
Calculate the elasticity matrix for a specified population matrix projection model using eigenvectors.
elas(A, eval = "max")
a square, non-negative numeric matrix of any dimension
the eigenvalue to evaluate. Default is eval="max"
, which
evaluates the dominant eigenvalue (the eigenvalue with largest REAL value:
for imprimitive or reducible matrices this may not be the first eigenvalue).
Otherwise, specifying e.g. eval=2
will evaluate elasticity of the
eigenvalue with second-largest modulus.
A numeric (real or complex) matrix of equal dimension to A
.
elas
uses the eigenvectors of A
to calculate the elasticity
matrix of the specified eigenvalue, see section 9.1 in Caswell (2001).
Same method as elasticity
in popbio
but can also evaluate
subdominant eigenvalues.
Caswell (2001) Matrix Population Models 2nd ed. Sinauer.
Other PerturbationAnalyses:
sens()
,
tfa_inertia()
,
tfa_lambda()
,
tfam_inertia()
,
tfam_lambda()
,
tfs_inertia()
,
tfs_lambda()
# NOT RUN {
# Create a 3x3 PPM
( A <- matrix(c(0,1,2,0.5,0.1,0,0,0.6,0.6), byrow=TRUE, ncol=3) )
# Calculate sensitivities of dominant eigenvalue
elas(A)
# Calculate sensitivities of first subdominant eigenvalue,
# only for observed transitions
elas(A, eval=2)
# }
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