ctsem (version 1.1.5.2)

ctFit: Fit a ctsem object

Description

This function fits continuous time SEM models specified via ctModel to a dataset containing one or more subjects.

Usage

ctFit(datawide, ctmodelobj, objective = "auto",
  stationary = c("T0TIPREDEFFECT"), optimizer = "SLSQP",
  retryattempts = 15, iterationSummary = FALSE, carefulFit = TRUE,
  showInits = FALSE, asymptotes = FALSE, meanIntervals = FALSE,
  plotOptimization = F, crossEffectNegStarts = TRUE, nofit = FALSE,
  discreteTime = FALSE, verbose = 0, useOptimizer = TRUE,
  omxStartValues = NULL, transformedParams = TRUE)

Arguments

datawide

the data you wish to fit a ctsem model to.

ctmodelobj

the ctsem model object you wish to use, specified via the ctModel function.

objective

'auto' selects either 'Kalman', if fitting to single subject data, or 'mxRAM' for multiple subjects. For single subject data, 'Kalman' uses the mxExpectationStateSpace function from OpenMx to implement the Kalman filter. For more than on

stationary

Character vector of T0 matrix names in which to constrain any free parameters to stationarity. Defaults to c('T0TIPREDEFFECT'), constraining only the between subject difference effects. Can be set to NULL to force all T0 matrices to be estimated, can b

optimizer

character string, defaults to the open-source 'SLSQP' optimizer that is distributed in all versions of OpenMx. However, 'NPSOL' may sometimes perform better for these problems, though requires that you have installed OpenMx via the OpenMx web site, by r

retryattempts

Number of times to retry the start value randomisation and fit procedure, if non-convergance or uncertain fits occur.

iterationSummary

if TRUE, outputs limited fit details after every fit attempt.

carefulFit

if TRUE, first fits the specified model with a penalised likelihood function to force MANIFESTVAR, DRIFT, TRAITVAR, MANIFESTTRAITVAR parameters to remain close to 0, then fits the specified model normally, using these estimates as starting values. Ca

showInits

if TRUE, prints the list of starting values for free parameters. These are the 'raw' values used by OpenMx, and reflect the log (var / cov matrices) or -log(DRIFT matrices) transformations used in ctsem. These are saved in the fit object under

asymptotes

when TRUE, optimizes over asymptotic parameter matrices instead of continuous time parameter matrices. Can be faster for optimization and in some cases makes reliable convergance easier. Will result in equivalent models when continuous time input matr

meanIntervals

Use average time intervals for each column for calculation (both faster and inaccurate to the extent that intervals vary across individuals).

plotOptimization

If TRUE, uses checkpointing for OpenMx function mxRun, set to checkpoint every iteration, output checkpoint file to working directory, then creates a plot for each parameter's values over iterations.

crossEffectNegStarts

Logical. If TRUE (default) free DRIFT matrix cross effect parameters have starting values set to small negative values (e.g. -.05), if FALSE, the start values are 0. The TRUE setting is useful for easy initialisation of higher order models, while the

nofit

if TRUE, output only openmx model without fitting

discreteTime

Estimate a discrete time model - ignores timing information, parameter estimates will correspond to those of classical vector autoregression models, OpenMx fit object will be directly output, thus ctsem summary and plot functionality will be unavailabl

verbose

Integer between 0 and 3. Sets mxComputeGradientDescent messaging level, defaults to 0.

useOptimizer

Logical. Defaults to TRUE. Passes argument to mxRun, useful for using custom optimizers or fitting to specified parameters.

omxStartValues

A named vector containing the raw (potentially log transformed) OpenMx starting values for free parameters, as captured by OpenMx function omxGetParameters(ctmodelobj$mxobj). These values will take precedence over any starting values alrea

transformedParams

Logical indicating whether or not to log transform parameters internally to allow unconstrained estimation over entire 'sensible' range for parameters. When TRUE (default) raw OpenMx parameters will reflect these transformations and may be harder to in

Examples

## Examples set to 'dontrun' because they take longer than 5s.
mfrowOld<-par()$mfrow
par(mfrow=c(2, 3))

### example from Driver, Oud, Voelkle (2015), 
### simulated happiness and leisure time with unobserved heterogeneity.
data(ctExample1)
traitmodel <- ctModel(n.manifest=2, n.latent=2, Tpoints=6, LAMBDA=diag(2), 
  manifestNames=c('LeisureTime', 'Happiness'), 
  latentNames=c('LeisureTime', 'Happiness'), TRAITVAR="auto")
traitfit <- ctFit(datawide=ctExample1, ctmodelobj=traitmodel)
summary(traitfit)
plot(traitfit, wait=FALSE)


###Example from Voelkle, Oud, Davidov, and Schmidt (2012) - anomia and authoritarianism.  
data(AnomAuth) 
AnomAuthmodel <- ctModel(LAMBDA = matrix(c(1, 0, 0, 1), nrow = 2, ncol = 2), 
Tpoints = 5, n.latent = 2, n.manifest = 2, MANIFESTVAR=diag(0, 2), TRAITVAR = NULL) 
AnomAuthfit <- ctFit(AnomAuth, AnomAuthmodel)
summary(AnomAuthfit)


### Single subject time series - using Kalman filter (OpenMx statespace expectation)
data('ctExample3')
model <- ctModel(n.latent = 1, n.manifest = 3, Tpoints = 100, 
  LAMBDA = matrix(c(1, 'lambda2', 'lambda3'), nrow = 3, ncol = 1), 
  MANIFESTMEANS = matrix(c(0, 'manifestmean2', 'manifestmean3'), nrow = 3, 
    ncol = 1))
fit <- ctFit(data = ctExample3, ctmodelobj = model, objective = 'Kalman', 
  stationary = c('T0VAR'))


###Oscillating model from Voelkle & Oud (2013). 
data("Oscillating")

inits <- c(-38, -.5, 1, 1, .1, 1, 0, .9)
names(inits) <- c("crosseffect","autoeffect", "diffusion",
  "T0var11", "T0var21", "T0var22","m1", "m2")

oscillatingm <- ctModel(n.latent = 2, n.manifest = 1, Tpoints = 11, 
  MANIFESTVAR = matrix(c(0), nrow = 1, ncol = 1), 
  LAMBDA = matrix(c(1, 0), nrow = 1, ncol = 2),
  T0MEANS = matrix(c('m1', 'm2'), nrow = 2, ncol = 1), 
  T0VAR = matrix(c("T0var11", "T0var21", 0, "T0var22"), nrow = 2, ncol = 2),
  DRIFT = matrix(c(0, "crosseffect", 1, "autoeffect"), nrow = 2, ncol = 2), 
  CINT = matrix(0, ncol = 1, nrow = 2),
  DIFFUSION = matrix(c(0, 0, 0, "diffusion"), nrow = 2, ncol = 2),
  startValues = inits)

oscillatingf <- ctFit(Oscillating, oscillatingm,carefulFit=FALSE)