Here are the brute force ways to get the covariance matrices. Note that these approaches might be fragile in that the structure of the
colext output might change (although I think it has been pretty stable - of course, the authors would know better.)
My recommendation is to always obtain the full covariance matrix rather than the individual parts related to just psi, colonization, extinction, or detection. Here is how to do that using an example from the colext documentation:
library(unmarked)
data(frogs)
umf <- formatMult(masspcru)
obsCovs(umf) <- scale(obsCovs(umf))
## Use 1/4 of data just for run speed in example
umf <- umf[which((1:numSites(umf)) %% 4 == 0),]
## constant transition rates
(fm <- colext(psiformula = ~ 1,
gammaformula = ~ 1,
epsilonformula = ~ 1,
pformula = ~ JulianDate + I(JulianDate^2),
umf, control = list(trace=1, maxit=1e4)))
# Covariance matrix for all parameters
solve(fm@opt$hessian)
# [,1] [,2] [,3] [,4] [,5] [,6]
#[1,] 0.157233764 -0.015852279 0.005617684 -0.002293078 0.002499374 0.001291073
#[2,] -0.015852279 0.247115560 0.074642827 0.004091612 -0.003769442 -0.003015345
#[3,] 0.005617684 0.074642827 0.230822847 0.011362919 -0.010602254 -0.002347965
#[4,] -0.002293078 0.004091612 0.011362919 0.022007161 -0.006686086 -0.017201613
#[5,] 0.002499374 -0.003769442 -0.010602254 -0.006686086 0.055609784 0.038921565
#[6,] 0.001291073 -0.003015345 -0.002347965 -0.017201613 0.038921565 0.048542605
# Covariance matrix for psi formula
fm@estimates@estimates$psi@covMat
# [,1]
#[1,] 0.1572338
# Covariance matrix for gamma (colonization) formula
fm@estimates@estimates$col@covMat
# [,1]
#[1,] 0.2471156
# Covariance matrix for epsilon (extinction) formula
fm@estimates@estimates$ext@covMat
# [,1]
#[1,] 0.2308228
# Covariance matrix for p formula
fm@estimates@estimates$det@covMat
# [,1] [,2] [,3]
#[1,] 0.022007161 -0.006686086 -0.01720161
#[2,] -0.006686086 0.055609784 0.03892157
#[3,] -0.017201613 0.038921565 0.04854260
Jim