snpStats (version 1.22.0)

chi.squared: Extract test statistics and p-values

Description

Generic functions to extract values from the SNP association test objects returned by various testing functions

Usage

chi.squared(x, df)
deg.freedom(x)
effect.sign(x, simplify)
p.value(x, df)
sample.size(x)
effective.sample.size(x)

Arguments

x

An object of class "SingleSnpTests", "SingleSnpTestsScore", or "GlmTests"

df

Either the numeric value 1 or 2 (not used when x is of class "GlmTests")

simplify

This switch is relevant when x is of class "GlmTests" and plays the same role as it does in sapply. If simplify=TRUE, where possible the output is returned as a simple numeric vector rather than as a list

Value

A numeric vector containing the chi-squared test statistics or p-values. The output vector has a names attribute.

Details

These functions operate on objects created by single.snp.tests, snp.lhs.tests, and snp.lhs.tests.

The functions chi.squared and p.value return the chi-squared statistic and the corresponding p-value. The argument df is only used for output from single.snp.tests, since this function calculates both 1 df and 2 df tests for each SNP. The functions snp.lhs.tests and snp.rhs.tests potentially calculate chi-squared tests on varying degrees of freedom, which can be extracted with deg.freedom. The function effect.sign indicates the direction of associations. When applied to an output object from snp.single.tests, it returns +1 if the association, as measured by the 1 df test, is positive and -1 if the association is negative. Each test calculated by GlmTests are potentially tests of several parameters so that the effect sign can be a vector. Thus effect.sign returns a list of sign vectors unless, if simplify=TRUE, and it can be simplified as a single vector with one sign for each test. The function sample.size returns the number of observations actually used in the test, after exclusions due to missing data have been applied, and effective.sample.size returns the effective sample size which is less than the true sample size for tests on imperfectly imputed SNPs.

See Also

single.snp.tests, snp.lhs.tests, snp.rhs.tests, SingleSnpTests-class, SingleSnpTestsScore-class, GlmTests-class

Examples

data(testdata)
tests <- single.snp.tests(cc, stratum=region, data=subject.data,
   snp.data=Autosomes, snp.subset=1:10)
chi.squared(tests, 1)
p.value(tests, 1)

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