sens: Calculate sensitivity matrix
In popdemo: Demographic Modelling Using Projection Matrices

Description Usage Arguments Details Value References See Also Examples

Calculate the sensitivity matrix for a population matrix projection model using eigenvectors.

1	sens(A, eval = "max", all = FALSE)

`A`	a square, non-negative numeric matrix of any dimension
`eval`	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 sensitivity of the eigenvalue with second-largest modulus.
`all`	(optional) if `FALSE`, then only sensitivity values for observed transitions (nonzero entries in `A`) are returned.

sens uses the eigenvectors of A to calculate the sensitivity matrix of the specified eigenvalue, see section 9.1 in Caswell (2001). Same method as sensitivity in popbio but can also evaluate subdominant eigenvalues.

A numeric (real or complex) matrix of equal dimension to A.

Caswell (2001) Matrix Population Models 2nd ed. Sinauer.

Other PerturbationAnalyses: elas(), tfa_inertia(), tfa_lambda(), tfam_inertia(), tfam_lambda(), tfs_inertia(), tfs_lambda()

  # 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
  sens(A)
  # Calculate sensitivities of first subdominant eigenvalue,
  # only for observed transitions
  sens(A, eval=2, all=FALSE)