arguments: Documentation

In rauschenberger/semisup: Semi-Supervised Mixture Model

Description

This page lists and describes all arguments of the R package semisup.

Arguments

y	observations: numeric vector of length n
Y	observations: numeric vector of length n, or numeric matrix with n rows (samples) and q columns (variables)
z	class labels: integer vector of length n, with entries 0, 1 and NA
Z	class labels: numeric vector of length n, or numeric matrix with n rows (samples) and p columns (variables), with entries 0 and NA
dist	distributional assumption: character "norm" (Gaussian), "nbinom" (negative bionomial), or "zinb" (zero-inflated negative binomial)
phi	dispersion parameters: numeric vector of length q, or NULL
pi	zero-inflation parameter(s): numeric vector of length q, or NULL
gamma	offset: numeric vector of length n, or NULL
test	resampling procedure: character "perm" (permutation) or "boot" (parametric bootstrap), or NULL
iter	(maximum) number of resampling iterations : positive integer, or NULL
kind	resampling accuracy: numeric between 0 and 1, or NULL; all p-values above kind are approximate
starts	restarts of the EM algorithm: positive integer (defaults to 1)
it.em	(maximum) number of iterations in the EM algorithm: positive integer (defaults to 100)
epsilon	convergence criterion for the EM algorithm: non-negative numeric (defaults to 1e-04)
debug	verification of arguments: TRUE or FALSE
pass	parameters for parametric bootstrap algorithm
...	settings EM algorithm: starts, it.em and epsilon (see arguments)

See Also

Use mixtura for model fitting, and scrutor for hypothesis testing. All other functions of the R package semisup are internal.

rauschenberger/semisup documentation built on May 11, 2020, 4 p.m.