GLMresponse {depmixS4} | R Documentation |
Methods for creating depmix response models
Description
Create GLMresponse
objects for depmix
models using
formulae and family objects.
Usage
GLMresponse(formula, data=NULL, family=gaussian(), pstart=NULL,
fixed=NULL, prob=TRUE, ...)
## S4 method for signature 'response'
getdf(object)
Arguments
formula |
A model |
data |
An optional data.frame to interpret the variables from the formula argument in. |
family |
A family object; |
pstart |
Starting values for the coefficients and other parameters, e.g. the standard deviation for the gaussian() family. |
fixed |
Logical vector indicating which paramters are to be fixed. |
prob |
Logical indicating whether the starting values for multinomial() family models are probabilities or logistic parameters (see details). |
object |
Object of class response. |
... |
Not used currently. |
Details
GLMresponse
is the default driver for specifying response
distributions of depmix
models. It uses the familiar formula
interface from glm
to specify how responses depend on
covariates/predictors.
Currently available options for the family argument are
binomial
, gaussian
, poisson
, Gamma
, and
multinomial
. Except for the latter option, the
GLMresponse
model is an interface to the glm
functions of
which the functionality is used: predict, fit and density functions.
The multinomial
model takes as link functions mlogit
, the
default, and then uses functionality from the nnet
package to
fit multinomial logistic models; using mlogit
as link allows
only n=1 models to be specified, i.e. a single observation for each
occasion; it also takes identity
as a link function. The latter
is typically faster and is hence preferred when no covariates are
present.
See the responses
help page for examples.
Value
GLMresponse
returns an object of class GLMresponse
which
extends the response-class
.
getdf
returns the number of free parameters of a
response
model.
Author(s)
Ingmar Visser & Maarten Speekenbrink
See Also
makeDepmix
has an example of specifying a model with a
multivariate normal response and an example of how to add a user-defined
response model, in particular an ex-gauss distribution used for the
speed
data.