mlreg.p: EXPERIMENTAL Linear and logistic regression and Cox models...

In GenABEL: genome-wide SNP association analysis

Description

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

Usage

mlreg.p(formula, data, snpsubset, idsubset, gtmode = "additive", trait.type = "guess")

Arguments

formula	Standard formula object
data	an object of gwaa.data-class
snpsubset	Index, character or logical vector with subset of SNPs to run analysis on. If missing, all SNPs from data are used for analysis.
idsubset	Index, character or logical vector with subset of IDs to run analysis on. If missing, all people from data/cc are used for analysis.
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

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

	require(GenABEL.data)
	data(ge03d2)
	dta <- ge03d2[,1:100]
# analysis using linear model
	xq <- mlreg.p(bmi~sex,dta)
# logistic regression, type guessed automatically
	xb <- mlreg.p(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.p(GASurv(age,dm2)~sex,dta)

Example output

Loading required package: MASS
Loading required package: GenABEL.data

GenABEL documentation built on May 30, 2017, 3:36 a.m.