INTlevels {Envisage} | R Documentation

## Calculate the total number of levels within a model for package Envisage

### Description

This function calculates the total number of levels of all variables and interactions within the maximal model considered in the stepwise modelling procedure of Envisage.

### Usage

      INTlevels(pData)

### Arguments

`pData` | An object of class `phenoData` containing the phenotypic information for the experiment.

### Details

The Envisage package contains methods allowing the use of linear models (LMs) for analysing significantly changing genes in experiments with a variety of sources of variation, be they experimentally controlled variables such as drug treatment or time, or non-controlled sources of confounding variation such as phenotypic or environmental differences. This allows all sources of variation to be considered when analysing for significant differential expression, ensuring resulting genes are biologically relevent to the experimental question.

The function `INTlevels` calculates the total number of levels of all main effect and interaction terms in the maximal model considered within the stepwise modelling procedure of Envisage. If too many variables are included in the model, with a greater total of levels than there are data points, this may result in incorrect fitting of the model. This should be avoided by being aware of how many degrees of freedom each variable that you want to include will take up. If a variable of interest has a large number of possible levels, consider combining some of these to avoid over-fitting the data or only use this as a main effect term instead of considering all interactions.

`INTlevels` is an internal function and is not designed to be accessed by users.

### Value

A vector is returned containing the total number of levels required for each main effect and first order interation of variables within the analysis.

### Author(s)

Sam Robson S.C.Robson@warwick.ac.uk

### References

Robson, S. C., Hunter, E., Bird, H., Turner, H. (2008) Envisage: model-based significance analysis of microarray gene expression data, manuscript in preparation

### See Also

For the main Envisage method, see `envisage`.

[Package Envisage version 1.0-2 Index]