#       SMSTC lecture 14

library(sm)
sm.options(describe = FALSE)
sm.options(show.script = TRUE)

library(gam)
provide.data(trawl)
ind       <- (Year == 0 & Zone == 1)
score1    <- Score1[ind]
latitude  <- Latitude[ind]
longitude <- Longitude[ind] - 143
position  <- cbind(latitude, longitude = -longitude)

par(mfrow = c(2,2))
par(cex=0.7)
model1  <- sm.regression(position, score1, h = c(0.1, 0.1))
model2  <- gam(score1 ~ lo(latitude) + lo(longitude))
ex      <- model1$eval.points[,1]
ey      <- model1$eval.points[,2]
ngrid   <- length(ex)
grid    <- data.frame(cbind(latitude = rep(ex, ngrid),
            longitude = rep(-ey, rep(ngrid, ngrid))))
surface <- predict(model2, grid)
mask    <- model1$estimate
mask[!is.na(mask)] <- 1
persp(ex, ey, matrix(surface * mask, ncol = ngrid),
          xlab = "latitude", ylab = "longitude", ticktype = "detailed",
          theta = -30, phi = 40, d = 4, col = "green")
summary(model2)
plot.gam(model2, se=TRUE)
par(cex=1)
par(mfrow = c(1,1))

sm.script(mackmap)

sm.script(mackplot)

sm.script(mackgam)

sm.script(smackplt)

sm.script(smackgam)