R: Linear and logistic regression and Cox models for genome-wide...

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

    	require(GenABEL.data)
    	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 1.8-0 Index]