epi.long {SimHap}R Documentation

Epidemiological analysis for longitudinal data

Description

epi.long is used to fit linear mixed effects models to epidemiological data with longitudinal outcomes.

Usage

epi.long(fixed, random, pheno, cor="corCAR1", value = 0.2, 
        form=~1, sub = NULL)

Arguments

fixed as per lme. A two-sided linear formula object describing the fixed-effects part of the model including SNP parameters, with the response on the left of a ~ operator and the terms, separated by + operators.
random as per lme. A one-sided formula of the form ~x1+...+xn | g1/.../gm, with x1+...+xn specifying the model for the random effects and g1/.../gm the grouping structure (m may be equal to 1, in which case no / is required). The random effects formula will be repeated for all levels of grouping, in the case of multiple levels of grouping.
pheno a dataframe containing phenotype data.
cor a corStruct object describing the within-group correlation structure. Available correlation structures are corAR1, corCAR1, and corCompSymm. See the documentation of corClasses for a description of these. Defaults to corCAR1.
value for corAR1 - the value of the lag 1 autocorrelation, which must be between -1 and 1. For corCAR1 - the correlation between two observations one unit of time apart. Must be between 0 and 1. For corCompSymm - the correlation between any two correlated observations. Defaults to 0.2.
form a one sided formula of the form ~ t, or ~ t | g, specifying a time covariate t and, optionally, a grouping factor g. A covariate for this correlation structure must be integer valued. When a grouping factor is present in form, the correlation structure is assumed to apply only to observations within the same grouping level; observations with different grouping levels are assumed to be uncorrelated. Defaults to ~ 1, which corresponds to using the order of the observations in the data as a covariate, and no groups.
sub an expression representing a subset of the data on which to perform the models.

Details

cor will always default to corCAR1 and value will always default to 0.2. Be sure to change both parameters accordingly if desired. See corClasses for more details.

Value

epi.long returns an object of class epiLong.
The summary function can be used to obtain and print a summary of the results.
An object of class epiLong is a list containing the following components:

results a table containing the coefficients, standard errors and p-values of the parameter estimates.
fixed_formula fixed effects formula.
random_formula random effects formula.
fit.lme a lme object fit using formula.
ANOD analysis of deviance table for the fitted model.
logLik the log-likelihood for the fitted model.
AIC Akaike Information Criterion for the model fit using formula.
corStruct correlation structure used in the fitted model.

Author(s)

Pamela A McCaskie

References

Bates, D.M., Pinheiro, J.C. (1998) Computational methods for multilevel models. Available in PostScript or PDF formats at http://franz.stat.wisc.edu/pub/NLME/

Box, G.E.P., Jenkins, G.M., Reinsel, G.C. (1994) Time Series Analysis: Forecasting and Control, 3rd Edition, Holden-Day.

Davidian, M., Giltinan, D.M. (1995) Nonlinear Mixed Effects Models for Repeated Measurement Data, Chapman and Hall.

Laird, N.M., Ware, J.H. (1982) Random-Effects Models for Longitudinal Data, Biometrics, 38, 963-974.

Lindstrom, M.J., Bates, D.M. (1988) Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data, Journal of the American Statistical Association, 83, 1014-1022.

Littel, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D. (1996) SAS Systems for Mixed Models, SAS Institute.

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.

Pinheiro, J.C., Bates, D.M. (1996) Unconstrained Parametrizations for Variance-Covariance Matrices, Statistics and Computing, 6, 289-296.

Pinheiro, J.C., Bates, D.M. (2000) Mixed-Effects Models in S and S-PLUS, Springer.

See Also

snp.long, haplo.long, corClasses

Examples


data(longPheno.dat)
mymodel <- epi.long(fixed=fev1f~height+weight, random=~1|id, 
        pheno=longPheno.dat, form=~year|id)
summary(mymodel)

# example with a subsetting variable, looking at males only
mymodel <- epi.long(fixed=fev1f~height+weight, random=~1|id, 
        pheno=longPheno.dat, form=~year|id, sub=expression(sex==1))
summary(mymodel)


[Package SimHap version 1.0.0 Index]