## S4 method for signature 'hansentree'
logLik(object)
## S4 method for signature 'hansentree'
coef(object, ...)
## S4 method for signature 'hansentree'
summary(object, ...)
## S4 method for signature 'hansentree'
show(object)
## S4 method for signature 'hansentree'
print(x, ...)
## S4 method for signature 'hansentree'
plot(x, y, regimes, ...)
## S4 method for signature 'hansentree'
simulate(object, nsim = 1, seed = NULL, ...)
## S4 method for signature 'hansentree'
update(object, data, regimes, sqrt.alpha, sigma, ...)
## S4 method for signature 'hansentree'
bootstrap(object, nboot = 200, seed = NULL, ...)
## S4 method for signature 'hansentree'
as(object, class)
## S4 method for signature 'hansentree,data.frame'
coerce(from, to = "data.frame", strict = TRUE)
Arguments
object
The hansentree object.
x
the hansentree object.
class
character;
name of the class to which object should be coerced.
from, to
the classes betwen which coercion should be performed.
nsim
The number of simulations to perform.
nboot
The number of boostraps to perform.
seed
The random seed to use in simulations.
regimes, sqrt.alpha, sigma
See hansen.
data
see hansen.
y, strict
Ignored.
...
Further arguments (either ignored or passed to underlying functions).
In the case of update, these replace the corresponding arguments in the original call.
Methods
plot()
plots the tree, with branches colored according to the selective regimes.
See plot-ouchtree for more details.
print()
prints the tree as a table, along with the coefficients of the fitted model and diagnostic information.
show()
displays the fitted hansentree object.
summary()
displays information on the call, the fitted coefficients, and model selection statistics.
coerce
A hansentree object can be coerced to a data-frame via as(object,"data.frame").
coef()
extracts the coefficients of the fitted model.
This is a list with five elements:
sqrt.alpha:
the coefficients that parameterize the alpha matrix.
sigma:
the coefficients that parameterize the sigma matrix.
theta:
a list of the estimated optima, one per character.
Each element of the list is a vector containing one optimal value per regime.
alpha.matrix:
the alpha matrix itself.
sigma.sq.matrix:
the sigma-squared matrix itself.
logLik()
extracts the log likelihood of the fitted model.
update()
refines the model fit.
bootstrap()
performs a parametric bootstrap for confidence intervals.
simulate()
generates random deviates from the fitted model.
object is the hansentree object, nsim is the desired number of replicates, and seed is (optionally) the random seed to use.
simulate returns a list of data-frames, each comparable to the original data.
providing 
.