Eigenvalues of a matrix, via TDA — tda

Runs TDA's etest, which computes the eigenvalues and eigenvectors of a square matrix and reports the residual of each. It is a test of TDA's eigen routines rather than a general facility – eigen is what you want for ordinary work – but it is the way to check what TDA itself computes for a matrix a model is about to be fitted on.

Usage

tda_eigen(
  x,
  algorithm = 1,
  max_iter = NULL,
  options = list(),
  dir = tempfile("tda"),
  ...
)

Arguments

x: a square numeric matrix.
algorithm: 1 (default), 2 (eigen1) or 3 (eigen2).
max_iter: for algorithm = 3: iteration limit, TDA's default is 100.
options: a named list of further TDA options, passed through.
dir: working directory.
...: passed to tda_run.

Value

A data frame with one row per eigenvalue: re and im, its real and imaginary parts. attr(x, "run") carries the run, whose output holds the eigenvectors and residuals.

Details

The eigenvalues come from an exporter rather than from the printed table, so they arrive at full precision instead of rounded to the print format.

Examples

m <- diag(c(2, 3, 5))
tda_eigen(m)
#>   re im
#> 1  2  0
#> 2  3  0
#> 3  5  0