First of all, a scatterplot is built using the native R
plot() function. Then, a polynomial model is fit thanks to the
lm() function. It is possible to have the estimated Y value for each step of the X axis using the
predict() function, and plot it with
It is a good practice to add the equation of the model with
Note: You can also add a confidence interval around the model as described in chart #45.
x <- runif(300, min=-10, max=10) y <- 0.1*x^3 - 0.5 * x^2 - x + 10 + rnorm(length(x),0,8) # plot of x and y : plot(x,y,col=rgb(0.4,0.4,0.8,0.6),pch=16 , cex=1.3) # Can we find a polynome that fit this function ? model <- lm(y ~ x + I(x^2) + I(x^3)) # I can get the features of this model : #summary(model) #model$coefficients #summary(model)$adj.r.squared # For each value of x, I can get the value of y estimated by the model, and add it to the current plot ! myPredict <- predict( model ) ix <- sort(x,index.return=T)$ix lines(x[ix], myPredict[ix], col=2, lwd=2 ) # I add the features of the model to the plot coeff <- round(model$coefficients , 2) text(3, -70 , paste("Model : ",coeff , " + " , coeff , "*x" , "+" , coeff , "*x^2" , "+" , coeff , "*x^3" , "\n\n" , "P-value adjusted = ",round(summary(model)$adj.r.squared,2)))