impacts_matrix: Compute spatial impacts matrix

In LukeCe/spflow: Spatial Econometric Interaction Models

Compute spatial impacts matrix

Description

Marginal effect in spatial econometric models are observation dependent. Here we compute the changes to all observations that follow from changes in a one variable for one (or multiple) observations. Here, these effects are represented as a matrix with the same dimension as the dependent variable.

Usage

## S4 method for signature 'spflow_model'
impacts_matrix(
  object,
  ...,
  change_net,
  change_var,
  change_obs = 1L,
  x_is_log = FALSE
)

Arguments

object	an spflow_model-class()
...	arguments passed on to predict_effect()
change_net	a character of length one, identifying one of the spflow_network_classes() where changes to the data should be applied
change_var	a character of length one, identifying one variable The character musts refer to the variable and its name to the id of
change_obs	A numeric that identifies the observation(s) to change (by position)
x_is_log	A logical, if TRUE the independent variable is assumed to be in logs and the effects are computed for a relative change of the form x_new = 1.01 * x_old. If FALSE the effects are computed for a unit increase of the form x_new = 1 + x_old.

Value

A Matrix

Author(s)

Lukas Dargel

See Also

predict_effect

Examples

model9 <- spflow(
spflow_formula = y9 ~ . + P_(DISTANCE),
spflow_networks = multi_net_usa_ge,
id_net_pair = "ge_ge")

# effect matrix, summary and decomposition
MI <- impacts_matrix(model9,change_net = "ge",change_var =  "X", change_obs = 8)
total_effect <- sum(MI)
intra_effect <- MI[8,8]
origi_effect <- sum(MI[,8]) - intra_effect
desti_effect <- sum(MI[8,]) - intra_effect
netwo_effect <- sum(MI[-8,-8])
total_effect == (intra_effect + origi_effect + desti_effect + netwo_effect)

LukeCe/spflow documentation built on Nov. 11, 2023, 8:20 p.m.