Once you have the polynomial curve fitting, how to add the confidence interval around it ?
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# We create 2 vectors x and y. It is a polynomial function. x <- runif(300, min=-30, max=30) y <- -1.2*x^3 + 1.1 * x^2 - x + 10 + rnorm(length(x),0,100*abs(x)) # Basic plot of x and y : plot(x,y,col=rgb(0.4,0.4,0.8,0.6), pch=16 , cex=1.3 , xlab="" , ylab="") # 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 the confidence interval around this value. myPredict <- predict( model , interval="predict" ) #Finally, I can add it to the plot using the line and the polygon function with transparency. ix <- sort(x,index.return=T)$ix lines(x[ix], myPredict[ix , 1], col=2, lwd=2 ) polygon(c(rev(x[ix]), x[ix]), c(rev(myPredict[ ix,3]), myPredict[ ix,2]), col = rgb(0.7,0.7,0.7,0.4) , border = NA) |
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