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
|
library(gam)
ind <- (trawl$Year == 0 & trawl$Zone == 1)
score1 <- trawl$Score1[ind]
latitude <- trawl$Latitude[ind]
longitude <- trawl$Longitude[ind] - 143
position <- cbind(latitude, longitude = -longitude)
par(mfrow = c(2,2))
par(cex=0.7)
model1 <- sm.regression(position, score1, h = c(0.1, 0.1))
model2 <- gam(score1 ~ lo(latitude) + lo(longitude))
ex <- model1$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")
summary(model2)
plot(model2, se=TRUE)
par(cex=1)
par(mfrow = c(1,1))
|
