dsemRTMB: Fit dynamic structural equation model
In dsem: Dynamic Structural Equation Models

dsemRTMB

R Documentation

Fit dynamic structural equation model

Description

Fits a dynamic structural equation model

Usage

dsemRTMB(
  sem,
  tsdata,
  family = rep("fixed", ncol(tsdata)),
  estimate_delta0 = FALSE,
  log_prior = function(p) 0,
  control = dsem_control(),
  covs = colnames(tsdata)
)

Arguments

`sem`	Specification for time-series structural equation model structure including lagged or simultaneous effects. See Details section in `make_dsem_ram` for more description
`tsdata`	time-series data, as outputted using `ts`, with `NA` for missing values.
`family`	Character-vector listing the distribution used for each column of `tsdata`, where each element must be `fixed` (for no measurement error), `normal` for normal measurement error using an identity link, `gamma` for a gamma measurement error using a fixed CV and log-link, `bernoulli` for a Bernoulli measurement error using a logit-link, or `poisson` for a Poisson measurement error using a log-link. `family="fixed"` is default behavior and assumes that a given variable is measured exactly. Other options correspond to different specifications of measurement error.
`estimate_delta0`	Boolean indicating whether to estimate deviations from equilibrium in initial year as fixed effects, or alternatively to assume that dynamics start at some stochastic draw away from the stationary distribution
`log_prior`	A user-provided function that takes as input the list of parameters `out$obj$env$parList()` where `out` is the output from `dsemRTMB()`, and returns the log-prior probability. For example `log_prior = function(p) dnorm( p$beta_z[1], mean=0, sd=0.1, log=TRUE)` specifies a normal prior probability for the first path coefficient with mean of zero and sd of 0.1. Note that the user must load RTMB using `library(RTMB)` prior to running the model.
`control`	Output from `dsem_control`, used to define user settings, and see documentation for that function for details.
`covs`	optional: a character vector of one or more elements, with each element giving a string of variable names, separated by commas. Variances and covariances among all variables in each such string are added to the model. Warning: covs="x1, x2" and covs=c("x1", "x2") are not equivalent: covs="x1, x2" specifies the variance of x1, the variance of x2, and their covariance, while covs=c("x1", "x2") specifies the variance of x1 and the variance of x2 but not their covariance. These same covariances can be added manually via argument `sem`, but using argument `covs` might save time for models with many variables.

Details

dsemRTMB is interchangeable with dsem, but uses RTMB instead of TMB for estimation. Both are provided for comparison and real-world comparison. See ?dsem for more details