monteCarlo.eigen | R Documentation |
monteCarlo.eigen
:
generates
Monte Carlo random eigenvalues to match
a data given matrix. The random numbers can be generated from
any of the random number generators in R
.
(of course, the default is a standard normal distribution).
Note that the specific parameters for the random gnerator
need to be passed as additional arguments to the function
(i.e., with the "..." procedure).
monteCarlo.eigen(
X,
nIter = 100,
center = TRUE,
scale = "SS1",
FUN = rnorm,
...
)
X |
The data matrix to match. |
nIter |
how many random set of eigenvalues
to generate; Default: |
center |
(Default = |
scale |
the type of scaling of the
data. Can be |
FUN |
the function to generate
random numbers; Default: |
... |
Stuff (i.e., parameters)
to pass the |
monteCarlo.eigen
can be used
to implement a parallel test for the number
of reliable components. Note that the parallel test
becomes equivalent to the Kaiser test (i.e., eigenvalues
larger than the average inertia) when the number of rows
of the data matrix is large enough.
a list with 3 elements
$fixed.eigs
: the eigen-values of X
,
$rand.eigs
: an nIter
by rank(X
) matrix
of the eigenvalues of the bootstrapped samples,
$rand.eigs.sorted
:
an nIter
by rank(X
) matrix
of the eigenvalues of the bootstrapped samples.
rnorm
scale0
boot.eigen
data(iris)
random.eigen <- monteCarlo.eigen(iris[,1:4], nIter = 10)
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