mlreg.p {GenABEL} | R Documentation |
Linear and logistic regression and Cox models for genome-wide SNP data
mlreg.p(formula, data, snpsubset, idsubset, gtmode = "additive", trait.type = "guess")
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 |
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.
An object of scan.gwaa-class
Yurii Aulchenko
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)