of EM algorithm initialization values
A string specifying the data type of the methods under evaluation.
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
Used for ordinal methods. Integer number of levels each method contains
Used for ordinal methods. A value from 1 to n_level which
indicates the initial threshold used to define positive and negative disease states.
Used for ordinal methods. Optional vector of length n_level
containing names for each level.
A double between 0-1 representing the proportion of positives in the population
Used for continuous methods. A vector of length 2 representing the quantiles at which the two groups are assumed to be centered
Used for continuous methods. A logical indicating whether larger values are considered "positive"