Use Enterprise Miner for nearest neighbor analysis.

1. Issue the following commands to SAS (with appropriate
   substitutions for "U:\").

LIBNAME GFLib 'U:\dm\sasdata' ; 

LIBNAME RCLib 'U:\dm\RC' ;

2. If necessary, apply macros to convert Excel data to
SAS (EXCELSAS, see MacroInstr.txt), to divide the data 
into training/test or training/validation/test subsets
(RANSPLIT, see MacroInstr.txt), or to explore the data
(UNIVAR and FREQ/FREQUENCY, see MacroInstr2.txt).

3. Determine the mean and standard deviation of each 
explanatory variable in the training data set.  Use 
this information to create standardized versions of
the explanatory variables, which will be used in the
nearest neighbors analysis.  (The standardized versions
will have mean 0 and standard deviation 1 in the 
training data set and will have approximately but not
exactly mean 0 and standard deviation 1 in other data
sets with the same explanatory variables.)  Note, 
however, that there is no need (or good reason) to use 
a standardized version of the response variable in the 
nearest neighbors analysis.

 
proc means data=RCLIB.diabtr mean stddev;
var NPREG GLU BP BMI AGE;
RUN;

data RCLIB.diabtr;
set  RCLIB.diabtr;
STNPREG = (NPREG - 3.53    )/ 3.1923362;
STGLU = (GLU -  123.03  )/ 31.1628032;
STBP =  (BP - 71.36   )/ 11.4508347;
STBMI = (BMI -   33.22 )/ 6.2331146;
STAGE = (AGE -   32.63  )/  10.5434694;
RUN;

data RCLIB.diabva;
set  RCLIB.diabva;
STNPREG = (NPREG - 3.53    )/ 3.1923362;
STGLU = (GLU -  123.03  )/ 31.1628032;
STBP =  (BP - 71.36   )/ 11.4508347;
STBMI = (BMI -   33.22 )/ 6.2331146;
STAGE = (AGE -   32.63  )/  10.5434694;
RUN;

data RCLIB.diabte;
set  RCLIB.diabte;
STNPREG = (NPREG - 3.53    )/ 3.1923362;
STGLU = (GLU -  123.03  )/ 31.1628032;
STBP =  (BP - 71.36   )/ 11.4508347;
STBMI = (BMI -   33.22 )/ 6.2331146;
STAGE = (AGE -   32.63  )/  10.5434694;
RUN;


4. In SAS, go to the "Solutions" menu.  Go to "Analysis"
and then select "Enterprise Miner".  From the "File" menu, 
choose "New" and then "Project".  A box will appear with 
"Name" and "Location" fields as well as "Create", 
"Cancel", and "Browse" buttons.  Press the "Browse" button 
and select a subdirectory such as 'U:\dm\RC'.  In the 
"Name" field, type a name like 'Neighbor'.  Then press 
"Create".  

5. In the left panel of the SAS Enterprise Miner window, 
you will see a diagram with 'Neighbor' and, immediately 
below it, 'Untitled'.  You can right-click on 'Untitled' 
to assign a name such as 'Diabetes'.

6. Near the top of the SAS Enterprise Miner window, 
click the "Input Data Source" icon (far left) and, while 
holding the mouse button down, drag it into the right 
panel.  Assuming that you have training, validation, and 
test data, repeat this process twice so that you have 
three "Input Data Source" icons in the right panel.  

7. Double click on one of the "Input Data Source" icons 
in the right panel.  Press the "Select" button and then 
choose an appropriate library such as 'RCLib'.  Choose a 
training data set like 'DIABTR'.  In the "Role" field, 
change "RAW" to "TRAIN".  Then click on the "Variables" 
tab near the top of the "Input Data Source" box.  You 
can alter entries in the "Model Role" column by right 
clicking and then selecting "Set Model Role".  You want 
the response variable to be identified as "target", the 
(standardized) explanatory variables to be identified as 
"input", and the ID variable (if any) to be identified as 
"ID".  Any variables that you know will not be used may 
be identified as "rejected".  Also, make any necessary 
adjustments in the "Measurement" column.  When finished, 
click the white on red X in the upper right corner of 
the "Input Data Source" box and confirm the changes.  
Assuming that you have validation and test data, repeat 
this process twice (except that "RAW" will be changed to 
"VALIDATE" and "TEST").

8. Click the "Tools" tab at the lower left part of the
SAS Enterprise Miner window.  Drag the "Memory-Based
Reasoning" icon into the right panel.  By holding the 
left mouse button down, 'draw' arrows from the data 
set icons to the "Memory-Based Reasoning" icon.  Also, 
drag the "Reporter" icon into the right panel, then 
'draw' an arrow from the "Memory-Based Reasoning" icon 
to the "Reporter" icon.

9. Double click on the "Memory-Based Reasoning" icon.  
A "Tools" menu will appear near the top left of the
screen.  Go to "Tools" and then "Settings".  You may
reset the "Number of Neighbors" to whatever you wish,
and you may deselect the "Weight Dimensions" option.
Then choose "OK".  Click the "Output" tab in the
"Memory-Based Reasoning" window.  Select "Process or 
Score:" Training, Validation, and Test.  Close the 
"Memory-Based Reasoning" window and assign a model 
name such as 'DiabNeigh'.
 
10. Right-click the "Memory-Based Reasoning" icon and 
choose "Run".  You will be asked if you want to view 
the results.  You can say "No", as you will acquire
what you need in the next step.

11. Right-click the "Reporter" icon and choose "Run".  
You can "Open" the report now or view it later by 
noting to which subdirectory it has been saved.  For 
a continuous target, you are interested in the 
average squared error for the training, validation, 
and test data sets.  For a categorical target, you 
are interested in the misclassification rate for the 
training, validation, and test data sets.  You can
find this information by clicking the "Output" link.
For either kind of target, you can click the 
"Datastep Score Code" link to get SAS code that will 
allow you to generate predictions for each and every 
individual in any data set that has the same 
(standardized) explanatory variables as the training 
data set.  You will need to modify the SAS code as
indicated below.


ORIGINAL (ignore portions before and after this one)

    proc pmbr score=_last_ out=work._tmpmbr 
                   data=EMDATA.VIEW_GF2 dmdbcat=EMPROJ.dm_DGM00000 k=16 method=RDTREE 
                  target DIAB; 
                  var STNPREG STGLU STBP STBMI STAGE; 
    data &_oldscr; set work._tmpmbr; 

MODIFIED (assuming you want predictions on DIAB
	  for individuals in the test data set)

    proc pmbr score=RCLIB.diabte out=RCLIB.diabtepred
                   data=EMDATA.VIEW_GF2 dmdbcat=EMPROJ.dm_DGM00000 k=16 method=RDTREE ;
                  target DIAB; 
                  var STNPREG STGLU STBP STBMI STAGE; 
    RUN;
    proc print data=RCLIB.diabtepred;
    var P_DIAB1;
    RUN;