# fig6_3

## PURPOSE

Local Regression and Likelihood, Figure 6.3.

## SYNOPSIS

This is a script file.

## DESCRIPTION

     Local Regression and Likelihood, Figure 6.3.
     Author: Catherine Loader

     Local smooth of CO2 dataset. Estimate the main trend,
     then use periodic smoothing of the residuals to estimate
     the annual effect.

     A periodic smooth is specified by 'style','a'.
     Note that year+month/12 scales the predictor to have a period
     of 1. The 'scale' argument to locfit() is period/(2*pi).

## CROSS-REFERENCE INFORMATION

This function calls:

- lfplot Plot (for one or two dimensions) a locfit() fit.
- locfit Smoothing noisy data using Local Regression and Likelihood.
- residuals Residuals (or a few other things) from a locfit() fit.

This function is called by:

- runbook

## SOURCE CODE

    0001 % Local Regression and Likelihood, Figure 6.3.
    0002 % Author: Catherine Loader
    0003 %
    0004 % Local smooth of CO2 dataset. Estimate the main trend,
    0005 % then use periodic smoothing of the residuals to estimate
    0006 % the annual effect.
    0007 %
    0008 % A periodic smooth is specified by 'style','a'.
    0009 % Note that year+month/12 scales the predictor to have a period
    0010 % of 1. The 'scale' argument to locfit() is period/(2*pi).
    0011
    0012 load co2;
    0013 fit1 = locfit(year+month/12,co2,'alpha',0.5,'deg',1);
    0014 res = residuals(fit1);
    0015 fit2 = locfit(year+month/12,res,'alpha',[0 2],'style','a','scale',1/(2*pi));
    0016 figure('Name','fig6_3: CO2 Dataset: Local smoothing' );
    0017 lfplot(fit2,'nodata');