# conditionLevel: Levels of all environmental factors In designGG: Computational tool for designing genetical genomics experiments.

## Description

Describe the levels of all environmental factors for each RIL/strain in the experiment.
This is a subfunction needed for `designScore`, but is not directly used.

## Usage

 ```1 2``` ``` conditionLevel( array.allocation, condition.allocation, condition.combination,nEnvFactors ) ```

## Arguments

 `array.allocation` a matrix with nArray rows and nRIL columns. Elements of 1/0 indicates this RIL (or strain) is/not selected for this array. `condition.allocation` a matrix with nCondition rows and nRIL columns. Elements of 1/0 indicates this RIL (or strain) is/not selected for this condition. `condition.combination` a matrix indicating all possible levels for environmental factors, with dimension of nConditions by nEnvFactors. `nEnvFactors` number of environmental factors, an integer bewteen 1 and 3. When `nEnvFactors` is 1 and nLevels is 1, there is one condition in the experiment (i.e. no enviromental perturbation) and thus only genetic factor will be considered in the algorithm. When `nEnvFactors` is 1 and nLevels is larger than 1 or `nEnvFactors` is larger than 1, all main factor(s) and interacting facotr(s) will be included.

## Details

For single-channel experiment, `array.allocation` is `NULL`. Then the `conditionLevel` is decided by `condition.allocation`. For dual-channel experiment, `array.allocation` decides which RILs are selected and then the `condition.allocation` indicates which condition this RIL will be put in for the experiment.

## Value

A matrix with dimension of nRILs by nEnvFactors, each element indicates the level of a certain environmental factor to which the RIL (or strain) is exposed in the experiment.

## Author(s)

Yang Li <[email protected]>, Gonzalo Vera <[email protected]>
Rainer Breitling <[email protected]>, Ritsert Jansen <[email protected]>

## References

Y. Li, R. Breitling and R.C. Jansen. Generalizing genetical genomics: the added value from environmental perturbation, Trends Genet (2008) 24:518-524.
Y. Li, M. Swertz, G. Vera, J. Fu, R. Breitling, and R.C. Jansen. designGG: An R-package and Web tool for the optimal design of genetical genomics experiments. BMC Bioinformatics 10:188(2009)
http://gbic.biol.rug.nl/designGG

`designScore`, `conditionCombination`