Package 'semisup'

Semi-supervised mixture model

Description

This R package implements the semi-supervised mixture model. Use mixtura for model fitting, and scrutor for hypothesis testing.

Getting started

Please type the following commands:
utils::vignette("semisup")
?semisup::mixtura
?semisup::scrutor

More information

A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2020). "Semi-supervised mixture test for detecting markers associated with a quantitative trait", Manuscript in preparation.

[email protected]

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Model fitting

Description

This function fits a semi-supervised mixture model. It simultaneously estimates two mixture components, and assigns the unlabelled observations to these.

Usage

mixtura(y, z, dist = "norm",
        phi = NULL, pi = NULL, gamma = NULL,
        test = NULL, iter = 100, kind = 0.05,
        debug = TRUE, ...)

Arguments

y	observations: numeric vector of length n
z	class labels: integer vector of length n, with entries 0, 1 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
debug	verification of arguments: TRUE or FALSE
...	settings EM algorithm: starts, it.em and epsilon (see arguments)

Details

By default, phi and pi are estimated by the maximum likelihood method, and gamma is replaced by a vector of ones.

Value

This function fits and compares a one-component (H0) and a two-component (H1) mixture model.

posterior	probability of belonging to class 1: numeric vector of length n
converge	path of the log-likelihood: numeric vector with maximum length it.em
estim0	parameter estimates under H0: data frame
estim1	parameter estimates under H1: data frame
loglik0	log-likelihood under H0: numeric
loglik1	log-likelihood under H1: numeric
lrts	likelihood-ratio test statistic: positive numeric
p.value	H0 versus H1: numeric between 0 and 1, or NULL

Reference

A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2020). "Semi-supervised mixture test for detecting markers associated with a quantitative trait", Manuscript in preparation.

See Also

Use scrutor for hypothesis testing. All other functions are internal.

Examples

# data simulation
n <- 100
z <- rep(0:1,each=n/2)
y <- rnorm(n=n,mean=2,sd=1)
z[(n/4):n] <- NA

# model fitting
mixtura(y,z,dist="norm",test="perm")

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Hypothesis testing

Description

This function tests whether the unlabelled observations come from a mixture of two distributions.

Usage

scrutor(Y, Z, dist = "norm",
        phi = NULL, pi = NULL, gamma = NULL,
        test = "perm", iter = NULL, kind = NULL,
        debug = TRUE, ...)

Arguments

Y	observations: numeric vector of length n, or numeric matrix with n rows (samples) and q columns (variables)
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 parameter(s): numeric vector of length q, or NULL (norm: none, nbinom: MLE)
pi	zero-inflation parameter(s): numeric vector of length q, or NULL (norm: none,nbinom: MLE)
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
debug	verification of arguments: TRUE or FALSE
...	settings EM algorithm: starts, it.em and epsilon (see arguments)

Details

By default, phi and pi are estimated by the maximum likelihood method, and gamma is replaced by a vector of ones.

Value

This function tests a one-component (H0) against a two-component mixture model (H1).

y	index observations
z	index class labels
lrts	test statistic
p.value	p-value

Reference

A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2020). "Semi-supervised mixture test for detecting markers associated with a quantitative trait", Manuscript in preparation.

See Also

Use mixtura for model fitting. All other functions are internal.

Examples

# data simulation
n <- 100
z <- rep(0:1,each=n/2)
y <- rnorm(n=n,mean=2*z,sd=1)
z[(n/4):n] <- NA

# hypothesis testing
scrutor(y,z,dist="norm")

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