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))