How to create design matrix in r

Question

I have two factors. factor A have 2 level, factor B have 3 level.

How to create the following design matrix?

     factorA1 factorA2 factorB1 factorB2 factorB3
[1,]        1        0        1        0        0
[2,]        1        0        0        1        0
[3,]        1        0        0        0        1
[4,]        0        1        1        0        0
[5,]        0        1        0        1        0
[6,]        0        1        0        0        1

Answer 1

You have a couple of options:

Use base and piece it together yourself:

(iris.dummy<-with(iris,model.matrix(~Species-1))) 
(IRIS<-data.frame(iris,iris.dummy)) 

Or use the ade4 package as follows:

dummy <- function(df) {
    require(ade4)
    ISFACT <- sapply(df, is.factor)
    FACTS <- acm.disjonctif(df[, ISFACT, drop = FALSE])
    NONFACTS <- df[, !ISFACT,drop = FALSE]
    data.frame(NONFACTS, FACTS)
}

dat <-data.frame(eggs = c("foo", "foo", "bar", "bar"),  
    ham = c("red","blue","green","red"), x=rnorm(4)) 
dummy(dat)



##           x eggs.bar eggs.foo ham.blue ham.green ham.red
## 1 0.3365302        0        1        0         0       1
## 2 1.1341354        0        1        1         0       0
## 3 2.0489741        1        0        0         1       0
## 4 1.1019108        1        0        0         0       1

Answer 2

Assuming your data in in a data.frame called dat, let's say the two factors are given as in this example:

> dat <- data.frame(f1=sample(LETTERS[1:3],20,T),f2=sample(LETTERS[4:5],20,T),id=1:20)
> dat
   f1 f2 id
1   C  D  1
2   B  E  2
3   B  E  3
4   A  D  4
5   C  E  5
6   C  E  6
7   C  D  7
8   B  E  8
9   C  D  9
10  A  D 10
11  B  E 11
12  C  E 12
13  B  D 13
14  B  E 14
15  A  D 15
16  C  E 16
17  C  D 17
18  C  D 18
19  B  D 19
20  C  D 20
> dat$f1
 [1] C B B A C C C B C A B C B B A C C C B C
Levels: A B C
> dat$f2
 [1] D E E D E E D E D D E E D E D E D D D D
Levels: D E

You can use outer to get a matrix as you showed, for each factor:

> F1 <- with(dat, outer(f1, levels(f1), `==`)*1)
> colnames(F1) <- paste("f1",sep="=",levels(dat$f1))
> F1
      f1=A f1=B f1=C
 [1,]    0    0    1
 [2,]    0    1    0
 [3,]    0    1    0
 [4,]    1    0    0
 [5,]    0    0    1
 [6,]    0    0    1
 [7,]    0    0    1
 [8,]    0    1    0
 [9,]    0    0    1
[10,]    1    0    0
[11,]    0    1    0
[12,]    0    0    1
[13,]    0    1    0
[14,]    0    1    0
[15,]    1    0    0
[16,]    0    0    1
[17,]    0    0    1
[18,]    0    0    1
[19,]    0    1    0
[20,]    0    0    1

Now do the same for the second factor:

> F2 <- with(dat, outer(f2, levels(f2), `==`)*1)
> colnames(F2) <- paste("f2",sep="=",levels(dat$f2))

And cbind them to get the final result:

> cbind(F1,F2)