testLmFisher {rotRPackage}R Documentation

Fisher test for a linear model.

Description

This ROT function, called from a Test C++ object, is given two samples, a scalar and a parameter vector. It predicts the values corresponding to the explanatory variables through the linear model, then computes the Fisher statistic. It is tested against the scalar, then the function returns the result of the test and the Fisher value.

Usage

testLmFisher(x, beta, y, testLevel = 0.95)

Arguments

x A m-by-n matrix containing the explanatory variables.
beta A n-by-1 vector containng the linear model parameters.
y A n-by-1 vector containng the response variables.
testLevel the test level. (scalar in [0:1])

Details

As it is not asked in LinearModel.getPredict(), no prediction interval is returned; it is up to the user to be careful about that. It is also to noted that the sample is not assumed to contain the '1's corresponding to the intercept parameter.

Value

A list is returned, containing two scalars ,

testResult A scalar simulating a boolean (easier for Rserve)
valueFisher A scalar.

Author(s)

Pierre-Matthieu Pair, Softia for EDF.

Examples

set.seed(1)
x <- matrix(runif(40), 10, 4)
r <- matrix(c(1,2,3,4), 4, 1)
y <- x %*% r + matrix(rnorm(10, 0, 0.05), 10, 1)
LM <- computeLinearModel(x, y)
testLmFisher(x, LM$parameterEstimate, y) 

[Package rotRPackage version 1.4.3 Index]