| haplo.cc.match {SimHap} | R Documentation |
haplo.surv performs a series of conditional logistic regression models to matched case-control data with haplotypes using a simulation-based approach to account for uncertainty in haplotype assignment when phase is unknown.
haplo.cc.match(formula1, formula2, pheno, haplo, sim, effect = "add",
sub = NULL)
formula1 |
a symbolic description of the full model to be fit, including haplotype parameters. The response must be binary indicator of case-control status, and the formula must contain a variable indicating strata, or the matching sequence. |
formula2 |
a symbolic description of the nested model excluding haplotype parameters, to be compared to formula1 in a likelihood ratio test. The response must be binary indicator of case-control status, and the formula must contain a variable indicating strata, or the matching sequence. |
pheno |
a dataframe containing phenotype data. |
haplo |
a haplotype object made by make.haplo.rare. |
sim |
the number of simulations from which to evaluate the results. |
effect |
the genetic effect type: "add" for additive, "dom" for dominant and "rec" for recessive. Defaults to additive. See note. |
sub |
optional. An expression using a binary operator, representing a subset of individuals on which to perform analysis. e.g. sub=expression(sex==1). |
formula1 should be in the form:
response ~ predictor(s) + strata(strata_variable) + haplotype(s)and
formula2 should be in the form: response ~ predictor(s) + strata(strata_variable). If case-control data is not matched, the
haplo.bin function should be used.
haplo.cc.match returns an object of 'class' hapClogit.
The summary function can be used to obtain and print a
summary of the results.
An object of class hapClogit is a list containing the
following components:
formula1 |
formula1 passed to haplo.cc.match. |
formula2 |
formula2 passed to haplo.cc.match. |
results |
a table containing the odds ratios, confidence intervals and p-values of the parameter estimates, averaged over the n=sim models performed. |
empiricalResults |
a list containing the odds ratios, confidence intervals and p-values calculated at each simulation |
logLik |
the average log-likelihood for the n=sim linear models fit using formula1. |
LRT |
a likelihood ratio test, testing for significant improvement of the model when haplotypic parameters are included |
ANOVA |
analysis of variance, comparing the two models fit with and without haplotypic parameters. |
Wald |
The Wald test for overall significance of the fitted model including haplotypes. |
rsquared |
r-squared values for models fit using formula1 and formula2. |
effect |
the haplotypic effect modelled, `ADDITIVE', `DOMINANT' or `RECESSIVE'. |
To model a codominant haplotypic effect, define the desired haplotype as a factor in the formula1 argument. e.g. factor(h.AAA), and use the default option for effect.
Pamela A. McCaskie
Little, R.J.A., Rubin, D.B. (2002) Statistical Analysis with Missing Data. John Wiley and Sons, New Jersey.
McCaskie, P.A., Carter, K.W. Hazelton, M., Palmer, L.J. (2007) SimHap: A comprehensive modeling framework for epidemiological outcomes and a multiple-imputation approach to haplotypic analysis of population-based data, [online] www.genepi.org.au/simhap.
Rubin, D.B. (1996) Multiple imputation after 18+ years (with discussion). Journal of the American Statistical Society, 91:473-489.
data(SNP.dat)
# convert SNP.dat to format required by infer.haplos
haplo.dat <- SNP2Haplo(SNP.dat)
data(pheno.dat)
# generate haplotype frequencies and haplotype design matrix
myinfer<-infer.haplos(haplo.dat)
# prints haplotype frequencies among cases
myinfer$hap.freq.cases
# prints haplotype frequencies among controls
myinfer$hap.freq.controls
# generate haplo object where haplotypes with a frequency
# below min.freq are grouped as a category called "rare"
myhaplo<-make.haplo.rare(myinfer,min.freq=0.05)
mymodel <- haplo.cc.match(formula1=DISEASE~SBP+DBP+h.N1AA+strata(STRAT),
formula2=DISEASE~SBP+DBP+strata(STRAT), haplo=myhaplo,
pheno=pheno.dat, sim=10)
summary(mymodel)
# example using a subsetting variable - looking at males only
mymodel <- haplo.cc.match(formula1=DISEASE~SBP+DBP+h.N1AA+strata(STRAT),
formula2=DISEASE~SBP+DBP+strata(STRAT), haplo=myhaplo,
pheno=pheno.dat, sim=10, sub=expression(SEX==1))
summary(mymodel)