The used.cars
data frame has 48 rows and 2 columns. The data set includes a neighbours list for the 48 states excluding DC from poly2nb().
used.cars
This data frame contains the following columns:
tax.charges taxes and delivery charges for 1955-9 new cars
price.1960 1960 used car prices by state
Hanna, F. A. 1966 Effects of regional differences in taxes and transport charges on automobile consumption, in Ostry, S., Rhymes, J. K. (eds) Papers on regional statistical studies, Toronto: Toronto University Press, pp. 199-223.
Hepple, L. W. 1976 A maximum likelihood model for econometric estimation with spatial series, in Masser, I (ed) Theory and practice in regional science, London: Pion, pp. 90-104.
if (requireNamespace("spdep", quietly = TRUE)) {
library(spdep)
data(used.cars)
moran.test(used.cars$price.1960, nb2listw(usa48.nb))
moran.plot(used.cars$price.1960, nb2listw(usa48.nb),
labels=rownames(used.cars))
uc.lm <- lm(price.1960 ~ tax.charges, data=used.cars)
summary(uc.lm)
lm.morantest(uc.lm, nb2listw(usa48.nb))
lm.morantest.sad(uc.lm, nb2listw(usa48.nb))
lm.LMtests(uc.lm, nb2listw(usa48.nb))
#if (requireNamespace("spatialreg", quietly = TRUE)) {
# library(spatialreg)
# uc.err <- errorsarlm(price.1960 ~ tax.charges, data=used.cars,
# nb2listw(usa48.nb), tol.solve=1.0e-13,
# control=list(tol.opt=.Machine$double.eps^0.3))
# summary(uc.err)
# uc.lag <- lagsarlm(price.1960 ~ tax.charges, data=used.cars,
# nb2listw(usa48.nb), tol.solve=1.0e-13,
# control=list(tol.opt=.Machine$double.eps^0.3))
# summary(uc.lag)
# uc.lag1 <- lagsarlm(price.1960 ~ 1, data=used.cars,
# nb2listw(usa48.nb), tol.solve=1.0e-13,
# control=list(tol.opt=.Machine$double.eps^0.3))
# summary(uc.lag1)
# uc.err1 <- errorsarlm(price.1960 ~ 1, data=used.cars,
# nb2listw(usa48.nb), tol.solve=1.0e-13,
# control=list(tol.opt=.Machine$double.eps^0.3))
# summary(uc.err1)
#}
}
#>
#> Lagrange multiplier diagnostics for spatial dependence
#>
#> data:
#> model: lm(formula = price.1960 ~ tax.charges, data = used.cars)
#> weights: nb2listw(usa48.nb)
#>
#> LMErr = 31.793, df = 1, p-value = 1.715e-08
#>