| snp.bin {SimHap} | R Documentation |
snp.bin is used to fit generalized linear regression models to single SNP genotype and phenotype data for a binary outcome.
snp.bin(formula1, formula2, geno, pheno, sub = NULL)
formula1 |
a symbolic description of the full model to be fit, including SNP parameters. The details of model specification are given below. |
formula2 |
a symbolic description of the nested model excluding SNP parameters, to be compared to formula1 in a likelihood ratio test. |
geno |
a dataframe containing genotype data. |
pheno |
a dataframe containing phenotype data. |
sub |
an expression representing a subset of the data on which to perform the models. |
formula1 should be in the form of outcome ~ predictor(s) + SNP(s) and formula2 should be in the form outcome ~ predictor(s). A formula has an implied intercept term. See documentation for formula function for more details of allowed formulae.
snp.bin returns an object of 'class' snpBin.
The summary function can be used to obtain and print a
summary of the results.
An object of class snpBin is a list containing the
following components:
results |
a table containing the odds ratios, confidence intervals and p-values of the parameter estimates. |
formula1 |
formula1 passed to snp.bin. |
formula2 |
formula2 passed to snp.bin. |
LRT |
a likelihood ratio test, testing for significant improvement of the model when haplotypic parameters are included. |
ANOD |
analysis of deviance table for the model fit using formula1. |
logLik |
the log-likelihood for the linear model fit using formula1. |
fit.glm |
a glm object fit using formula1. |
fitsub.glm |
a glm object fit using formula2. |
AIC |
Akaike Information Criterion for the linear model fit using formula1. |
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.
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.
Venables, W.N., Ripley, D.B. (2002) Modern Applied Statistics with S. New York: Springer.
data(SNP.dat)
# convert SNP.dat to format required by snp.bin
geno.dat <- SNP2Geno(SNP.dat, baseline=c("MM", "11", "GG", "CC"))
data(pheno.dat)
mymodel <- snp.bin(formula1=PLAQUE~AGE+SEX+SNP_1_add,
formula2=PLAQUE~AGE+SEX, geno=geno.dat, pheno=pheno.dat)
summary(mymodel)
# example with a subsetting variable, looking at
# people over 50 years of age only
mymodel <- snp.bin(formula1=PLAQUE~AGE+SEX+SNP_1_add,
formula2=PLAQUE~AGE+SEX, geno=geno.dat, pheno=pheno.dat,
sub=expression(AGE>50))