snp.rhs.estimates(formula, family = "binomial", link, weights, subset,
data = parent.frame(), snp.data, rules = NULL, sets = NULL, robust = FALSE, uncertain = FALSE, control
= glm.test.control())
"binomial"
, "Poisson"
, "Gaussian"
or
"gamma"
(case-insensitive)"logit"
,
"log"
, "identity"
or "inverse"
. The
default action is to use the "canonical" link for the family selected"SnpMatrix"
or
"XSnpMatrix"
containing the SNP data"ImputationRules"
snp.data
or a list of short vectors
defining sets of SNPs to be included (see Details
)data
to useTRUE
, robust tests will be carried outTRUE
, uncertain genotypes are used and
scored by their posterior expectations. Otherwise they are treated
as missingglm.test.control
GlmEstimates
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. Usually SNPs to be fitted in models will be referenced by name. However,
they can
also be referenced by number, indicating the
appropriate column in the input snp.data
. They can also
be referenced by a logical selection vector of length equal to the
number of columns in snp.data
.
If the rules
argument is supplied, SNPs may be imputed using
these rules and included in the model.
GlmEstimates-class
,
snp.lhs.estimates
,
snp.rhs.tests
,
SnpMatrix-class
, XSnpMatrix-class
data(testdata)
test <- snp.rhs.estimates(cc~strata(region), family="binomial",
data=subject.data, snp.data= Autosomes, sets=1:10)
print(test)
test2 <- snp.rhs.estimates(cc~region+sex, family="binomial",
data=subject.data, snp.data= Autosomes, sets=1:10)
print(test2)
test.robust <- snp.rhs.estimates(cc~strata(region), family="binomial",
data=subject.data, snp.data= Autosomes, sets=1:10, robust=TRUE)
print(test.robust)
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