Methods for mlt Objects
Methods for objects of class mlt
## 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(object, parm = coef(object, fixed = FALSE),
w = NULL, newdata, ...)
## 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),
...)
## S3 method for class 'mlt'
as.mlt(object)object |
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 |
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 |
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.
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