Methods of the "hansentree" class.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  ## 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)

object 
The 
x 
the 
class 
character;
name of the class to which 
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 
data 
see 
y, strict 
Ignored. 
... 
Further arguments (either ignored or passed to underlying functions).
In the case of 
plot()
plots the tree, with branches colored according to the selective regimes. See plotouchtree 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.
A hansentree
object can be coerced to a dataframe 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 sigmasquared 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 dataframes, each comparable to the original data.
Aaron A. King kingaa at umich dot edu
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