summary.snpQuant {SimHap} | R Documentation |
Summary method for objects of class snpQuant
## S3 method for class 'snpQuant': summary(object, ...) ## S3 method for class 'summary.snpQuant': print(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class snpQuant , a result of a call to snp.quant . |
x |
an object of class summary.snpQuant , the result of a call to summary.snpQuant. |
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. |
summary.snpQuant
returns an object of class summary.snpQuant
, a list with components
call |
the formula call. |
terms |
terms attribute of the formula called in snp.quant . |
residuals |
the residuals, that is response minus fitted values. |
df.residuals |
the residual degrees of freedom. |
na.action |
method used for missing data. |
df |
residual degrees of freedom. |
sigma |
residual standard error. |
coefficients |
summarized results from fitted model, including coefficients, standard errors and p-values. |
formula |
formula1 used in snp.quant . |
LRT |
likelihood ratio test comparing the model with SNP variables compared to the model without SNPs. |
AIC |
Akaike information criterion for the full fitted model. |
rsquared |
adjusted r-squared values for the fitted model. |
predicted.values |
estimated marginal means for a chosen model term. |
Pamela A. McCaskie
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.
data(SNP.dat) # convert SNP.dat to format required by snp.quant geno.dat <- SNP2Geno(SNP.dat, baseline=c("MM", "11", "GG", "CC")) data(pheno.dat) mymodel <- snp.quant(formula1=HDL~AGE+SBP+factor(SNP_1_add), formula2=HDL~AGE+SBP, geno=geno.dat, pheno=pheno.dat) summary(mymodel)