snp.effects {CGEN} | R Documentation

## Joint and Stratified Effects

### Description

Computes joint and stratified effects of the SNP and another variable based on a fitted model.

### Usage

     snp.effects(fit, var, var.levels=c(0, 1), method=NULL)

### Arguments

`fit` | Return object from `snp.logistic` or `snp.matched`. If `fit` is the return object from `snp.matched`, then the `snp.vars` argument in `snp.matched` must consist of a single SNP. No default.
`var` | Name of the second variable to compute the effects for. This variable can be a dummy variable, continuous variable, or a factor. Note that if this variable enters the model as both a main effect and interaction, then it must enter the model the same way as a main effect and interaction for the effects to be computed correctly. For example, if `var` is a factor as a main effect, then it also must be a factor as an interaction. No default.
`var.levels` | (For continuous `var`) Vector of levels. First level is assumed to be the baseline level. The default is c(0, 1).
`method` | Vector of values from "UML", "CML", "EB" or "CCL", "HCL", "CLR". The default is NULL.

### Details

The joint and stratified effects are computed for each method in `fit`. The stratified effects are the sub-group effect of the SNP stratified by `var` and the sub-group effect of `var` stratified by the SNP.
Definition of joint and stratified effects:
Consider the model:

logit(P(y=1)) = alpha + beta*SNP + gamma*X + delta*SNP*X.

Let 0 be the baseline for SNP and x_0 the baseline for X. Then the joint effect for SNP = s and X = x relative to SNP = 0 and X = x_0 is

exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + gamma*x_0)

The stratified effect of the SNP relative to SNP = 0 given X = x is

exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + gamma*x)

The stratified effect of `var` relative to X = x given SNP = s is

exp(alpha + beta*s + gamma*x + delta*s*x)/exp(alpha + beta*s)

A convenient way to print the returned object to view the effects tables is with the function `printEffects`.

### Value

If `fit` is of class `snp.logistic`, then the return object is a list of with names "UML", "CML", and "EB". If `fit` is of class `snp.matched`, then the return object is a list of with names "CLR", "CCL", and "HCL". Each sublist contains joint effects, stratified effects, standard errors and confidence intervals. The sub-group effect of the SNP stratified by `var` is in the list "StratEffects", and the sub-group effect of `var` stratified by the SNP is in the list "StratEffects.2".

### See Also

`printEffects` `snp.effects.plot`

### Examples

     # Use the ovarian cancer data
     data(Xdata, package="CGEN")

     # Fit using a stratification variable
     fit <- snp.logistic(Xdata, "case.control", "BRCA.status",
                         main.vars=c("oral.years", "n.children"),
                         int.vars=c("oral.years", "n.children"),
                         strata.var="ethnic.group")

     # Compute the effects
     effects <- snp.effects(fit, "oral.years", var.levels=0:5)

[Package CGEN version 3.30.0 Index]