modsem_da {modsem}

Interaction between latent variables using lms and qml approaches

Description

modsem_da is a function for estimating interaction effects between latent variables, in structural equation models (SEMs), using distributional analytic (DA) approaches. Methods for estimating interaction effects in SEM's can basically be split into two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). modsem_da() handles the latter, and can estimate models using both qml and lms necessary syntax, and variables for the estimation of models with latent product indicators. NOTE: run 'default_settings_da()' to see default arguments.

Usage

modsem_da(
  model.syntax = NULL,
  data = NULL,
  method = "lms",
  verbose = NULL,
  optimize = NULL,
  nodes = NULL,
  convergence = NULL,
  optimizer = NULL,
  center.data = NULL,
  standardize.data = NULL,
  standardize.out = NULL,
  standardize = NULL,
  mean.observed = NULL,
  cov.syntax = NULL,
  double = NULL,
  calc.se = NULL,
  FIM = NULL,
  EFIM.S = NULL,
  OFIM.hessian = NULL,
  EFIM.parametric = NULL,
  robust.se = NULL,
  max.iter = NULL,
  max.step = NULL,
  fix.estep = NULL,
  start = NULL,
  epsilon = NULL,
  quad.range = NULL,
  ...
)

Arguments

model.syntax

lavaan syntax

data

dataframe

method

method to use: "lms" = laten model structural equations (not passed to lavaan). "qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan).

verbose

should estimation progress be shown

optimize

should starting parameters be optimized

nodes

number of quadrature nodes (points of integration) used in lms, increased number gives better estimates but slower computation. How many is needed, depends on the complexity of the model For simple models you somwhere between 16-24 should be enough, for more complex higher numbers may be needed. For models where there is an interaction effects between and endogenous and exogenous variable the number of nodes should at least be 32, but practically (e.g., ordinal/skewed data) more than 32 is recommended. In cases, where data is non-normal it might be better to use the qml approach instead. For large numbers of nodes, you might want to change the 'quad.range' argument.

convergence

convergence criterion. Lower values give better estimates but slower computation.

optimizer

optimizer to use, can be either "nlminb" or "L-BFGS-B". For LMS, "nlminb" is recommended. For QML, "L-BFGS-B" may be faster if there is a large number of iterations, but slower if there are few iterations.

center.data

should data be centered before fitting model

standardize.data

should data be scaled before fitting model, will be overridden by standardize if standardize is set to TRUE. NOTE: It is recommended that you estimate the model normally and then standardize the output using 'standardized_estimates()'.

standardize.out

should output be standardized (note will alter the relationsships of parameter constraints, since to parameters are scaled unevenly, even if they have the same label). This does not alter the estimation of the model, only the output. NOTE: It is recommended that you estimate the model normally and then standardize the output using 'standardized_estimates()'.

standardize

will standardize the data before fitting the model, remove the mean structure of the observed variables, and standardize the output. Note that standardize.data mean.observed, standardize.out will be overridden by standardize if standardize is set to TRUE. NOTE: It is recommended that you estimate the model normally and then standardize the output using 'standardized_estimates()'.

mean.observed

should mean structure of the observed variables be estimated, will be overridden by standardize if standardize is set to TRUE. NOTE: Not recommended unless you know what you are doing.

cov.syntax

model syntax for implied covariance matrix (see 'vignette("interaction_two_etas", "modsem")')

double

try to double the number of dimensions of integrations used in LMS, this will be extremely slow, but should be more similar to mplus.

calc.se

should standard errros be computed, NOTE: If 'FALSE' information matrix will not be computed either

FIM

should fisher information matrix be calculated using observed of expected. must be either "observed" or "expected"

EFIM.S

if expected fisher information matrix is computed, EFIM.S selects the sample size of the generated data

OFIM.hessian

should observed fisher information be computed using hessian? if FALSE, it is computed using gradient

EFIM.parametric

should data for calculating expected fisher information matrix be simulated parametrically (simulated based on the assumptions- and implied parameters from the model), or non-parametrically (stochastically sampled). If you believe that normality assumptions are violated, 'EFIM.parametric = FALSE' might be the better option.

robust.se

should robust standard errors be computed? Meant to be used for QML, can be unreliable with the LMS-approach.

max.iter

max numebr of iterations

max.step

max steps for the M-step in the EM algorithm (LMS)

fix.estep

if TRUE, E-step will be fixed and the prior probabilities are set to the best prior probabilities, if loglikelihood is decreasing for more than 30 iterations.

start

starting parameters

epsilon

finite difference for numerical derivatives

quad.range

range in z-scores to perform numerical integration in LMS using Gaussian-Hermite Quadratures. By default Inf, such that f(t) is integrated from -Inf to Inf, but this will likely be inefficient and pointless at large number of nodes. Nodes outside +/- quad.range will be ignored.

...

additional arguments to be passed to the estimation function

Value

modsem_lms or modsem_qml object

Examples

library(modsem)
# For more examples check README and/or GitHub.
# One interaction
m1 <- "
  # Outer Model
  X =~ x1 + x2 +x3
  Y =~ y1 + y2 + y3
  Z =~ z1 + z2 + z3

  # Inner model
  Y ~ X + Z + X:Z
"

## Not run: 
# QML Approach
est1 <- modsem_da(m1, oneInt, method = "qml")
summary(est1)


# Theory Of Planned Behavior
tpb <- "
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

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

# lms approach
estTpb <- modsem_da(tpb, data = TPB, method = lms)
summary(estTpb)

## End(Not run)