ctsem (version 3.0.4)

summary.ctsemFit: Summary function for ctsemFit object

Description

Provides summary details for ctsemFit objects.

Usage

# S3 method for ctsemFit
summary(object, ridging = FALSE, timeInterval = 1,
  verbose = FALSE, ...)

Arguments

object

ctsemFit object as generated by ctFit.

ridging

if TRUE, adds a small amount of variance to diagonals when calculating standardised (correlation) matrices, should only be used if standardised matrices return NAN.

timeInterval

positive numeric value specifying time interval to use for discrete parameter matrices, defaults to 1.

verbose

Logical. If TRUE, displays the raw, internally transformed (when fitting with default arguments) OpenMx parameters and corresponding standard errors, as well as additional summary matrices. Parameter transforms are described in the vignette, vignette('ctsem'). Additional summary matrices include: 'discrete' matrices -- matrices representing the effect for the given time interval (default of 1); 'asymptotic' matrices -- represents the effect as time interval approaches infinity (therefore asymCINT describes mean level of processes at the asymptote, asymDIFFUSION describes total within- subject variance at the asymptote, etc); 'standardised' matrices -- transforms covariance matrices to correlation matrices, and transforms discreteDRIFT based on DIFFUSION, to give effect sizes.

...

additional parameters to pass.

Value

Summary of ctsemFit object

Details

Important: Although ctModel takes cholesky decomposed variance-covariance matrices as input, the summary function displays the full variance-covariance matrices. These can be cholesky decomposed for comparison purposes using t(chol(summary(ctfitobject)$covariancematrix)). Standard errors are displayed in the $ctparameters section, however if ctFit was used with transformedParams=TRUE (the default, and recommended) covariance matrix standard errors will have been approximated using the delta method. For inferential purposes, maximum likelihood confidence intervals may be estimated using the ctCI function.

Examples

# NOT RUN {
## Examples set to 'donttest' because they take longer than 5s. 

# }
# NOT RUN {
### 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(dat=ctExample1, ctmodelobj=traitmodel)
summary(traitfit,timeInterval=1)
# }