Description Usage Arguments Details Value See Also
Estimation of parameters in a multivariate process model. The function fits the models by minimizing an l1-penalized squared error loss by a coordinate wise descent algorithm. Alternatively, the function can do unpenalized least squares forward or backward model search.
1 2 3 4 |
model |
|
method |
a |
scope |
a |
... |
additional arguments passed on to the optimization algorithm. |
The stepwise model search algorithms as well as the algorithm for l1-panalized estimation return a sequence of models parametrized by a vector of tuning parameters. For the model search, the tuning parameter is the dimension of the model (the number of nonzero entries in the parameter vector). For the l1-panalized models, the tuning parameter is a sequence of penalty parameters.
The scope argument specifies the smallest and largest model fitted. They must be nested. The zero weights
of the multModel
object are used to specify the smallest model, if the scope argument does not
contain a smallest model.
a multModel
object with the additional elements
lambda | the sequence of tuning parameters |
Blambda | the corresponding matrix of estimated parameters. The i'th column corresponds to the i'th tuning parameter. |
status | either 0 (meaning no errors), 1 (convergence), 2 (MLE not computed) or 3 (other errors) indicating different problems or errors |
msg | errors encountered or other notable conditions |
multModel
, coordinateDescent
, coordinateDescentMF
,
stepOptim
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.