qml: Quasi-maximum likelihood estimation of a nonlinear structural...

In nlsem: Fitting Structural Equation Mixture Models

Quasi-maximum likelihood estimation of a nonlinear structural equation model

Description

Fits a structural equation model with latent interaction effects using Quasi-maximum likelihood estimation.

Usage

  qml(model, data, start, max.iter = 150, optimizer = c("nlminb",
      "optim"), neg.hessian = TRUE, ...) 

Arguments

model	a specified structural equation model of class singleClass.
data	the data the model should be fitted to. Data needs to be a matrix and variables need to be in the order x1, x2, ..., y1, y2, ... as specified in specify_sem. Data matrix needs no column names (will be ignored anyways).
start	starting values for parameters.
max.iter	maximum number of iterations for optimizer.
optimizer	which optimizer should be used for maximization of parameters: nlminb or optim.
neg.hessian	should negative Hessian be calculated.
...	additional arguments. See Details.

Details

Additional arguments can be passed to ... for these optimizers. See documentation for optim and nlminb.

Quasi-maximum likelihood (QML) estimation is in principle a faster version for LMS, but might be less accurate for normal data. For practical purposes differences are negligible, though. For nonnormal data QML outperforms LMS.

Value

An object of class qmlEst that consists of the following components:

model.class	class of model that was fitted. Will always be singleClass.
coefficients	estimated parameters.
objective	final loglikelihood obtained with EM algorithm.
convergence	convergence code for optimizer. See documentation for optim and nlminb.
Hessian	negative Hessian matrix for final parameter estimation.
info	list of number of exogenous (num.xi) and endogenous (num.eta) variables and of indicators (num.x and num.y). Corresponds to specifications given to specify_sem when specifiying structural equation model.

References

Klein, A. &, Muthen, B. O. (2007). Quasi-Maximum Likelihood Estimation of Structural Equation Models With Multiple Interaction and Quadratic Effects. Multivariate Behavior Research, 42, 647–673. doi:http://dx.doi.org/10.1080/00273170701710205

See Also

specify_sem

Examples

# specify model of class singleClass
sc <- specify_sem(num.x=4, num.y=2, num.xi=2, num.eta=1, xi="x1-x2,x3-x4",
                  eta="y1-y2", interaction="eta1~xi1:xi2") 

# simulate data
pars.orig <- c(0.6, 0.7,                 # Lx
               0.8,                      # Ly
               0.2, 0.4,                 # G
               0.25, 0.25, 0.25, 0.25,   # Td
               0.25, 0.25,               # Te
               0.2,                      # Psi
               0.49, 0.235, 0.64,        # Phi
               0, 0,                     # nu.x
               0,                        # nu.x
               1,                        # alpha
               1, 1,                     # tau
               0.7                       # Omega
              )

dat <- simulate(sc, parameters=pars.orig, seed=81)

# fit model
set.seed(1609)
start <- runif(count_free_parameters(sc))
## Not run: 
qml1 <- qml(sc, dat, start)
summary(qml1)

## End(Not run)

nlsem documentation built on Aug. 31, 2023, 5:14 p.m.