Ornstein1 <- read.table("c:/temp/Ornstein.txt",header=TRUE)

Ornstein1$assets2  <- Ornstein1$assets/1000

Ornstein1$lassets2  <- log(Ornstein1$assets2)

Ornstein1$xlx  <- Ornstein1$assets2 * Ornstein1$lassets2 


 # the original model, now using quasilikelihood

Ornstein1.qglm1 <- glm(interlocks ~ as.factor(sector) +as.factor(nation) +assets2,
                        data=Ornstein1,family=quasipoisson())

summary(Ornstein1.qglm1)

par(mfrow=c(2,2)) 
plot(Ornstein1.qglm1)
par(mfrow=c(1,1)) 

 # plots to check influence, etc.

library(car)

influencePlot(Ornstein1.qglm1)
plot(cooks.distance(Ornstein1.qglm1))
plot(covratio(Ornstein1.qglm1))
plot(dffits(Ornstein1.qglm1))


 # using a Box-Tidwell constructed variable

Ornstein1.qglm2 <- glm(interlocks ~ as.factor(sector) +as.factor(nation) +assets2 +xlx,
                        data=Ornstein1,family=quasipoisson())

summary(Ornstein1.qglm2)

par(mfrow=c(2,2)) 
plot(Ornstein1.qglm2)
par(mfrow=c(1,1)) 

 # added variable plots

avPlot(Ornstein1.qglm2,variable="xlx",identify.points=FALSE)

avPlots(Ornstein1.qglm2,one.page=TRUE,ask=FALSE,identify.points=FALSE)


 # now incorporate transformed assets

Ornstein1$fsector <- as.factor(Ornstein$sector)

Ornstein1$fnation <- as.factor(Ornstein$nation)

Ornstein1.qglm3 <- glm(interlocks ~ fsector +fnation +lassets2,
                        data=Ornstein1,family=quasipoisson())

summary(Ornstein1.qglm3)

par(mfrow=c(2,2)) 
plot(Ornstein1.qglm3)
par(mfrow=c(1,1)) 


 # component plus residual plot for log(assets), function from the car package

crPlot(Ornstein1.qglm3,"lassets2")


influencePlot(Ornstein1.qglm3)
plot(cooks.distance(Ornstein1.qglm3))
plot(covratio(Ornstein1.qglm3))
plot(dffits(Ornstein1.qglm3))



library(effects)

plot(effect("lassets2", Ornstein1.qglm3))