epi.bin {SimHap} | R Documentation |
epi.bin
is used to fit generalized linear regression models to epidemiological phenotype data for a binary outcome, assuming a binomial error distribution and logit link function.
epi.bin(formula, pheno, sub = NULL)
formula |
a symbolic description of the model to be fit. The details of model specification are given below. |
pheno |
a dataframe containing phenotype data. |
sub |
an expression representing a subset of the data on which to perform the model. |
formula
should be in the form of outcome ~ predictor(s)
. A formula has an implied intercept term. See documentation for formula
function for more details of allowed formulae.
epi.bin
returns an object of class epiBin
containing the following items
formula |
formula passed to epi.bin . |
results |
a table containing the odds ratios, confidence intervals and p-values of the parameter estimates. |
fit.glm |
a glm object fit using formula . |
ANOD |
analysis of deviance table for the model fit using formula . |
logLik |
the log-likelihood for the linear model fit using formula . |
AIC |
Akaike Information Criterion for the linear model fit using formula . |
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(pheno.dat) mymodel <- epi.bin(formula=PLAQUE~AGE+SBP, pheno=pheno.dat) summary(mymodel) # example with a subsetting variable, looking at males only mymodel <- epi.bin(formula=PLAQUE~AGE+SBP, pheno=pheno.dat, sub=expression(SEX==1))