# mixed_LICORS: Mixed LICORS: An EM-like Algorithm for Predictive State Space... In LICORS: Light Cone Reconstruction of States - Predictive State Estimation From Spatio-Temporal Data

## Description

mixed_LICORS is the core function of this package as it estimates the “parameters” in the model for the spatio-temporal 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)) .

## Usage

 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"))) 

## Arguments

 LCs list of PLCs and FLCs matrices (see output of data2LCs for details and formatting). num.states.init number of states to start the EM algorithm initialization a a) character string, b) vector, or c) matrix. a) results num.states.init many states initialized by passing the character string as method argument of initialize_states; if b) the vector will be taken as initial state labels; if c) the matrix will be taken as initial weights. Note that for both b) and c) num.states.init will be ignored. k = 1, …, K of PLC i control a list of control settings for the EM algorithm. See complete_LICORS_control for details.

## Value

An object of class "LICORS".

## See Also

plot.mixed_LICORS, summary.mixed_LICORS

## Examples

  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) 

LICORS documentation built on May 29, 2017, 1:02 p.m.