spmoran (version 0.2.0)

lsem: Low rank spatial error model (LSEM) estimation

Description

This function estimates the low rank spatial error model.

Usage

lsem( y, x, weig, method = "reml" )

Arguments

y

Vector of explained variables (N x 1)

x

Matrix of explanatory variables (N x K)

weig

eigenvectors and eigenvalues of a spatial weight matrix. Output from weigen

method

Estimation method. Restricted maximum likelihood method ("reml") and maximum likelihood method ("ml") are available. Default is "reml"

Value

b

Matrix with columns for the estimated coefficients on x, their standard errors, t-values, and p-values (K x 4)

s

Vector of estimated variance parameters (2 x 1). The first and the second elements denote the estimated rho parameter (sp_lambda) quantfying the scale of spatial dependent process, and the standard error of the process (sp_SE), respectively.

e

Vector whose elements are residual standard error (resid_SE), adjusted conditional R2 (adjR2(cond)), restricted log-likelihood (rlogLik), Akaike information criterion (AIC), and Bayesian information criterion (BIC). When method = "ml", restricted log-likelihood (rlogLik) is replaced with log-likelihood (logLik)

r

Vector of estimated random coefficients on the spatial eigenvectors (L x 1)

pred

Vector of predicted values (N x 1)

resid

Vector of residuals (N x 1)

References

Murakami, D., Seya, H. and Griffith, D.A. (2018) Low rank spatial econometric models. Arxiv.

See Also

meigen, meigen_f

Examples

# NOT RUN {
require(spdep)
data(boston)
y	<- boston.c[, "CMEDV" ]
x	<- boston.c[,c("CRIM","ZN","INDUS", "CHAS", "NOX","RM", "AGE",
                       "DIS" ,"RAD", "TAX", "PTRATIO", "B", "LSTAT")]
coords<- boston.c[,c("LAT","LON")]
weig 	<- weigen( coords )
res	  <- lsem(y=y,x=x,weig=weig)
res
# }