Linear and logistic regression and Cox models for genome-wide SNP data

mlreg {GenABEL}R Documentation

Linear and logistic regression and Cox models for genome-wide SNP data

Description

Linear and logistic regression and Cox models for genome-wide SNP data

Usage


mlreg(formula, data, gtmode = "additive", trait.type = "guess", propPs = 1)

Arguments

formula Standard formula object
data an object of gwaa.data-class
gtmode Either "additive", "dominant", "recessive" or "overdominant". Specifies the analysis model.
trait.type Either "gaussian", "binomial" or "survival", corresponding to analysis using linear regression, logistic regression, and Cox proportional hazards models, respectively. When default vale "guess" is used, the program tries to guess the type
propPs proportion of non-corrected P-values used to estimate the inflation factor Lambda, passed directly to the estlambda

Details

Linear regression is performed using standard approach; logisitc regression is implemented using IRLS; Cox model makes use of code contributed by Thomas Lumley (survival package).

For logistic and Cox, exp(effB) gives Odds Ratios and Hazard Ratios, respectively.

Value

An object of scan.gwaa-class

Author(s)

Yurii Aulchenko

See Also

GASurv, qtscore

Examples

	data(ge03d2)
	dta <- ge03d2[,1:100]
# analysis using linear model
	xq <- mlreg(bmi~sex,dta)
# logistic regression, type guessed automatically
	xb <- mlreg(dm2~sex,dta)
# Cox proportional hazards model, assuming that age is the follow-up time 
# generally this does not make sense (could be ok if age is age at onset)
	xs <- mlreg(GASurv(age,dm2)~sex,dta)

[Package GenABEL version 1.6-7 Index]