"SnpMatrix"
as dependent variable, this function first fits a
"base" logistic regression model and then carries out a score test for
the addition of further term(s). The Hardy-Weinberg
assumption can be relaxed by use of a "robust" option.
snp.lhs.tests(snp.data, base.formula, add.formula, subset, snp.subset, data = sys.parent(), robust = FALSE, uncertain = FALSE, control=glm.test.control(), score=FALSE)
"SnpMatrix"
or "XSnpMatrix"
formula
object describing the base model,
with dependent variable omitted formula
object describing the additional
terms to be tested, also with dependent variable omittedbase.formula
,
add.formula
and subset
are to be evaluatedTRUE
, a test which does not assume
Hardy-Weinberg equilibrium will be used TRUE
, uncertain genotypes are used and
scored by their posterior expectations. Otherwise they are treated
as missing. If set, this option forces robust
variance estimatesglm.test.control
snp.tests.glm
or GlmTests.score
depending on whether score
is set to FALSE
or TRUE
in the call.
data
argument is supplied, the snp.data
and
data
objects are aligned by rowname. Otherwise all variables in
the model formulae are assumed to be stored in the same order as the
columns of the snp.data
object.
GlmTests-class
,
GlmTestsScore-class
,
glm.test.control
,snp.rhs.tests
single.snp.tests
, SnpMatrix-class
,
XSnpMatrix-class
data(testdata)
snp.lhs.tests(Autosomes[,1:10], ~cc, ~region, data=subject.data)
snp.lhs.tests(Autosomes[,1:10], ~strata(region), ~cc,
data=subject.data)
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