haplo.bin {SimHap} | R Documentation |
haplo.bin
performes a series of generalized linear models using a simulation-based approach to account for uncertainty in haplotype assignment when phase is unknown.
haplo.bin(formula1, formula2, pheno, haplo, sim, effect = "add", sub = NULL)
formula1 |
a symbolic description of the full model including haplotype parameters to be fit. The details of model specification are given below. |
formula2 |
a symbolic description of the nested model excluding haplotype parameters, to be compared to formula1 in a likelihood ratio test. |
pheno |
a phenotype data set. |
haplo |
a haplo 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 |
an expression representing a subset of the data on which to perform the models. |
formula1
should be in the form outcome ~ predictor(s) + haplotype(s)
and formula2
should be in the form outcome ~ predictor(s)
. A formula has an implied intercept term. See documentation for the formula
function for more details of allowed formulae.
haplo.bin
returns an object of class hapBin
.
The summary
function can be used to obtain and print a
summary of the results.
An object of class hapBin
is a list containing the
following components:
formula1 |
formula1 passed to haplo.bin . |
formula1 |
formula2 passed to haplo.bin . |
results |
a table containing the odds ratios, confidence intervals and p-values of the parameter estimates, averaged over the sim models performed. |
empiricalResults |
a list containing the odds ratios, confidence intervals and p-values calculated at each simulation. |
ANOD |
analysis of deviance table for the model fit using formula1 , averaged over all simulations. |
logLik |
the average log-likelihood for the generalized linear model fit using formula1 . |
LRT |
a likelihood ratio test, testing for significant improvement of the model when haplotypic parameters are included. |
aic |
Akaike Information Criterion for the generalized linear model fit using
formula1 , averaged over all simulations. |
aicPredicted |
Akaike Information Criteria calculated at each simulation. |
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
Dobson, A.J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
Hastie, T.J., Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S, eds Chambers, J.M., Hastie, T.J., Wadsworth & Brooks/Cole.
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.
McCullagh, P., Nelder, J.A. (1989) Generalized Linear Models. London: Chapman and Hall.
Rubin, D.B. (1996) Multiple imputation after 18+ years (with discussion). Journal of the American Statistical Society, 91:473-489.
Venables, W.N., Ripley, D.B. (2002) Modern Applied Statistics with S. New York: Springer.
snp.bin
, haplo.quant
, haplo.quant
, haplo.long
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) # print haplotype frequencies generated by infer.haplos myinfer$hap.freq # 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.bin(formula1=PLAQUE~AGE+SBP+h.N1AA, formula2=PLAQUE~AGE+SBP, pheno=pheno.dat, haplo=myhaplo, sim=10) # example with a subsetting variable, looking at males only # and modelling a dominant haplotypic effect mymodel <- haplo.bin(formula1=PLAQUE~AGE+SBP+h.N1AA, formula2=PLAQUE~AGE+SBP, pheno=pheno.dat, haplo=myhaplo, sim=10, effect="dom", sub=expression(SEX==1))