
ADJRSQ <(adjrsqoptions)>

displays the adjusted Rsquare values for the models examined when you request variable selection with the SELECTION= option
in the MODEL statement.
The following adjrsqoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the largest adjusted Rsquare statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
largest adjusted Rsquare statistic at each value of the number of parameters.

AIC <(aicoptions)>

displays Akaike’s information criterion (AIC) for the models examined when you request variable selection with the SELECTION=
option in the MODEL statement.
The following aicoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the smallest AIC statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
smallest AIC statistic at each value of the number of parameters.

ALL

produces all appropriate plots.

BIC <(bicoptions)>

displays Sawa’s Bayesian information criterion (BIC) for the models examined when you request variable selection with the
SELECTION= option in the MODEL statement.
The following bicoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the smallest BIC statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
smallest BIC statistic at each value of the number of parameters.

COOKSD <(LABEL)>

plots Cook’s D statistic by observation number. Observations whose Cook’s D statistic lies above the horizontal reference line at value , where n is the number of observations used, are deemed to be influential (Rawlings, Pantula, and Dickey, 1998). If you specify the LABEL option, then points deemed as influential are labeled. If you do not specify an ID variable, the
observation number within the current BY group is used as the label. If you specify one or more ID variables in one or more
ID statements, then the first ID variable you specify is used for the labeling.

CP <(cpoptions)>

displays Mallows’ statistic for the models examined when you request variable selection with the SELECTION= option in the MODEL statement. For models where you request the RSQUARE, ADJRSQ, or CP selection, reference lines corresponding to the equations
and , where is the number of parameters in the full model (excluding the intercept) and p is the number of parameters in the subset model (including the intercept), are displayed on the plot of versus p. For the purpose of parameter estimation, Hocking (1976) suggests selecting a model where . For the purpose of prediction, Hocking suggests the criterion . Mallows (1973) suggests that all subset models with small and near p be considered for further study.
The following cpoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the smallest statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
smallest statistic at each value of the number of parameters.

CRITERIA  CRITERIONPANEL <(criteriaoptions)>

produces a panel of fit criteria for the models examined when you request variable selection with the SELECTION= option in
the MODEL statement. The fit criteria displayed are Rsquare, adjusted Rsquare, Mallows’ , Akaike’s information criterion (AIC), Sawa’s Bayesian information criterion (BIC), and Schwarz’s Bayesian information criterion
(SBC). For SELECTION=RSQUARE, SELECTION=ADJRSQ, or SELECTION=CP, scatter plots of these statistics versus the number of parameters
(including the intercept) are displayed. For other selection methods, line plots of these statistics as function of the selection
step number are displayed.
The following criteriaoptions are available:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the best model at each value of the number of parameters. This option applies only to the RSQUARE,
ADJRSQ, and CP selection methods.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the best model at
each value of the number of parameters. Since these labels are typically long, LABELVARS is supported only when the panel
is unpacked. This option applies only to the RSQUARE, ADJRSQ, and CP selection methods.

UNPACK

suppresses paneling. Separate plots are produced for each of the six fit statistics. For models where you request the RSQUARE,
ADJRSQ, or CP selection, two reference lines corresponding to the equations and , where is the number of parameters in the full model (excluding the intercept) and p is the number of parameters in the subset model (including the intercept), are displayed on the plot of versus p. For the purpose of parameter estimation, Hocking (1976) suggests selecting a model where . For the purpose of prediction, Hocking suggests the criterion . Mallows (1973) suggests that all subset models with small and near p be considered for further study.

DFBETAS <(DFBETASoptions)>

produces panels of DFBETAS by observation number for the regressors in the model. Note that each panel contains at most six
plots, and multiple panels are used in the case where there are more than six regressors (including the intercept) in the
model. Observations whose DFBETAS’ statistics for a regressor are greater in magnitude than , where n is the number of observations used, are deemed to be influential for that regressor (Rawlings, Pantula, and Dickey, 1998).
The following DFBETASoptions are available:

COMMONAXES

specifies that the same DFBETAS axis be used in all panels when multiple panels are needed. By default, the DFBETAS axis is
chosen independently for each panel. If you also specify the UNPACK option, then the same DFBETAS axis is used for each regressor.

LABEL

specifies that observations whose magnitude are greater than be labeled. If you do not specify an ID variable, the observation number within the current BY group is used as the label.
If you specify one or more ID variables on one or more ID statements, then the first ID variable you specify is used for the
labeling.

