First case: linear specification is wrong

Make up sample data; it’ll simplify things later if they’re sorted for plotting

x <- sort(runif(300, 0, 3))

Impose true regression function \(\log{(x+1)}\), with Gaussian noise:

yg <- log(x+1) + rnorm(length(x), 0, 0.15)

Bind into a data frame:

gframe <- data.frame(x=x, y=yg)

Plot it, plus the true regression curve

plot(y~x, data=gframe, xlab="x", ylab="y", pch=16, cex=0.5)
curve(log(1+x), col="grey", add=TRUE, lwd=4)

Fit the linear model, add to the plot:

glinfit <- lm(y~x, data=gframe)
print(summary(glinfit), signif.stars=FALSE, digits=2)
## 
## Call:
## lm(formula = y ~ x, data = gframe)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -0.447 -0.111 -0.002  0.091  0.437 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)    0.192      0.019      10   <2e-16
## x              0.431      0.010      41   <2e-16
## 
## Residual standard error: 0.15 on 298 degrees of freedom
## Multiple R-squared:  0.85,   Adjusted R-squared:  0.85 
## F-statistic: 1.7e+03 on 1 and 298 DF,  p-value: <2e-16
plot(x, yg, xlab="x", ylab="y")
curve(log(1+x), col="grey", add=TRUE, lwd=4)
abline(glinfit, lwd=4)

MSE of linear model, in-sample: 0.0238.

We’ll need to do that a lot, so make it a function:

mse.residuals <- function(model) { mean(residuals(model)^2) }

Fit the non-parametric alternative:

library(mgcv)

We’ll use spline smoothing as provided by the gam function:

gnpr <- gam(y~s(x),data=gframe)

Add the fitted values from the spline to the plot:

plot(x,yg,xlab="x",ylab="y")
curve(log(1+x),col="grey",add=TRUE,lwd=4)
abline(glinfit, lwd=4)
lines(x,fitted(gnpr),col="blue",lwd=4)