The LOESS Procedure

ODS Graphics

Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described in detail in Chapter 21: Statistical Graphics Using ODS.

Before you create graphs, ODS Graphics must be enabled (for example, by specifying the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.

The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS Graphics are discussed in the section A Primer on ODS Statistical Graphics in Chapter 21: Statistical Graphics Using ODS.

You can reference every graph produced through ODS Graphics with a name. The names of the graphs that PROC LOESS generates are listed in Table 71.6, along with the relevant PLOTS= options.

Table 71.6: Graphs Produced by PROC LOESS

ODS Graph Name

Plot Description

PLOTS Option

ContourFitPanel

Panel of loess contour surfaces overlaid on scatter plots of data

CONTOURFITPANEL

ContourFit

Loess contour surface overlaid on scatter plot of data

CONTOURFITPANEL

DiagnosticsPanel

Panel of fit diagnostics

DIAGNOSTICS

FitPanel

Panel of loess curves overlaid on scatter plots of data

FITPANEL

FitPlot

Loess curve overlaid on scatter plot of data

FIT

ObservedByPredicted

Dependent variable versus loess fit

OBSERVEDBYPREDICTED

QQPlot

Normal quantile plot of residuals

QQPLOT

ResidualsBySmooth

Panel of residuals versus regressor by smoothing parameter values

RESIDUALSBYSMOOTH

ResidualByPredicted

Residuals versus loess fit

RESIDUALBYPREDICTED

ResidualHistogram

Histogram of fit residuals

RESIDUALHISTOGRAM

ResidualPanel

Panel of residuals versus regressors for fixed smoothing parameter value

RESIDUALS

ResidualPlot

Plot of residuals versus regressor

RESIDUALS

RFPlot

Side-by-side plots of quantiles of centered fit and residuals

RFPLOT

ScorePlot

Loess fit evaluated at scoring points

SCOREPLOT

CriterionPlot

Selection criterion versus smoothing parameter

CRITERION