Description Usage Arguments Details Value Note Author(s) See Also Examples
This function fits a generalized linear model with phenotype as dependent variable and with a series of SNPs (or small sets of SNPs) as predictor variables. Optionally, one or more potential confounders of a phenotypegenotype association may be included in the model. In order to protect against misspecification of the variance function, "robust" estimates of the variancecovariance matrix of estimates may be calculated in place of the usual modelbased estimates.
1 2 3 4  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())

formula 
The model formula, with phenotype as dependent variable and any potential confounders as independent variables. Note that parameter estimates are not returned for these model terms 
family 
A string defining the generalized linear model
family. This currently should (partially) match one of

link 
A string defining the link function for the GLM. This
currently should (partially) match one of 
data 
The dataframe in which the model formula is to be interpreted 
snp.data 
An object of class 
rules 
Optionally, an object of class 
sets 
Either a vector of SNP names (or numbers) for the SNPs
to be added to the model formula, or a logical vector of length
equal to the number of columns in 
weights 
"Prior" weights in the generalized linear model 
subset 
Array defining the subset of rows of 
robust 
If 
uncertain 
If 
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 
Homozygous SNP genotypes are coded 0 or 2 and heterozygous genotypes are coded 1. For SNPs on the X chromosome, males are coded as homozygous females. For X SNPs, it will often be appropriate to include sex of subject in the base model (this is not done automatically). The "robust" option causes HuberWhite estimates of the variancecovariance matrix of the parameter estimates to be returned. These protect against misspecification of the variance function in the GLM, for example if binary or count data are overdispersed,
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.
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.
An object of class GlmEstimates
A factor (or
several factors) may be included as arguments to the function
strata(...)
in the 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 parameter
estimates is used.
If uncertain genotypes (e.g. as a result of imputation) are used, the interpretation of the regression coefficients is questionable; the regression coefficient for an imperfectly measurement of a variable is not a biased (attenuated) estimate of the coefficient of the variable measured.
David Clayton [email protected]
GlmEstimatesclass
,
snp.lhs.estimates
,
snp.rhs.tests
,
SnpMatrixclass
, XSnpMatrixclass
1 2 3 4 5 6 7 8 9 10  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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