Spatial Lag Model, one weight matrix, MLE

Matrices http://stat.ethz.ch/R-manual/R-patched/library/base/html/colSums.html

1. Spatial Lag Model, one weight matrix, MLE

load("http://intersci.ss.uci.edu/wiki/Eff/SCCS_stata.dta")
tmpfile <- tempfile()
download.file('http://intersci.ss.uci.edu/wiki/Eff/SCCS_stata.dta',tmpfile)
risk2 <- read.dta(tmpfile)
unlink(tmpfile)

#datafile 1: http://intersci.ss.uci.edu/wiki/Eff/SCCS_stata.dta
#distancematrix: http://intersci.ss.uci.edu/wiki/Eff/distmat.dbf
#languagemetrix: http://intersci.ss.uci.edu/wiki/Eff/langmat.dbf
#This program: http://intersci.ss.uci.edu/wiki/Eff/A_EFF_SAR_R
#Change the following to your own working directory (note UNIX slant of slashes--even on Windows machine)
library(spdep)
setwd("d:/projects/dow/")
#source("http://intersci.ss.uci.edu/wiki/pub/R-KinSim.R")
#net=source("C:/Program Files/pajek/PAJEK/PajekR.r")
#setwd("C:/Program Files/pajek/PAJEK/")
setwd("C:/Program Files/pajek/PAJEK/Anthon_Eff-Autocorrelation")
options(echo=TRUE)
library(foreign)
library(spdep)
#Read in the dbf format weight matrices-the dbf file is 186x186 (no row names)
#Comment the matrix you do NOT want to use
lds<-read.dbf("langmat.dbf") #language phylogeny
#lds<-read.dbf("distmat.dbf") #great circle distance
#convert to matrix
lds<-as.matrix(lds)
#take a quick look at the upper left hand corner to see that it is OK
lds[1:5,1:5]
#read in SCCS data. It is in STATA format, since this is numeric--there are problems with the SPSS version,
#since R imports the value labels from SPSS and the variables become non-numeric
gg<-read.dta("SCCS_stata.dta")
length(gg[,1]) #the number of observations
length(gg[1,]) #the number of variables
#create a data frame containing our variables, also give the variables names
df<-data.frame(femsubs=gg$v826,fishimp=gg$v816,huntimp=gg$v817,pathstress=gg$v1260,rainfall=gg$v855,polygamy=gg$v79,eboysxp=gg$v353,fixres=gg$v150,landtrans=gg$v154,polinteg=gg$v157,socstrat=gg$v158)
#since the estimation doesn't work with missing values, here we identify all observations with non-missing values
kk<-as.matrix(df)
oo<-matrix(1,length(kk[1,]),1)
tm<-kk%*%oo
rr<-which(tm[,1]!="NA")
#here we restrict the weight matrix and data to include only non-missing values
wmat<-mat2listw(lds[rr,rr])
ffd<-df[rr,]
length(df[,1]) #number of observations before dropping those with missing values
length(ffd[,1]) #number of observations after dropping those with missing values
summary(ffd)
#We estimate a spatial lag model
library(spdep) #I ADDED THIS AFTER INSTALLING SPDEP-DW
col.lm<-lagsarlm(femsubs~fishimp+huntimp+pathstress+rainfall+polygamy+eboysxp+fixres+landtrans+polinteg+socstrat,
data=ffd,wmat,quiet=FALSE)
#this next displays parameter estimates and diagnostics for the spatial lag model
summary(col.lm)
pseudoR2<-cor(col.lm$fitted.values,ffd$femsubs)^2
#could not find function lagsarlm--> install packages -->spdep  library(spdep)
#help{"lagsarlm")