plrs (version 1.12.0)

plrs.test: Likelihood ratio test for a plrs model

Description

Test whether copy number has an effect on mRNA expression.

Usage

plrs.test(object, alpha=0.05)

Arguments

object

An object of class plrs-class

alpha

Significance level

Value

list object with the following components:

stat

Test statistic

pvalue

Calculated pvalue

wt.bar

Weights (if the model is constrained)

df.bar

Degrees of freedom.

unconstr

Unconstrained model of class plrs-class

qbetabar

(1-alpha) quantile of the beta mixture distribution

alpha

Significance level

Details

Two cases present themselves:

1. The model is unconstrained. Thus, the model under the null hypothesis is the intercept and an F-test is performed.

2. The model is constrained and the following hypothesis are tested: H0: All constraints are actives (=) H1: At least one constraint is strict (>) Under H0, we always have the intercept model. Indeed, if constr.slopes = 1 (or 2) and constr.intercepts = T, then the only parameter free of inequality constraint is the overall intercept. If constr.intercepts = F, the local intercepts are additionnaly constrained to be 0 in order to obtain the intercept model under the null. The likelihood ratio statistic (unknown variance) is asymptotically distributed as a weighted mixture of Beta distribution (cf Gromping (2010)). Calculation of p-values is based on functions ic.weights and pbetabar of package ic.infer. The package mvtnorm is also involved.

In both cases the input model is taken as the model under the alternative.

References

Gromping, U. (2010). Inference with linear equality and inequality constraints using R: The package ic.infer. J Stat Softw, 33(i10).

Examples

# Simulate data
sim <- plrs.sim(n=80, states=2, sigma=0.5)

# Fit a model 
model <- plrs(expr=sim$expr, cghseg=sim$seg, cghcall=sim$cal)

# Testing
model <- plrs.test(model)
model