Question

I’m trying to conduct a discriminant analysis and keep running into the following error:

Error in sqrt((n * prior) * fac) * scale(group.means, center = xbar, scale = FALSE) %*%  : 
  non-conformable arrays
Calls: lda -> lda.formula -> lda.default

Here is my code broken down to a few lines:

categories <- c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 1, 1, 3, 3, 2, 1, 1, 3, 1, 1, 3, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3)
values <- c(1, 2, 3, 2, 0, 2, 2, 3, 3, 1, 3, 5, 5, 4, 3, 3, 4, 4, 2, 4, 7, 6, 7, 5, 7, 7, 7, 7, 7, 5, 3, 6, 7, 7, 7, 2, 5, 0, 3, 7, 6, 3, 2, 2, 4, 2, 2, 5, 5, 6, 2, 2, 4, 1, 3, 0, 3, 1, 4, 1, 1, 2, 4, 2, 4, 3, 3, 4, 7, 6, 4, 7, 6, 7, 7, 3, 6, 7, 4, 7, 3, 1, 2, 0, 2, 2, 5, 2, 7, 6, 6, 7)

data <- data.frame(categories=categories, values=values)
counts <- table(data[["categories"]])
prior <- counts / sum(counts)

z <- lda(categories ~ values, data, prior=prior)
predict(z, data)$class

It is probably something trivial…

Answer 1

The problem is that your object prior is of class table, but lda needs your priors to be a vector.

A simple workaround is to use as.vector on the results of table

prior <- as.vector(counts / sum(counts))

z <- lda(categories ~ values, dat, prior=prior)
predict(z, data)$class

 [1] 1 1 2 1 1 1 1 2 2 1 2 3 3 2 2 2 2 2 1 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 1 3 1
[39] 2 3 3 2 1 1 2 1 1 3 3 3 1 1 2 1 2 1 2 1 2 1 1 1 2 1 2 2 2 2 3 3 2 3 3 3 3 2
[77] 3 3 2 3 2 1 1 1 1 1 3 1 3 3 3 3
Levels: 1 2 3