snp.lhs.tests {snpMatrix} | R Documentation |
Under the assumption of Hardy-Weinberg equilibrium, a SNP genotype is
a binomial variate with two trials for an autosomal SNP or with one or
two trials (depending on sex) for a SNP on the X chromosome.
With each SNP in an input
"snp.matrix"
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, control=glm.test.control(maxit=20, epsilon=1.e-4,R2Max=0.98), score=FALSE)
snp.data |
The SNP data, as an object of class
"snp.matrix" or "X.snp.matrix" |
base.formula |
A formula object describing the base model,
with dependent variable omitted |
add.formula |
A formula object describing the additional
terms to be tested, also with dependent variable omitted |
subset |
An array describing the subset of observations to be considered |
snp.subset |
An array describing the subset of SNPs to be considered. Default action is to test all SNPs. |
data |
The data frame in which base.formula ,
add.formula and subset are to be evaluated |
robust |
If TRUE , a test which does not assume
Hardy-Weinberg equilibrium will be used |
control |
An object giving parameters for the IRLS algorithm fitting of the base model and for the acceptable aliasing amongst new terms to be tested. See code{glm.test.control} |
score |
Is extended score information to be returned? |
The tests used are asymptotic chi-squared tests based on the vector of
first and second derivatives of the log-likelihood with respect to the
parameters of the additional model. The "robust" form is a generalized
score test in the sense discussed by Boos(1992).
If a 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.
An object of class snp.tests.glm
or snp.tests.glm.score
depending on whether score
is set to FALSE
or TRUE
in the call.
A factor (or
several factors) may be included as arguments to the function
strata(...)
in the base.formula
. This fits all
interactions of the factors so included, but leads to faster
computation than fitting these in the normal way. Additionally, a
cluster(...)
call may be included in the base model
formula. This identifies clusters of potentially correlated
observations (e.g. for members of the same family); in this case, an
appropriate robust estimate of the variance of the score test is used.
David Clayton david.clayton@cimr.cam.ac.uk
Boos, Dennis D. (1992) On generalized score tests. The American Statistician, 46:327-333.
snp.tests.glm-class
,
snp.tests.glm.score-class
,
glm.test.control
,snp.rhs.tests
single.snp.tests
, snp.matrix-class
,
X.snp.matrix-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)