# NOT RUN {
library(mgcv)
data(rent)
#---------------------------------------------------------
## normal errors one x-variable
ga1 <- gam(R~s(Fl, bs="ps", k=20), data=rent, method="REML")
gn1 <- gamlss(R~ga(~s(Fl, bs="ps", k=20), method="REML"), data=rent) # additive
gb1 <- gamlss(R~pb(Fl), data=rent) # additive
AIC(ga1,gn1, gb1, k=0)
AIC(ga1,gn1, gb1)
#--------------------------------------------------------
## normal error additive in Fl and A
ga2 <- gam(R~s(Fl)+s(A), method="REML", data=rent)
gn2 <- gamlss(R~ga(~s(Fl)+s(A), method="REML"), data=rent) # additive
gb2 <- gamlss(R~pb(Fl)+pb(A), data=rent) # additive
AIC(ga2,gn2, gb2, k=0)
AIC(ga2,gn2, gb2)
#---------------------------------------------------------
# }
# NOT RUN {
## gamma error additive in Fl and A
ga3 <- gam(R~s(Fl)+s(A), method="REML", data=rent, family=Gamma(log))
gn3 <- gamlss(R~ga(~s(Fl)+s(A), method="REML"), data=rent, family=GA)# additive
gb3 <- gamlss(R~pb(Fl)+pb(A), data=rent, family=GA) # additive
AIC(ga3,gn3, gb3, k=0)
AIC(ga3,gn3, gb3)
#---------------------------------------------------------
## gamma error surface fitting
ga4 <-gam(R~s(Fl,A), method="REML", data=rent, family=Gamma(log))
gn4 <- gamlss(R~ga(~s(Fl,A), method="REML"), data=rent, family=GA)
AIC(ga4,gn4, k=0)
AIC(ga4,gn4)
## plot the fitted surfaces
op<-par(mfrow=c(1,2))
vis.gam(ga4)
vis.gam(getSmo(gn4))
par(op)
## contour plot using mgcv's plot() function
plot(getSmo(gn4))
#---------------------------------------------------------
## predict
newrent <- data.frame(expand.grid(Fl=seq(30,120,5), A=seq(1890,1990,5 )))
newrent1 <-newrent2 <- newrent
newrent1$pred <- predict(ga4, newdata=newrent, type="response")
newrent2$pred <- predict(gn4, newdata=newrent, type="response")
library(lattice)
wf1<-wireframe(pred~Fl*A, newrent1, aspect=c(1,0.5), drape=TRUE,
colorkey=(list(space="right", height=0.6)), main="gam()")
wf2<-wireframe(pred~Fl*A, newrent2, aspect=c(1,0.5), drape=TRUE,
colorkey=(list(space="right", height=0.6)), main="gamlss()")
print(wf1, split=c(1,1,2,1), more=TRUE)
print(wf2, split=c(2,1,2,1))
#---------------------------------------------------------
##gamma error two variables te() function
ga5 <- gam(R~te(Fl,A), data=rent, family=Gamma(log))
gn5 <- gamlss(R~ga(~te(Fl,A)), data=rent, family=GA)
AIC(ga5,gn5)
AIC(ga5,gn5, k=0)
op<-par(mfrow=c(1,2))
vis.gam(ga5)
vis.gam(getSmo(gn5))
par(op)
#----------------------------------------------------------
## use of Markov random fields
## example from package mgcv of Simon Wood
## Load Columbus Ohio crime data (see ?columbus for details and credits)
data(columb) ## data frame
data(columb.polys) ## district shapes list
xt <- list(polys=columb.polys) ## neighbourhood structure info for MRF
## First a full rank MRF...
b <- gam(crime ~ s(district,bs="mrf",xt=xt),data=columb,method="REML")
bb <- gamlss(crime~ ga(~s(district,bs="mrf",xt=xt), method="REML"), data=columb)
AIC(b,bb, k=0)
op<-par(mfrow=c(2,2))
plot(b,scheme=1)
plot(bb$mu.coefSmo[[1]], scheme=1)
## Compare to reduced rank version...
b <- gam(crime ~ s(district,bs="mrf",k=20,xt=xt),data=columb,method="REML")
bb <- gamlss(crime~ ga(~s(district,bs="mrf",k=20,xt=xt), method="REML"),
data=columb)
AIC(b,bb, k=0)
plot(b,scheme=1)
plot(bb$mu.coefSmo[[1]], scheme=1)
par(op)
## An important covariate added...
b <- gam(crime ~ s(district,bs="mrf",k=20,xt=xt)+s(income),
data=columb,method="REML")
## x in gam()
bb <- gamlss(crime~ ga(~s(district,bs="mrf",k=20,xt=xt)+s(income),
method="REML"), data=columb)
## x in gamlss()
bbb <- gamlss(crime~ ga(~s(district,bs="mrf",k=20,xt=xt),
method="REML")+pb(income), data=columb)
AIC(b,bb,bbb)
## ploting the fitted models
op<-par(mfrow=c(2,2))
plot(b,scheme=c(0,1))
plot(getSmo(bb), scheme=c(0,1))
par(op)
plot(getSmo(bbb, which=2))
## plot fitted values by district
op<- par(mfrow=c(1,2))
fv <- fitted(b)
names(fv) <- as.character(columb$district)
fv1 <- fitted(bbb)
names(fv1) <- as.character(columb$district)
polys.plot(columb.polys,fv)
polys.plot(columb.polys,fv1)
par(op)
# }
# NOT RUN {
## bam
# }
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