proc import OUT=WORK.c076
DATAFILE="W:\courses\stat704\C076.txt" replace;
RUN;

*Slight increasing trend in baseline evident.  Large outliers.  Scale transformation needed;
proc sgplot data=c076;
series x=Date y=Fecal_Coliform;
run;


data c076; set c076; lFC=log(Fecal_Coliform);
run;

*Default loess must have a large bandwidth;
proc sgplot data=c076;
scatter x=Date y=lFC;
loess x=Date y=lFC/nomarkers;
yaxis label='log(Fecal Coliform)';
run;

*Results similar to SGPLOT;
proc loess data=c076;
model lFC=Date/clm alpha=0.05;
run;

*We use a quadratic fit for each local regression;
*No amount of smoothing eliminates the problem with large outliers;
proc loess data=c076;
model lFC=Date/degree=2 smooth=(.3 .4 .5 .6);
run;

*Another smoothing criterion--look at the effective smoothing parameter;
proc loess data=c076;
model lFC=Date/degree=2 select=df1(6);
run;

*We definitely need to run robust iteratively reweighted least squares;
proc loess data=c076;
model lFC=Date/degree=2 smooth=0.5 iterations=5 clm;
run;