snp.quant {SimHap} | R Documentation |
snp.quant
is used to fit linear regression models to single SNP genotype and phenotype data for continuous Normal outcomes.
snp.quant(formula1, formula2, geno, pheno, sub = NULL, predict_variable = 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. |
predict_variable |
a factored SNP variable, at each level of which the response variable will be predicted based on the model fit using formula1. |
formula1
should be in the form: response ~ predictor(s) + SNP(s)
and formula2
should be in the form: response ~ predictor(s)
. A formula has an implied intercept term. See documentation for formula
function for more details of allowed formulae.
snp.quant
returns an object of 'class' snpQuant
.
The summary
function can be used to obtain and print a
summary of the results.
An object of class snpQuant
is a list containing the
following components:
results |
a table containing the coefficients, standard errors and p-values of the parameter estimates. |
formula1 |
formula1 passed to snp.quant . |
formula2 |
formula2 passed to snp.quant . |
LRT |
a likelihood ratio test, testing for significant improvement of the model when genotypic 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.lm |
a lm object fit using formula1 . |
fitsub.lm |
a lm object fit using formula2 . |
rsquared |
r-squared values for models fit using formula1 and formula2 . |
predicted.values |
estimated marginal means of the outcome variable broken down by SNP levels, evaluated at mean values of the model predictors. |
AIC |
Akaike Information Criterion for the linear model fit using formula1 . |
Pamela A. McCaskie
Chambers, J.M. (1992) Linear models. Chapter 4 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.
Wilkinson, G.N., Rogers, C.E. (1973) Symbolic descriptions of factorial models for analysis of variance. Applied Statistics, 22, 392-9.
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) # example using a variable for which to predict marginal means mymodel <- snp.quant(formula1=HDL~AGE+SBP+factor(SNP_1_add), formula2=HDL~AGE+SBP, geno=geno.dat, pheno=pheno.dat, predict_variable="SNP_1_add") summary(mymodel) # example with a subsetting variable, looking at males only mymodel <- snp.quant(formula1=HDL~AGE+SBP+factor(SNP_1_add), formula2=HDL~AGE+SBP, geno=geno.dat, pheno=pheno.dat, sub=expression(SEX==1))