# contrastAsCoef: Reform a Design Matrix to that Contrasts Become Coefficients In limma: Linear Models for Microarray Data

## Description

Reform a design matrix so that one or more coefficients from the new matrix correspond to specified contrasts of coefficients from the old matrix.

## Usage

 `1` ```contrastAsCoef(design, contrast=NULL, first=TRUE) ```

## Arguments

 `design` numeric design matrix. `contrast` numeric matrix with rows corresponding to columns of the design matrix (coefficients) and columns containing contrasts. May be a vector if there is only one contrast. `first` logical, should coefficients corresponding to contrasts be the first columns (`TRUE`) or last columns (`FALSE`) of the output design matrix.

## Details

If the contrasts contained in the columns of `contrast` are not linearly dependent, then superfluous columns are dropped until the remaining matrix has full column rank. The number of retained contrasts is stored in `qr\$rank` and the retained columns are given by `qr\$pivot`.

## Value

A list with components

 `design` reformed design matrix `coef` columns of design matrix which hold the meaningful coefficients `qr` QR-decomposition of contrast matrix

## Author(s)

Gordon Smyth

`model.matrix` in the stats package.

An overview of linear model functions in limma is given by 06.LinearModels.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```design <- cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)) cont <- c(0,-1,1) design2 <- contrastAsCoef(design, cont)\$design # Original coef[3]-coef[2] becomes coef[1] y <- rnorm(6) fit1 <- lm(y~0+design) fit2 <- lm(y~0+design2) coef(fit1) coef(fit1) coef(fit2) ```

### Example output

```   design1    design2    design3
-0.1032381  0.2882667  0.9806105
design1    design2    design3
-0.1032381  0.2882667  0.9806105
design2C1 design2Q2 design2Q3
0.6923438 0.5614383 0.7074390
```

limma documentation built on Nov. 8, 2020, 8:28 p.m.