Accelerated EM and Adaptive Quadrature

By default (as of v1.0.9) the LMS approach uses an accelerated EM procedure ("EMA") that uses Quasi-Newton and Fisher Scoring optimization steps when needed. If desireable, this can be switched to the standard Expectation-Maximization (EM) algorithm, by setting algorithm = "EM".

By default the LMS approach also uses a fixed Gauss-Hermite quadrature, to estimate a numerical approximation of the log-likelihood. Instead of a fixed quadrature, it is possible to use a quasi-adaptive quadrature instead. Due to performance reasons, the adaptive quadrature does not fit an individual quadrature to each participant, but instead one for the entire sample (at each EM iteration), based on the whole sample densities of the likelihood function. It essentially works by removing irrelevant nodes which don’t contribute to the integral, and increasing the number of nodes which actually contribute to the integral. This usually means that more nodes are placed towards the center of the distribution, compared to a standard fixed Gauss-Hermite quadrature. Using the EMA and adaptive quadrature might yield estimates that are closer to results from Mplus.

If the model struggles to converge, you can try changing the EM procedure by setting algorithm = "EMA", or algorithm = "EM", and adaptive.quad = TRUE in the modsem() function. Additionally it is possible to tweak these parameters:

• max.iter: Maximum number of iterations for the EM algorithm (default is 500).
• max.step: Maximum number of steps used in the Maximization step of the EM algorithm (default is 1).
• convergence.rel: Relative convergence criterion for the EM algorithm.
• convergence.abs: Absolute convergence criterion for the EM algorithm.
• nodes: Number of nodes for numerical integration (default is 24). Increasing this number can improve the accuracy of the estimates, especially for complex models.
• quad.range: Integration range for quadrature. Smaller ranges means that the integral is more focused. Applies to only when using a quasi-adaptive quadrature.
• adaptive.frequency: How often should the quasi-adaptive quadrature be calculated? Defaults to every third EM iteration.
• adaptive.quad.tol: Relative error tolerance when calculating the quasi-adaptive quadrature.

Here we can see an example using the TPB_UK dataaset, which is more troublesome to estimate than the simulated TPB dataset.

tpb_uk <- "
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att3 + att2 + att1 + att4
  SN =~ sn4 + sn2 + sn3 + sn1
  PBC =~ pbc2 + pbc1 + pbc3 + pbc4
  INT =~ int2 + int1 + int3 + int4
  BEH =~ beh3 + beh2 + beh1 + beh4

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
  BEH ~ INT:PBC
"

fit <- modsem(tpb_uk, 
              data = TPB_UK, 
              method = "lms", 
              nodes = 32, # Number of nodes for numerical integration
              adaptive.quad = TRUE, # Use quasi-adaptive quadrature
              adaptive.frequency = 3, # Update the quasi-adaptive quadrature every third EM-iteration
              adaptive.quad.tol = 1e-12, # Relative error tolerance for quasi-adaptive quad
              algorithm ="EMA", # Use accelerated EM algorithm (Default)
              convergence.abs = 1e-4, # Relative convergence criterion
              convergence.rel = 1e-10, # Relative convergence criterion
              max.iter = 500, # Maximum number of iterations
              max.step = 1) # Maximum number of steps in the maximization step
summary(fit)