summary.hapSurv {SimHap} | R Documentation |
Summary method for objects of class hapSurv
## S3 method for class 'hapSurv': summary(object, ...) ## S3 method for class 'summary.hapSurv': print(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class hapSurv , a result of a call to haplo.surv . |
x |
an object of class summary.hapSurv , the result of a call to summary.hapSurv. |
digits |
the number of significant digits to use when printing. |
signif.stars |
logical. If TRUE , ``significance stars" are printed for each coefficient. |
... |
further arguments passed to or from other methods. |
summary.hapSurv
returns an object of class summary.hapSurv
, a list with components
formula |
formula1 passed into haplo.surv . |
coefficients |
summarized results from fitted model, including hazard ratios and p-values. |
empiricalResults |
a list containing the hazard ratios, confidence intervals and p-values calculated at each simulation of haplo.surv . |
rsquared |
r-squared values for the model including haplotypes and the model excluding haplotypes. |
LRT |
a likelihood ratio test, testing for significant improvement of the model when haplotypic parameters are included. |
Wald |
The Wald test for overall significance of the fitted model. |
effect |
the haplotypic effect modelled: `ADDITIVE', `DOMINANT' or `RECESSIVE'. |
Pamela A. McCaskie
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.
data(SNPsurv.dat) # convert SNP.dat to format required by infer.haplos survHaplo.dat <- SNP2Haplo(SNPsurv.dat) data(survPheno.dat) # generate haplotype frequencies and haplotype design matrix myinfer<-infer.haplos(survHaplo.dat) # print haplotype frequencies generated by infer.haplos myinfer$hap.freq myhaplo<-make.haplo.rare(myinfer,min.freq=0.05) mymodel <- haplo.surv(formula1=Surv(time, status)~age+h.V1AA, formula2=Surv(time, status)~age, haplo=myhaplo, pheno=survPheno.dat, sim=10) summary(mymodel)