semTools (version 0.4-14)

lavaanStar-class: Class For Representing A (Fitted) Latent Variable Model with Additional Elements

Description

This is the lavaan class that contains additional information about the fit values from the null model. Some functions are adjusted according to the change.

Objects from the Class

Objects can be created via the auxiliary function or runMI.

Slots

call:

The function call as returned by match.called().

timing:

The elapsed time (user+system) for various parts of the program as a list, including the total time.

Options:

Named list of options that were provided by the user, or filled-in automatically.

ParTable:

Named list describing the model parameters. Can be coerced to a data.frame. In the documentation, this is called the `parameter table'.

Data:

Object of internal class "Data": information about the data.

SampleStats:

Object of internal class "SampleStats": sample statistics

Model:

Object of internal class "Model": the internal (matrix) representation of the model

Fit:

Object of internal class "Fit": the results of fitting the model

nullfit:

The fit-indices information from the null model

imputed:

The list of information from running multiple imputation. The first element is the convergence rate of the target and null models. The second element is the fraction missing information. The first estimate of FMI (FMI.1) is asymptotic FMI and the second estimate of FMI (FMI.2) is corrected for small numbers of imputation. The third element is the fit values of the target model by the specified chi-squared methods. The fourth element is the fit values of the null model by the specified chi-square methods. The fifth element is the adjusted log-likelihood for target model and satuated model. The sixth element is the chi-square values and the log-likehood values (based on fixing parameter estimates as the estimated values) from each imputed data set.

imputedResults:

Results from fitting models for imputed data sets.

auxNames:

The list of auxiliary variables in the analysis.

References

see '>lavaan

See Also

auxiliary; runMI

Examples

# NOT RUN {
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '
			  
dat <- data.frame(HolzingerSwineford1939, z=rnorm(nrow(HolzingerSwineford1939), 0, 1))
			  
fit <- cfa(HS.model, data=dat) 
fitaux <- auxiliary(HS.model, aux="z", data=dat, fun="cfa")
# }