Hello,
I am having a problem understanding the lda package. I have a dataset here:
[,1] [,2] [,3]
[1,] 2.95 6.63 0
[2,] 2.53 7.79 0
[3,] 3.57 5.65 0
[4,] 3.16 5.47 0
[5,] 2.58 4.46 1
[6,] 2.16 6.22 1
[7,] 3.27 3.52 1
If I do the following;
"names(d)<-c("y","x1","x2")
d$x1 = d$x1 * 100
d$x2 = d$x2 * 100
g<-lda( y ~ x1 + x2, data=d)
v2 <- predict(g, d)",
I get;
LD1
1 -2.3769280
2 -2.7049437
3 -3.4748309
4 -0.9599825
5 4.2293774
6 2.6052193
7 2.6820884
However, If I do it manually,
"rawdata<-matrix(scan("tab1_1.dat"),ncol=3,byrow=T)
group <- rawdata[,1]
X <- 100 * rawdata[,2:3]
Apf <- X[group==1,]
Af <- X[group==0,]
xbar1 <- apply(Af, 2, mean)
S1 <- var(Af)
N1 <- dim(Af)[1]
xbar2 <- apply(Apf, 2, mean)
S2 <- var(Apf)
N2 <- dim(Apf)[1]
S<-((N1-1)*S1+(N2-1)*S2)/(N1+N2-2)
Sinv=solve(S)
d<-xbar1-xbar2
b <- Sinv %*% d
v <- X %*% b",
I get;
[,1]
[1,] 164.4283
[2,] 166.2492
[3,] 170.5232
[4,] 156.5622
[5,] 127.7540
[6,] 136.7704
[7,] 136.3436
It seems there is an extra step that I am missing? The predict step that
adds a constant to the second set of values? Can anyone clear this up for
me?
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