dsem (version 1.6.0)

total_effect: Calculate total effects

Description

Calculate a data frame of total effects, representing the estimated effect of every variable on every other variable and any time-lag from 0 (simultaneous effects) to a user-specified maximum lag.

Usage

total_effect(object, n_lags = 4)

Value

A data frame listing the time-lag (lag), variable that is undergoing some exogenous change (from), and the variable being impacted (to), along with the total effect (total_effect) including direct and indirect pathways, and the partial "direct" effect (direct_effect)

Arguments

object

Output from dsem

n_lags

Number of lags over which to calculate total effects

Details

Total effects are taken from the Leontief matrix \(\mathbf{(I-P)^{-1}}\), where \(\mathbf{P}\) is the path matrix across variables and times. This calculates the effect of a pulse perturbation at lag=0 for a given variable (from) upon any other variable (to) either in the same time (lag=0), or subsequent times (lag >= 1).

Examples

# Define linear model with slope of 0.5
sem = "
  # from, to, lag, name, starting_value
  x -> y, 0, slope, 0.5
"
# Build DSEM with specified value for path coefficients
mod = dsem(
  sem = sem,
  tsdata = ts(data.frame(x=rep(0,20),y=rep(0,20))),
  control = dsem_control( run_model = FALSE )
)
# Show that total effect of X on Y is 0.5 but does not propagate over time
total_effect(mod, n_lags = 2)

# Define linear model with slope of 0.5 and autocorrelated response
sem = "
  x -> y, 0, slope, 0.5
  y -> y, 1, ar_y, 0.8
"
mod = dsem(
  sem = sem,
  tsdata = ts(data.frame(x=rep(0,20),y=rep(0,20))),
  control = dsem_control( run_model = FALSE )
)
# Show that total effect of X on Y is 0.5 with decay of 0.8 for each time
total_effect(mod, n_lags = 4)