Tag Archives: lm()

On linear models with no constant and R2

In econometrics course we always say to our students that “if you fit a linear model with no constant, then you might have trouble. For instance, you might have a negative R-squared”. So I tried to find databases on the internet such that, when we compute a linear regression, we actually obtain a negative R squared. I have generated hundreds to random databases that should exhibit such a property, in R. With no success. Perhaps to be more specific, I should explain what might happen if we do not include a constant in a linear model. Consider the following dataset, where points are on a straight line, with a negative slope, far from the origin, symmetric with respect to the first diagonal.

> x=1:3
> y=3:1
> plot(x,y)

Points are on a straight line, so it is actually possible to get a perfect linear model. But only if we integrate a constant in our model. This is related to the fact that the correlation between our two variates is -1,

> cor(x,y)
[1] -1

The least-square program is here


i.e. the estimate of the slope is


Numerically, we obtain

> sum(x*y)/sum(x^2)
[1] 0.7142857

which is the actual slope on the illustration above. If we compute the sum of squares of errors (as a function of the slope), we have here

> ssr=function(b){sum((y-b*x)^2)}
> SSR=Vectorize(ssr)
> B=seq(-1,3,by=.1)
> plot(B,SSR(B),ylim=c(0,ssr(3)),cex=.6,type="b")

so the value we have computed is actually the minimum of the sum of squares of errors. But note that the sum of squares always exceeds the total sum of squares in red on the graph above

> ssr(b)
[1] 6.857143
> sum((y-mean(y))^2)
[1] 2

Recall that the total “coefficient of variation“, denoted http://freakonometrics.blog.free.fr/public/perso5/R2.gif, is defined as



> 1-ssr(b)/sum((y-mean(y))^2)
[1] -2.428571

which is negative. It is also sometimes defined as “the square of the sample correlation coefficient between the outcomes and their predicted values“. Here it would be related to

> cor(b*x,y)
[1] -1

so we would have a unit http://freakonometrics.blog.free.fr/public/perso5/R2.gif . So obviously, using the http://freakonometrics.blog.free.fr/public/perso5/R2.gif in a model without a constant would give odd results. But the weird part is that if we run that regression with R, we get

> summary(lm(y~0+x))

lm(formula = y ~ 0 + x)

1       2       3
2.2857  0.5714 -1.1429

Estimate Std. Error t value Pr(>|t|)
x   0.7143     0.4949   1.443    0.286

Residual standard error: 1.852 on 2 degrees of freedom
Multiple R-squared: 0.5102,	Adjusted R-squared: 0.2653
F-statistic: 2.083 on 1 and 2 DF,  p-value: 0.2857

Here, the estimation is correct. But the http://freakonometrics.blog.free.fr/public/perso5/R2.gif we obtain tells us that the model is not that bad… So if anyone knows what R computes, I’d be glad to know. The value given by R (thanks Vincent for asking me to look carefully at the R source code) is obtained using Pythagoras’s theorem to compute the total sum of square,

> sum((b*x)^2)/(sum((b*x)^2)+sum((y-b*x)^2))
[1] 0.5102041

So be careful, the http://freakonometrics.blog.free.fr/public/perso5/R2.gif might look good, but meaningless !

ACT2040, introduction aux modèles linéaires généralisés

On commencera ce mardi les GLM, après avoir introduit les lois exponentielles (qui ont du être revues en démonstration vendredi dernier). La notation utilisée sera que la loi (densité ou fonction de probabilité) de http://freakonometrics.blog.free.fr/public/perso4/Yi-ltx.gif sera de la forme


Pour un complément plus exhaustif, je renvoie au chapitre en ligne.

  • Le modèle linéaire (Gaussien)

Le modèle de base est le modèle Gaussien que l’on avait revu au dernier cours,

> X=c(1,2,3,4)
> Y=c(1,2,5,6)
> base=data.frame(X,Y)
> reg1=lm(Y~1+X,data=base)
> nbase=data.frame(X=seq(0,5,by=.1))
> Y1=predict(reg1,newdata=nbase)

Pour une prédiction (unique), on obtient la prédiction suivante

Le code pour une telle représentation est le suivant

> plot(X,Y,pch=3,cex=1.5,lwd=2,xlab="",ylab="")
> lines(nbase$X,Y1,col="red",lwd=2)
> u=2
> mu=predict(reg1)[2]
> sigma=summary(reg1)$sigma
> y=seq(0,7,.05)
> loi=dnorm(y,mu,sigma)
> segments(u,y,loi+u,y,col="light green")
> lines(loi+u,y)
> abline(v=u,lty=2)
> points(X[2],Y[2],pch=3,cex=1.5,lwd=2)
> points(X[2],predict(reg1)[2],pch=19,col="red")
> arrows(u-.2,qnorm(.05,mu,sigma),
+ u-.2,qnorm(.95,mu,sigma),length=0.1,code=3,col="blue")

On peut multiplier les prédictions, en se basant sur l’hypothèse d’homoscédasticité (la variance sera alors constante)

Mais on peut aller plus loin

  • Le modèle linéaire généralisé

Plusieurs modèles peuvent etre estimés, en changeant la loi de la variable à expliquer, et la fonction lien,

> reg2=glm(Y~1+X,data=base,family=poisson(link="identity"))
> Y2=predict(reg2,newdata=nbase,type="response")
> reg3=glm(Y~1+X,data=base,family=poisson(link="log"))
> Y3=predict(reg3,newdata=nbase,type="response")
> reg4=glm(Y~1+X,data=base,family=gaussian(link="log"))
> Y4=predict(reg4,newdata=nbase,type="response")
> sigma=sqrt(summary(reg4)$dispersion)

Pour le modèle Poissonnien avec un lien identité, on obtient

On obtient ainsi une variance qui augmente avec la prédiction,

Pour une régression de Poisson avec un lien logarithmique,

i.e. pour nos quatre prédictions

On peut comparer avec une prédiction d’un modèle Gaussien avec un lien logarithmique,

i.e. pour les quatre prédictions