I am attempting to use the lars package with a sparse input feature matrix,
but the following fails:
library(Matrix)
library(lars)
data(diabetes)
attach(diabetes)
x = as(as.matrix(as.data.frame(x)), 'dgCMatrix')
lars(x, y, intercept = FALSE)
Error in scale.default(x, FALSE, normx) :
>
> length of 'scale' must equal the number of columns of 'x'
>
>
More specifically, scale.default fails:
normx = new(
"dgeMatrix",
x = c(1.00000000000004, 1, 1.00000000000009,
1.00000000000001, 1.00000000000001,
0.999999999999992, 1.00000000000004,
0.999999999999975, 1.00000000000006,
1.00000000000006), Dim = c(1L, 10L),
Dimnames =
list(NULL, c("x.age", "x.sex", "x.bmi", "x.map", "x.tc",
"x.ldl", "x.hdl", "x.tch", "x.ltg", "x.glu")),
factors = list()
)
scale(x, FALSE, normx)
The problem is that this check fails because is.numeric(normx) is FALSE:
if (is.numeric(scale) && length(scale) == nc)
So, the error message is misleading. In fact length(scale) is the same as
nc.
At a minimum, the error message needs to be repaired; do we also want to
attempt as.numeric(normx) (which I believe would have allowed scale to work
in this case)?
(I'm aware that there's some import issues in lars, as the offending line
to create normx *should* work, as is.numeric(sqrt(drop(rep(1, nrow(x)) %*%
(x^2)))) is TRUE -- it's simply that lars doesn't import the appropriate S4
methods)
Michael Chirico
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