UNPACK

suppresses paneling. The DFBETAS statistics for each regressor are displayed on separate plots.

DFFITS <(LABEL)>

plots the DFFITS statistic by observation number. Observations whose DFFITS’ statistic is greater in magnitude than , where n is the number of observations used and p is the number of regressors, are deemed to be influential (Rawlings, Pantula, and Dickey, 1998). If you specify the LABEL option, then these influential observations are labeled. If you do not specify an ID variable,
the observation number within the current BY group is used as the label. If you specify one or more ID variables in one or
more ID statements, then the first ID variable you specify is used for the labeling.

DIAGNOSTICS <(diagnosticsoptions)>

produces a summary panel of fit diagnostics:

residuals versus the predicted values

studentized residuals versus the predicted values

studentized residuals versus the leverage

normal quantile plot of the residuals

dependent variable values versus the predicted values

Cook’s D versus observation number

histogram of the residuals

“ResidualFit” (or RF) plot consisting of sidebyside quantile plots of the centered fit and the residuals

box plot of the residuals if you specify the STATS=NONE suboption
You can specify the following diagnosticsoptions:

STATS=statsoptions

determines which model fit statistics are included in the panel. See the global STATS= suboption for details. The PLOTS= suboption
of the DIAGNOSTICSPANEL option overrides the global PLOTS= suboption.

UNPACK

produces the eight plots in the panel as individual plots. Note that you can also request individual plots in the panel by
name without having to unpack the panel.

FITPLOT  FIT <(fitoptions)>

produces a scatter plot of the data overlaid with the regression line, confidence band, and prediction band for models that
depend on at most one regressor excluding the intercept. When the number of points exceeds the MAXPOINTS=max value, a heat map is displayed instead of a scatter plot. By default, heat maps are not displayed if the number of observations
times the number of independent variables is greater than 150,000. See the MAXPOINTS= option.
You can specify the following fitoptions:

NOCLI

suppresses the prediction limits.

NOCLM

suppresses the confidence limits.

NOLIMITS

suppresses the confidence and prediction limits.

STATS=statsoptions

determines which model fit statistics are included in the panel. See the global STATS= suboption for details. The PLOTS= suboption
of the FITPLOT option overrides the global PLOTS= suboption.

OBSERVEDBYPREDICTED <(LABEL)>

plots dependent variable values by the predicted values. If you specify the LABEL option, then points deemed as outliers or
influential (see the RSTUDENTBYLEVERAGE option for details) are labeled.

NONE

suppresses all plots.

PARTIAL <(UNPACK)>

produces panels of partial regression plots for each regressor with at most six regressors per panel. If you specify the UNPACK
option, then all partial plot panels are unpacked.

PREDICTIONS (X=numericvariable <predictionoptions>)

produces a panel of two plots whose horizontal axis is the variable you specify in the required X= suboption. The upper plot
in the panel is a scatter plot of the residuals. The lower plot shows the data overlaid with the regression line, confidence
band, and prediction band. This plot is appropriate for models where all regressors are known to be functions of the single
variable that you specify in the X= suboption.
You can specify the following predictionoptions:

NOCLI

suppresses the prediction limits.

NOCLM

suppresses the confidence limits

NOLIMITS

suppresses the confidence and prediction limits

SMOOTH

requests a nonparametric smoothing of the residuals as a function of the variable you specify in the X= suboption. This nonparametric
fit is a loess fit that uses local linear polynomials, linear interpolation, and a smoothing parameter that is selected to
yield a local minimum of the corrected Akaike’s information criterion (AICC). See Chapter 53: The LOESS Procedure, for details. The SMOOTH option is not supported when a FREQ statement is used.

UNPACK

suppresses paneling.

QQPLOT  QQ

produces a normal quantile plot of the residuals.

RESIDUALBOXPLOT  BOXPLOT <(LABEL)>

produces a box plot consisting of the residuals. If you specify label option, points deemed faroutliers are labeled. If you
do not specify an ID variable, the observation number within the current BY group is used as the label. If you specify one
or more ID variables in one or more ID statements, then the first ID variable you specify is used for the labeling.

RESIDUALBYPREDICTED <(LABEL)>

plots residuals by predicted values. If you specify the LABEL option, then points deemed as outliers or influential (see the
RSTUDENTBYLEVERAGE option for details) are labeled.

