impacts.ols_sphet: Generate impacts for objects of class ols_sphet created in...

In gpiras/sphet: Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations

Generate impacts for objects of class ols_sphet created in sphet

Description

Generate impacts for objects of class ols_sphet created in sphet

Usage

## S3 method for class 'ols_sphet'
impacts(
  obj,
  ...,
  tr = NULL,
  R = NULL,
  listw = NULL,
  evalues = NULL,
  tol = 1e-06,
  empirical = FALSE,
  Q = NULL
)

Arguments

obj	A spreg spatial regression object created by spreg with model ="lag"
...	Arguments passed through to methods in the coda package
tr	A vector of traces of powers of the spatial weights matrix created using trW, for approximate impact measures; if not given, listw must be given for exact measures (for small to moderate spatial weights matrices); the traces must be for the same spatial weights as were used in fitting the spatial regression
R	If given, simulations are used to compute distributions for the impact measures, returned as mcmc objects
listw	a listw object
evalues	vector of eigenvalues of spatial weights matrix for impacts calculations
tol	Argument passed to mvrnorm: tolerance (relative to largest variance) for numerical lack of positive-definiteness in the coefficient covariance matrix
empirical	Argument passed to mvrnorm (default FALSE): if true, the coefficients and their covariance matrix specify the empirical not population mean and covariance matrix
Q	default NULL, else an integer number of cumulative power series impacts to calculate if tr is given

Value

Estimate of the Average Total, Average Direct, and Average Indirect Effects

Examples

data(boston, package="spData")
Wb <- as(spdep::nb2listw(boston.soi), "CsparseMatrix") 
ev <- eigen(Wb)$values
trMatb <- spatialreg::trW(Wb, type="mult")

lm.D <- spreg(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +  AGE + log(DIS), 
          data = boston.c, listw = Wb, model = "ols", Durbin =  TRUE)
summary(lm.D)
impacts(lm.D)
summary(impacts(lm.D))

lm.D2 <- spreg(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +  AGE + log(DIS), 
               data = boston.c, listw = Wb, model = "ols", Durbin =  ~AGE)
summary(lm.D2)
impacts(lm.D2)
summary(impacts(lm.D2))

lm.D3 <- spreg(log(CMEDV) ~ CRIM + ZN +  CHAS + I(NOX^2) + I(RM^2) +  AGE, 
               data = boston.c, listw = Wb, model = "ols", Durbin =  ~AGE + INDUS )
summary(lm.D3)
impacts(lm.D3)
summary(impacts(lm.D3))

require("sf", quietly=TRUE)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
col.gal.nb <- spdep::read.gal(system.file("weights/columbus.gal", package="spData")[1])
listw <- spdep::nb2listw(col.gal.nb)
knear <- spdep::knearneigh(cbind(columbus$X, columbus$Y), 5)
knb <- spdep::knn2nb(knear)
dist <- spdep::nbdists(knb, cbind(columbus$X, columbus$Y))
k5d <- spdep::nb2listw(knb, glist = dist, style = "B")
class(k5d) <- c("listw", "nb", "distance")
lm.D4 <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin=TRUE,
               model = "ols")
summary(lm.D4)
impacts(lm.D4)

lm.D5 <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin= ~ INC,
               model = "ols")
summary(lm.D5)
impacts(lm.D5)
summary(impacts(lm.D5))

lm.D6 <- spreg(CRIME ~ HOVAL, columbus, listw, Durbin= ~ INC,
               model = "ols")
summary(lm.D6)
summary(impacts(lm.D6))
## Not run: 
lm.D7 <- spreg(CRIME ~ INC + HOVAL, columbus, listw,
                 model = "ols", HAC = TRUE, distance = k5d, 
                                  type = "Triangular")
summary(lm.D7)
impacts(lm.D7)
summary(impacts(lm.D7))

## End(Not run)
lm.D8 <- spreg(CRIME ~ INC + HOVAL, data = columbus, listw = listw, Durbin=TRUE,
               model = "ols", distance = k5d, type = "Triangular")
summary(lm.D8)
impacts(lm.D8)
summary(impacts(lm.D8))


lmD.9 <- spreg(CRIME ~ INC + HOVAL, data = columbus, listw = listw, Durbin= ~ INC,
               model = "ols", distance = k5d, type = "Parzen")
               
impacts(lmD.9)

lmD.10 <- spreg(CRIME ~ HOVAL, columbus, listw, Durbin= ~ INC,
                model = "ols", distance = k5d, type = "Bisquare")
summary(lmD.10)
summary(impacts(lmD.10))

gpiras/sphet documentation built on Feb. 21, 2024, 3:39 p.m.