Description Usage Arguments Value References Examples
This package provides robust maximum likelihood estimation for spatial error model.
1 2 3 4 5 6 7 8 9 10 11 12 | RoMLE.error(
initial.beta,
initial.s2,
initial.lambda,
W,
y,
x,
phi.function,
converge.v,
iter,
print.values
)
|
initial.beta |
initial value of coefficients |
initial.s2 |
initial value of varaince |
initial.lambda |
initial value of autocorrelation parameters |
W |
a symmetric weight matrix |
y |
dependent variable |
x |
independent variables |
phi.function |
a robust m-estimator function, should be set as 1 for Cauchy, 2 for Welsch, 3 for Insha and 4 for Logistic |
converge.v |
converge value for fisher scoring algorithm, can be set as 1e-04 |
iter |
iteration number for fisher scoring algorithm, set by users (e.g. 100) |
print.values |
printing estimated values for each step until converge, should be set TRUE or FALSE |
coefficients, lambda, s2, Phi
Yildirim, V. and Kantar, Y.M. (2020). Robust estimation of spatial error model. in Journal of Statistical Computation and Simulation https://doi.org/10.1080/00949655.2020.1740223
Yildirim, V., Mert Kantar, Y. (2019). Spatial Statistical Analysis of Participants in The Individual Pension System of Turkey. Eskisehir Teknik Universitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler, 7(2), 184-194 https://doi.org/10.20290/estubtdb.518706
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | #spdep library can be used to create a weight matrix from listw
#require(spdep)
#W <- as(listw, "CsparseMatrix")
#example 1
data(TRQWM)
data(unemployment_data)
data(unemployment_coefs)
y <- unemployment_data$unemployment
x <- unemployment_data$urbanization
#initial values was taken from MLE
initial.beta <- unemployment_coefs[1:2,2]
initial.lambda <- unemployment_coefs[3,2]
initial.s2 <- unemployment_coefs[4,2]
RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x,
phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)
#example 2
data(TRQWM)
data(IPS_data)
data(IPS_coefs)
y <- IPS_data[,3]
x <- IPS_data[,4:10]
#initial values was taken from MLE
initial.beta <- IPS_coefs[1:8,2]
initial.lambda <- IPS_coefs[9,2]
initial.s2 <- IPS_coefs[10,2]
RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x,
phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)
|
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