RESIDUALS <(residualoptions)>

produces panels of the residuals versus the regressors in the model. Each panel contains at most six plots, and multiple panels
are used when the model contains more than six regressors (including the intercept). When the number of points exceeds the
MAXPOINTS=max value, a heat map is displayed instead of a scatter plot. By default, heat maps are not displayed if the number of observations
times the number of independent variables is greater than 150,000. See the MAXPOINTS= option. You can specify the following residualoptions:

SMOOTH

requests a nonparametric smoothing of the residuals for each regressor. Each nonparametric fit is a loess fit that uses local
linear polynomials, linear interpolation, and a smoothing parameter that is selected to yield a local minimum of the corrected
Akaike’s information criterion (AICC). See Chapter 53: The LOESS Procedure, for details. The SMOOTH option is not supported when a FREQ statement is used.

UNPACK

suppresses paneling.

RESIDUALHISTOGRAM

produces a histogram of the residuals.

RFPLOT  RF

produces a “ResidualFit” (or RF) plot consisting of sidebyside quantile plots of the centered fit and the residuals. This plot “shows how much variation in the data is explained by the fit and how much remains in the residuals” (Cleveland, 1993).

RIDGE  RIDGEPANEL  RIDGEPLOT <(ridgeoptions)>

creates panels of VIF values and standardized ridge estimates by ridge values for each coefficient. The VIF values for each
coefficient are connected by lines and are displayed in the upper plot in each panel. The points corresponding to the standardized
estimates of each coefficient are connected by lines and are displayed in the lower plot in each panel. By default, at most
10 coefficients are represented in a panel and multiple panels are produced for models with more than 10 regressors. For ridge
estimates to be computed and plotted, the OUTEST= option must be specified in the PROC REG statement, and the RIDGE= list must be specified in either the PROC REG or the MODEL statement. (See Example 79.5.)
The following ridgeoptions are available:

COMMONAXES

specifies that the same VIF axis and the same standardized estimate axis are used in all panels when multiple panels are needed.
By default, these axes are chosen independently for the regressors shown in each panel.

RIDGEAXIS=LINEAR  LOG

specifies the axis type used to display the ridge parameters. The default is RIDGEAXIS=LINEAR. Note that the point with the
ridge parameter equal to zero is not displayed if you specify RIDGEAXIS=LOG.

UNPACK

suppresses paneling. The traces of the VIF statistics and standardized estimates are shown in separate plots.

VARSPERPLOT=ALL
VARSPERPLOT=number

specifies the maximum number of regressors displayed in each panel or in each plot if you additionally specify the UNPACK
option. If you specify VARSPERPLOT=ALL, then the VIF values and ridge traces for all regressors are displayed in a single
panel.

VIFAXIS=LINEAR  LOG

specifies the axis type used to display the VIF statistics. The default is VIFAXIS=LINEAR.

RSQUARE <(rsquareoptions)>

displays the Rsquare values for the models examined when you request variable selection with the SELECTION= option in the
MODEL statement.
The following rsquareoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the largest Rsquare statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
largest Rsquare statistic at each value of the number of parameters.

RSTUDENTBYLEVERAGE <(LABEL)>

plots studentized residuals by leverage. Observations whose studentized residuals lie outside the band between the reference
lines are deemed outliers. Observations whose leverage values are greater than the vertical reference , where p is the number of parameters including the intercept and n is the number of observations used, are deemed influential (Rawlings, Pantula, and Dickey, 1998). If you specify the LABEL option, then points deemed as outliers or influential are labeled. If you do not specify an ID
variable, the observation number within the current BY group is used as the label. If you specify one or more ID variables
in one or more ID statements, then the first ID variable you specify is used for the labeling.

RSTUDENTBYPREDICTED <(LABEL)>

plots studentized residuals by predicted values. If you specify the LABEL option, then points deemed as outliers or influential
(see the RSTUDENTBYLEVERAGE option for details) are labeled.

SBC <(sbcoptions)>

displays Schwarz’s Bayesian information criterion (SBC) for the models examined when you request variable selection with the
SELECTION= option in the MODEL statement.
The following sbcoptions are available for models where you request the RSQUARE, ADJRSQ, or CP selection method:

LABEL

requests that the model number corresponding to the one displayed in the “Subset Selection Summary” table be used to label the model with the smallest SBC statistic at each value of the number of parameters.

LABELVARS

requests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the
smallest SBC statistic at each value of the number of parameters.