Description Usage Arguments Examples
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
loglikelihood/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
crossvalidation (CV). Default: mean squared error (MSE),
i.e. loss = function(x, xhat) mean((xxhat)^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)
'lambda' penalization parameter; larger lambda gives sparser weights
'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"
).
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## 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")))

x 
object of class 
type 
should only 
cex.axis 
The magnification to be used for axis
annotation relative to the current setting of

cex.lab 
The magnification to be used for x and y
labels relative to the current setting of 
cex.main 
The magnification to be used for main
titles relative to the current setting of 
line 
on which margin line should the labels be
ploted, starting at 0 counting objectwards (see also

... 
optional arguments passed to 
object 
object of class 
new.LCs 
a list with PLC configurations to predict FLCs given these PLCs 
control 
a list of controls for

1 2  # see examples of LICORSpackage see examples in LICORSpackage see examples in
# LICORSpackage see examples in LICORSpackage

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