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]