Create a design matrix for a linear model

1 | ```
make.design(target, cov, int=NULL)
``` |

`target` |
a data frame contains chip and covaraite information, or experimental phenotypes recorded in eSet and ExpressionSet-derived classes |

`cov` |
a list of 1-n covariates |

`int` |
if int=NULL, the interaction effect is not considered; otherwise, use two integers to indicate which covariates are considered for interaction effect. For example, if cov<-c("estrogen","drug","time") and you are considering the interaction between "estrogen" and "time", then you would write int=c(1,3) |

a matrix containing design matrix for the linear model

Xiwei Wu xwu@coh.org, Xuejun Arthur Li xueli@coh.org

1 2 3 4 5 6 7 8 9 10 | ```
target<-data.frame(drug=(c(rep("A",4),rep("B",4),rep("C",4))),
gender=factor(c(rep("M",6),rep("F",6))),
group=factor(rep(c(1,2,3),4)))
#To create a design matrix using "drug", "gender" as covariates
design1<-make.design(target, c("drug","gender"))
#To create a design matrix by using "drug","gender","group" as covariates,
#and consider the interaction effect of "drug" and "group"
design2<-make.design(target, c("drug","gender", "group"), int=c(1,3))
``` |

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