emxStateSpaceMixtureClassify: Classify time series in a state space mixture model

In EasyMx: Easy Model-Builder Functions for 'OpenMx'

Classify time series in a state space mixture model

Description

This function classifies time series (usually people) in a state space mixture model.

Usage

emxStateSpaceMixtureClassify(model)

Arguments

model

MxModel. The output from emxStateSpaceMixtureModel

Details

This is a helper function for state space mixture modeling. The function will almost exclusively be used in conjunction with emxStateSpaceMixtureModel. The present function takes a state space mixture model as input, and returns detailed information about the most likely class for each unique ID.

Value

A named list with elements

estimated_classes	A vector of the most likely class for each person. Dimension is people.
joint_m2ll	A matrix of joint minus two summed log likelihoods of each person and each class. Dimension is people by classes.
m2ll	A matrix of minus two summed log likelihoods of each person given each class. Dimension is people by classes.
likelihood	An array of the likelihoods (i.e., probability densities) of each combination of time point, person, and class. Dimension is time points by people by classes.

See Also

emxStateSpaceMixtureModel , emxMixtureModel

Examples

   
# Example
require(EasyMx)
data(myFADataRaw)

ds0 <- myFADataRaw[,1:3]

# Make a VAR Model
vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel')

# Re-label parameters to have different AR parameters
# for class 1 and class 2
vm1 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels,
    newlabels=paste0(vm$Dynamics$labels, '_k1'), name='klass1')
vm2 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels,
    newlabels=paste0(vm$Dynamics$labels, '_k2'), name='klass2')

# Pretend you have a data set of 50 people
# each measured 10 times on 3 variables
ds1 <- myFADataRaw[, 1:3]
ds1$id <- rep(1:50, each=nrow(myFADataRaw)/50)

## Not run: 
# Make the state space mixture model
ssmm <- emxStateSpaceMixtureModel(model=list(vm1, vm2),
    data=ds1, ID='id')

# Fit model
ssmmr <- mxRun(ssmm)

# Extract estimated classes and diagnostics
eclasses <- emxStateSpaceMixtureClassify(ssmmr)

## End(Not run)

EasyMx documentation built on Feb. 16, 2023, 7:27 p.m.