luca(pen.model, gLabel, dat, HWP = FALSE, dep.model = NULL)
pen.model
has the form d ~ g + a + g:a
pen.model
.pen.model
,
currently, with no default value. Each row of dat
is
considered as one multivariate observation for a subject. Note that the
genetic term must be a
TRUE
, the genetic term must be a genotype
object.NULL
(default),
it indicates independence between the genetic factor and nonge"luca"
with the following components:covmod.XX
) and the penetrance model
(labelled as penmod.YY
where YY
denotes the name of a term
in the model).
The covariate model parameters depend on the covariate assumptions and are
1) control-population log-odds for each level of the genetic
factor relative to a baseline level under independence,
2) control-population log-odds for each allele relative to a baseline allele
under independence plus HWP, or
3) the parameters from the polychotomous regression model under
dependence (see the Details section for
a description of this model).summary.luca
(or summary
) can be used to obtain a summary of the results in a similar style to the lm
and glm
summaries.luca
may be used
to screen for coxph.fit
from the survival
package is used to fit the conditional logistic regression.A dependence model such as g ~ a
specifies a polychotomous
regression model for the genetic factor g
as a function of the
nongenetic attribute a
. The polychotomous regression for g
given a
holds when the conditional distribution of a
given
g
is from the exponential family of distributions, with a constant
dispersion parameter across the levels of the genetic factor.
Alternately, g
and a
may be conditionally independent
given a third variable a2
. Typically, a2
is also a term in
the penetrance model (pen.model
). To model conditional independence
of g
and a
given a2
, specify the dependence model
(dep.model
) as g ~ a2
. See Shin, McNeney and Graham (2007)
for details. luca
also allows dependence models of the form
g ~ a1 + a2 + ...
for multiple attributes a1
, a2
, ...
However, there is no formal justification for the use of such a model to capture the
dependence between g
and multiple nongenetic attributes.
summary.luca
, glm
, coxph
, clogit
data(lucaDat) # typical penetrance model: pen.model<-formula(d~I(allele.count(g,"C"))+a+a2+I(allele.count(g,"C")):a) #1. Assuming independence and HWP fitHWP<-luca(pen.model=pen.model, gLabel="g", dat=lucaDat, HWP=TRUE) fitHWP$coef fitHWP$var summary.luca(fitHWP) # OR 'summary(fitHWP)' #2. Assuming independence only fitDefault<-luca(pen.model=pen.model, gLabel="g", dat=lucaDat) fitDefault$coef fitDefault$var #3. Allowing for dependence between genetic and nongenetic factors # General dependence model fitDep1<-luca(pen.model=pen.model, gLabel="g", dat=lucaDat, dep.model=formula(g~a)) fitDep1$coef fitDep1$var # When 'g' and 'a' are conditioanally independent given the third variable 'a2': fitDep2<-luca(pen.model=pen.model, gLabel="g", dat=lucaDat, dep.model=formula(g~a2)) fitDep2$coef fitDep2$var