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mlt (version 1.0-2)

mlt-methods: Methods for mlt Objects

Description

Methods for objects of class mlt

Usage

# S3 method for mlt
coef(object, fixed = TRUE, ...)
coef(object) <- value
# S3 method for mlt
weights(object, ...)
# S3 method for mlt
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata, ...)
# S3 method for mlt
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
Hessian(object, ...)
# S3 method for mlt
Hessian(object, parm = coef(object, fixed = FALSE), ...)
Gradient(object, ...)
# S3 method for mlt
Gradient(object, parm = coef(object, fixed = FALSE), ...)
# S3 method for mlt
estfun(object, parm = coef(object, fixed = FALSE),
       w = NULL, newdata, ...)
# S3 method for mlt
mkgrid(object, n, ...)
# S3 method for mlt
bounds(object)
# S3 method for mlt
variable.names(object, ...)
# S3 method for mlt_fit
update(object, weights, subset = NULL, theta, ...)
# S3 method for mlt
as.mlt(object)

Arguments

object

a fitted conditional transformation model as returned by mlt

fixed

a logical indicating if only estimated coefficients (fixed = FALSE) should be returned

value

coefficients to be assigned to the model

parm

model parameters

w

model weights

weights

model weights

newdata

an optional data frame of new observations. Allows evaluation of the log-likelihood for a given model object on these new observations. The parameters parm and w are ignored in this situation.

n

number of grid points

subset

an optional integer vector indicating the subset of observations to be used for fitting.

theta

optional starting values for the model parameters

complete

currently ignored

…

additional arguments

Details

coef can be used to get and set model parameters, weights and logLik extract weights and evaluate the log-likelihood (also for parameters other than the maximum likelihood estimate). Hessian returns the Hessian and vcov the inverse thereof. Gradient gives the gradient (sum of the score contributions) and estfun the score contribution by each observation. mkgrid generates a grid of all variables (as returned by variable.names) in the model. update allows refitting the model with alternative weights and potentially different starting values. bounds gets bounds for bounded variables in the model.