summary.dsem {dsem}

summarize dsem

Description

summarize parameters from a fitted dynamic structural equation model

Usage

## S3 method for class 'dsem'
summary(object, ...)

Arguments

object

Output from dsem

...

Not used

Details

A DSEM is specified using "arrow and lag" notation, which specifies the set of path coefficients and exogenous variance parameters to be estimated. Function dsem then estimates the maximum likelihood value for those coefficients and parameters by maximizing the log-marginal likelihood. Standard errors for parameters are calculated from the matrix of second derivatives of this log-marginal likelihood (the "Hessian matrix").

However, many users will want to associate individual parameters and standard errors with the path coefficients that were specified using the "arrow and lag" notation. This task is complicated in models where some path coefficients or variance parameters are specified to share a single value a priori, or were assigned a name of NA and hence assumed to have a fixed value a priori (such that these coefficients or parameters have an assigned value but no standard error). The summary function therefore compiles the MLE for coefficients (including duplicating values for any path coefficients that assigned the same value) and standard error estimates, and outputs those in a table that associates them with the user-supplied path and parameter names. It also outputs the z-score and a p-value arising from a two-sided Wald test (i.e. comparing the estimate divided by standard error against a standard normal distribution).

Value

Returns a data.frame summarizing estimated path coefficients, containing columns:

path

The parsed path coefficient

lag

The lag, where e.g. 1 means the predictor in time t effects the response in time t+1

name

Parameter name

start

Start value if supplied, and NA otherwise

parameter

Parameter number

first

Variable in path treated as predictor

second

Variable in path treated as response

direction

Whether the path is one-headed or two-headed

Estimate

Maximum likelihood estimate

Std_Error

Estimated standard error from the Hessian matrix

z_value

Estimate divided by Std_Error

p_value

P-value associated with z_value using a two-sided Wald test