mixed_LICORS
is the core function of this package
as it estimates the “parameters” in the model for the
spatiotemporal process.
P(X_1, …, X_{\tilde{N}}) \propto ∏_{i=1}^{N} P(X_i \mid \ell^{}_i) = ∏_{i=1}^{N} P(X_i \mid ε(\ell^{}_i)) .
1 2 3 4 5 6  mixed_LICORS(LCs = list(PLC = NULL, FLC = NULL, dim = list(original = NULL,
truncated = NULL)), num.states.init = NULL, initialization = NULL,
control = list(max.iter = 500, alpha = 0.01, trace = 0, lambda = 0,
sparsity = "stochastic", CV.split.random = FALSE, CV.train.ratio = 0.75,
seed = NULL, loss = function(x, xhat) mean((x  xhat)^2),
estimation.method = list(PLC = "normal", FLC = "nonparametric")))

LCs 
list of PLCs and FLCs matrices (see output of

num.states.init 
number of states to start the EM algorithm 
initialization 
a a) character string, b) vector,
or c) matrix. a) results 
control 
a list of control settings for the EM
algorithm. See 
An object of class "LICORS"
.
plot.mixed_LICORS
,
summary.mixed_LICORS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Not run:
data(contCA00)
LC_geom < setup_LC_geometry(speed = 1, horizon = list(PLC = 2, FLC = 0),
shape = "cone")
bb < data2LCs(t(contCA00$observed), LC.coordinates = LC_geom$coordinates)
mm < mixed_LICORS(bb, num.states.init = 15, init = "KmeansPLC",
control = list(max.iter = 50, lambda = 0.001))
plot(mm)
ff_new < estimate_LC_pdfs(bb$FLC, weight.matrix = mm$conditional_state_probs,
method = "nonparametric")
matplot(bb$FLC, ff_new, pch = ".", cex = 2)
## End(Not run)

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