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)