mlt-methods {mlt} | R Documentation |
Methods for mlt Objects
Description
Methods for objects of class mlt
Usage
## S3 method for class 'mlt'
coef(object, fixed = TRUE, ...)
coef(object) <- value
## S3 method for class 'mlt'
weights(object, ...)
## S3 method for class 'mlt'
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata, ...)
## S3 method for class 'mlt'
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
Hessian(object, ...)
## S3 method for class 'mlt'
Hessian(object, parm = coef(object, fixed = FALSE), ...)
Gradient(object, ...)
## S3 method for class 'mlt'
Gradient(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mlt'
estfun(x, parm = coef(x, fixed = FALSE),
w = NULL, newdata, ...)
## S3 method for class 'mlt'
residuals(object, parm = coef(object, fixed = FALSE),
w = NULL, newdata, what = c("shifting", "scaling"), ...)
## S3 method for class 'mlt'
mkgrid(object, n, ...)
## S3 method for class 'mlt'
bounds(object)
## S3 method for class 'mlt'
variable.names(object, ...)
## S3 method for class 'mlt_fit'
update(object, weights = stats::weights(object),
subset = NULL, offset = object$offset, theta = coef(object, fixed = FALSE),
fixed = NULL, ...)
## S3 method for class 'mlt'
as.mlt(object)
Arguments
object , x |
a fitted conditional transformation model as returned by |
fixed |
a logical indicating if only estimated coefficients ( |
value |
coefficients to be assigned to the model |
parm |
model parameters |
w |
model weights |
what |
type of residual: |
weights |
model weights |
newdata |
an optional data frame of new observations. Allows
evaluation of the log-likelihood for a given
model |
n |
number of grid points |
subset |
an optional integer vector indicating the subset of observations to be used for fitting. |
offset |
an optional vector of offset values |
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 (of the negative log-likelihood) and vcov
the inverse thereof. Gradient
gives the negative gradient (minus sum of the score contributions)
and estfun
the negative 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.