The "mixed_LICORS" class is the objectput from the
mixed_LICORS estimator.
plot.mixed_LICORS gives a visual summary of the
estimates such as marginal state probabilities,
conditional state probabilities (= weight matrix),
predictive state densities, trace plots for
log-likelihood/loss/penalty.
summary.mixed_LICORS prints object a summary of
the estimated LICORS model.
predict.mixed_LICORS predicts FLCs based on PLCs
given a fitted mixed LICORS model. This can be done on an
iterative basis, or for a selection of future PLCs.
complete_LICORS_control completes the controls for
the mixed LICORS estimator. Entries of the list are:
'loss' an R function specifying the loss for
cross-validation (CV). Default: mean squared error (MSE),
i.e. loss = function(x, xhat) mean((x-xhat)^2)
'method' a list of length 2 with arguments
PLC and FLC for the method of density
estimation in each (either "normal" or
"nonparametric").
'max.iter' maximum number of iterations in the EM
'trace' if > 0 it prints output in the console as the EM
is running
'sparsity' what type of sparsity (currently not
implemented)
'alpha' significance level to stop testing. Default:
alpha = 0.01
'seed' set seed for reproducibility. Default:
NULL. If NULL it sets a random seed and
then returns this seed in the output.
'CV.train.ratio' how much of the data should be training
data. Default: 0.75, i.e., 75% of data is
for training
'CV.split.random' logical; if TRUE training and
test data are split randomly; if FALSE (default)
it uses the first part (in time) as training, rest as
test.
'estimation' a list of length 2 with arguments
PLC and FLC for the method of density
estimation in each (either "normal" or
"nonparametric").
Usage
## S3 method for class 'mixed_LICORS'
plot(x, type = "both", cex.axis = 1.5, cex.lab = 1.5,
cex.main = 2, line = 1.5, ...)
## S3 method for class 'mixed_LICORS'
summary(object, ...)
## S3 method for class 'mixed_LICORS'
predict(object, new.LCs = list(PLC = NULL), ...)
complete_LICORS_control(control = list(alpha = 0.01, CV.split.random = FALSE,
CV.train.ratio = 0.75, lambda = 0, max.iter = 500, seed = NULL,
sparsity = "stochastic", trace = 0, loss = function(x, xhat) mean((x -
xhat)^2), estimation.method = list(PLC = "normal", FLC = "nonparametric")))
Arguments
x
object of class "mixed_LICORS"
type
should only "training", "test",
or "both" be plotted. Default: "both".
cex.axis
The magnification to be used for axis
annotation relative to the current setting of
cex.
cex.lab
The magnification to be used for x and y
labels relative to the current setting of cex.
cex.main
The magnification to be used for main
titles relative to the current setting of cex.
line
on which margin line should the labels be
ploted, starting at 0 counting objectwards (see also
mtext).
...
optional arguments passed to plot,
summary, or predict
object
object of class "mixed_LICORS"
new.LCs
a list with PLC configurations to predict
FLCs given these PLCs
control
a list of controls for
"mixed_LICORS".
Examples
# see examples of LICORS-package see examples in LICORS-package see examples in
# LICORS-package see examples in LICORS-package
providing 
.