Last week in the non-life insurance course, we’ve seen the theory of the Generalized Linear Models, emphasizing the two important components
- the link function (which is actually the key component in predictive modeling)
- the distribution, or the variance function
Just to illustrate, consider my favorite dataset
lin.mod = lm(dist~speed,data=cars)
A linear model means here
where the residuals are assumed to be centered, independent, and with identical variance. If we visualize that linear regression, we usually see something like that
The idea here (in GLMs) is to assume
which will produce the same model as the one describe previously, based on some error term. That model can be visualized below,
attach(cars)
n=2
X= cars$speed
Y=cars$dist
df=data.frame(X,Y)
vX=seq(min(X)-2,max(X)+2,length=n)
vY=seq(min(Y)-15,max(Y)+15,length=n)
mat=persp(vX,vY,matrix(0,n,n),zlim=c(0,.1),theta=-30,ticktype ="detailed", box = FALSE)
reggig=glm(Y~X,data=df,family=gaussian(link="identity"))
x=seq(min(X),max(X),length=501)
C=trans3d(x,predict(reggig,newdata=data.frame(X=x),type="response"),rep(0,length(x)),mat)
lines(C,lwd=2)
sdgig=sqrt(summary(reggig)$dispersion)
x=seq(min(X),max(X),length=501)
y1=qnorm(.95,predict(reggig,newdata=data.frame(X=x),type="response"), sdgig)
C=trans3d(x,y1,rep(0,length(x)),mat)
lines(C,lty=2)
y2=qnorm(.05,predict(reggig,newdata=data.frame(X=x),type="response"), sdgig)
C=trans3d(x,y2,rep(0,length(x)),mat)
lines(C,lty=2)
C=trans3d(c(x,rev(x)),c(y1,rev(y2)),rep(0,2*length(x)),mat)
polygon(C,border=NA,col="yellow")
C=trans3d(X,Y,rep(0,length(X)),mat)
points(C,pch=19,col="red")
n=8
vX=seq(min(X),max(X),length=n)
mgig=predict(reggig,newdata=data.frame(X=vX))
sdgig=sqrt(summary(reggig)$dispersion)
for(j in n:1){
stp=251
x=rep(vX[j],stp)
y=seq(min(min(Y)-15,qnorm(.05,predict(reggig,newdata=data.frame(X=vX[j]),type="response"), sdgig)),max(Y)+15,length=stp)
z0=rep(0,stp)
z=dnorm(y, mgig[j], sdgig)
C=trans3d(c(x,x),c(y,rev(y)),c(z,z0),mat)
polygon(C,border=NA,col="light blue",density=40)
C=trans3d(x,y,z0,mat)
lines(C,lty=2)
C=trans3d(x,y,z,mat)
lines(C,col="blue")}
We do have two parts here: the linear increase of the average, and the constant variance of the normal distribution
.
On the other hand, if we assume a Poisson regression,
poisson.reg = glm(dist~speed,data=cars,family=poisson(link="log"))
we have something like
This time, two things have changed simultaneously: our model is no longer linear, it is an exponential one , and the variance is also increasing with the explanatory variable
, since with a Poisson regression,
If we adapt the previous code, we get
The problem is that we changed two things when we introduced the Poisson regression from the linear model. So let us look at what happens when we change the two components independently. First, we can change the link function, with a Gaussian model but this time a multiplicative model (with a logarithm link function)
gaussian.reg = glm(dist~speed,data=cars,family=gaussian(link="log"))
which is still, here, an homoscedasctic model, but this time non-linear. Or we can change the link function in the Poisson regression, to get a linear model, but heteroscedastic
poisson.lin = glm(dist~speed,data=cars,family=poisson(link="identity"))
So this is basically what GLMs are about….