summary.snpLong {SimHap}R Documentation

Summarizing single SNP analysis models for longitudinal data

Description

Summary method for objects of class snpLong

Usage

## S3 method for class 'snpLong':
summary(object, ...)
## S3 method for class 'summary.snpLong':
print(x, digits = max(3, getOption("digits") - 3), 
        signif.stars = getOption("show.signif.stars"), ...)

Arguments

object an object of class snpLong, a result of a call to snp.long.
x an object of class summary.snpLong, the result of a call to
summary.snpLong.
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.

Value

summary.snpLong returns an object of class summary.snpLong. Some components taken from summary.lme. A list with components

call the formula call.
terms terms attribute of the formula called in snp.long.
fixDF as per lmeObject. A list with components X and terms specifying the denominator degrees of freedom for, respectively, t-tests for the individual fixed effects and F-tests for the fixed-effects terms in the models.
sigma as per lmeObject. The estimated within-group error standard deviation.
groups as per lmeObject. A data frame with the grouping factors as columns. The grouping level increases from left to right.
dims as per lmeObject. A list with basic dimensions used in the lme fit, including the components N - the number of observations in the data, Q - the number of grouping levels, qvec - the number of random effects at each level from innermost to outermost (last two values are equal to zero and correspond to the fixed effects and the response), ngrps - the number of groups at each level from innermost to outermost (last two values are one and correspond to the fixed effects and the response), and ncol - the number of columns in the model matrix for each level of grouping from innermost to outermost (last two values are equal to the number of fixed effects and one).
method as per lmeObject. The estimation method: either ``ML" for maximum likelihood, or ``REML" for restricted maximum likelihood.
coefficients summarized results from fitted model, including coefficients, standard errors and p-values.
formula fixed effects formula used in snp.long.
residuals as per lmeObject. If more than five observations are used in the lme fit, a vector with the minimum, first quartile, median, third quartile, and maximum of the innermost grouping level residuals distribution; else the innermost grouping level residuals.
AIC Akaike Information Criterion for the model fitted in snp.long.
corStruct the correlation structure specified in snp.long.
modelStruct as per lmeObject. An object inheriting from class lmeStruct, representing a list of mixed-effects model components, such as reStruct, corStruct, and varFunc objects.

Author(s)

Pamela A. McCaskie

References

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.

See Also

haplo.long, lme

Examples


data(SNPlong.dat)

# transforms SNPlong.dat to an object containing 3 columns
# per SNP - additive, dominant and recessive, where genotypes
# defined in 'baseline' serve as the baseline genotypes
longGeno.dat <- SNP2Geno(SNPlong.dat, baseline=c("AA", "GG", "V2V2"))

data(longPheno.dat)
mymodel <- snp.long(fixed=fev1f~SNP_1_add, random=~1|id, 
        geno=longGeno.dat, pheno=longPheno.dat, form=~year|id)
summary(mymodel)


[Package SimHap version 1.0.0 Index]