arguments: Documentation

Description Arguments See Also

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.