BOXCHART Statement: ANOM Procedure

Saving Decision Limits

Note: See Saving Decision Limits Using ANOM BOXCHART in the SAS/QC Sample Library.

You can save the decision limits for an ANOM chart, together with the parameters used to compute the limits, in a SAS data set.

The following statements read measurements from the data set LabelDeviations (see Creating ANOM Boxcharts from Response Values.) and save the decision limits displayed in Figure 4.3 in a data set named LabelLimits:

proc anom data=LabelDeviations;
   boxchart Deviation*Position / outlimits=LabelLimits
                                 nochart;
run;

The OUTLIMITS= option names the data set containing the decision limits, and the NOCHART option suppresses the display of the chart. The data set LabelLimits is listed in Figure 4.7.

Figure 4.7: The Data Set LabelLimits Containing Decision Limit Information

Decision Limits for Labler Position Deviations

_VAR_ _GROUP_ _TYPE_ _LIMITN_ _ALPHA_ _LDLX_ _MEAN_ _UDLX_ _MSE_ _DFE_ _LIMITK_
Deviation Position ESTIMATE 10 0.05 -.009878975 .009991333 0.029862 .000643646 54 6



The data set LabelLimits contains one observation with the limits for response Deviation. The values of _LDLX_ and _UDLX_ are the lower and upper decision limits for the means, and the value of _MEAN_ is the weighted average of the group means, which is represented by the central line.

The values of _MEAN_, _MSE_, _DFE_, _LIMITK_, _LIMITN_, and _ALPHA_ are the parameters used to compute the decision limits. The value of _MSE_ is the mean square error, and the value of _DFE_ is the associated degrees of freedom. The value of _LIMITK_ is the group size (k), the value of _LIMITN_ is the nominal sample size associated with the decision limits, and the value of _ALPHA_ is the value of the significance level ($\alpha $). The variables _VAR_ and _GROUP_ are bookkeeping variables that save the response and group-variable. The variable _TYPE_ is a bookkeeping variable that indicates whether the values of _MEAN_ and _MSE_ are estimates computed from the data or standard (known) values specified with procedure options. In most applications, the value of _TYPE_ will be 'ESTIMATE.'

Note: See Saving Decision Limits and Summary Statistics in the SAS/QC Sample Library.

You can create an output data set containing both decision limits and group summary statistics with the OUTTABLE= option, as illustrated by the following statements:

proc anom data=LabelDeviations;
   boxchart Deviation*Position / outtable=LabelTab
                                 nochart;
run;

The data set LabelTab is listed in Figure 4.8.

Figure 4.8: The Data Set LabelTab

Summary Statistics and Decision Limits

_VAR_ Position _ALPHA_ _LIMITN_ _SUBN_ _LDLX_ _SUBX_ _MEAN_ _UDLX_ _EXLIM_ _SUBMIN_ _SUBQ1_ _SUBMED_ _SUBQ3_ _SUBMAX_
Deviation 1 0.05 10 10 -.009878975 -0.022342 .009991333 0.029862 LOWER -0.06466 -0.03623 -0.026195 -0.00163 0.00944
Deviation 2 0.05 10 10 -.009878975 0.016241 .009991333 0.029862   -0.03323 -0.02014 0.020420 0.04378 0.05641
Deviation 3 0.05 10 10 -.009878975 0.006011 .009991333 0.029862   -0.04404 -0.01394 0.005680 0.02849 0.04855
Deviation 4 0.05 10 10 -.009878975 0.064729 .009991333 0.029862 UPPER 0.03620 0.05298 0.060315 0.07551 0.10729
Deviation 5 0.05 10 10 -.009878975 0.008121 .009991333 0.029862   -0.04640 -0.00741 0.007625 0.03021 0.03736
Deviation 6 0.05 10 10 -.009878975 -0.012812 .009991333 0.029862 LOWER -0.03839 -0.02845 -0.009495 0.00167 0.00710



This data set contains one observation for each group sample. The variable _SUBMIN_ contains the group minimums, and the variable _SUBQ1_ contains the first quartile for each group. The variables _SUBX_ and _SUBMED_ contain the group means and medians. The variable _SUBQ3_ contains the third quartiles, _SUBMAX_ contains the group maximums, and _SUBN_ contains the group sample sizes. The variables _LDLX_ and _UDLX_ contain the lower and upper decision limits, and the variable _MEAN_ contains the central line. The variables _VAR_ and Position contain the response name and values of the group-variable, respectively. For more information, see OUTTABLE= Data Set.

An OUTTABLE= data set can be read later as a TABLE= data set. For example, the following statements read LabelTab and display an ANOM boxchart (not shown here) identical to the chart in Figure 4.3:

title 'Analysis of Label Deviations';
proc anom table=LabelTab;
   boxchart Deviation*Position / odstitle=title;
label _SUBX_ = 'Mean Deviation from Center (mm)';
run;

Because the ANOM procedure simply displays the information in a TABLE= data set, you can use TABLE= data sets to create specialized ANOM boxcharts.

For more information, see TABLE= Data Set.