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Assorted MODEL Statement Details for PROC TRANSREG


/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: TREGMOD                                             */
/*   TITLE: Assorted MODEL Statement Details for PROC TRANSREG  */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: regression analysis, transformations                */
/*   PROCS: TRANSREG                                            */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: saswfk                UPDATE: July 25, 2010         */
/*     REF: PROC TRANSREG, DETAILS, MODEL                       */
/*    MISC:                                                     */
/****************************************************************/

title 'Missing Data';

data x;
   input y x1 x2 @@;
   datalines;
1 3 7    8 3 9    1 8 6    . . 9    3 3 9
8 5 1    6 7 3    2 7 2    1 8 2    . 9 1
;


proc transreg solve;
   model linear(y) = linear(x1 x2);
   output;
run;

proc print;
run;

title 'Redundancy Analysis';

data x;
   input y1-y3 x1-x4;
   datalines;
 6  8  8 15 18 26 27
 1 12 16 18  9 20  8
 5  6 15 20 17 29 31
 6  9 15 14 10 16 22
 7  5 12 14  6 13  9
 3  6  7  2 14 26 22
 3  5  9 13 18 10 22
 6  3 11  3 15 22 29
 6  3  7 10 20 21 27
 7  5  9  8 10 12 18
;


proc transreg data=x tstandard=z method=redundancy;
   model identity(y1-y3) = identity(x1-x4);
   output out=red mredundancy replace;
run;

proc print data=red(drop=Intercept);
   format _numeric_ 4.1;
run;

proc standard data=x out=std m=0 s=1;
   title2 'Manually Generate Redundancy Variables';
run;

proc reg noprint data=std;
   model y1-y3 = x1-x4;
   output out=p p=ay1-ay3;
run; quit;

proc princomp data=p cov noprint std out=p;
   var ay1-ay3;
run;

proc print data=p(keep=Prin:);
   format _numeric_ 4.1;
run;

proc reg data=p outest=redcoef noprint;
   title2 'Manually Create Redundancy Coefficients';
   model Prin1-Prin3 = x1-x4;
run; quit;

proc print data=redcoef(keep=x1-x4);
   format _numeric_ 4.1;
run;

proc reg data=p outest=redcoef2 noprint;
   title2 'Manually Create Other Coefficients';
   model x1-x4 = prin1-prin3;
run; quit;

proc print data=redcoef2(keep=Prin1-Prin3);
   format _numeric_ 4.1;
run;

title 'ANOVA Output Data Set Example';

data ReferenceCell;
   input y x1 $ x2 $;
   datalines;
11  a  a
12  a  a
10  a  a
 4  a  b
 5  a  b
 3  a  b
 5  b  a
 6  b  a
 4  b  a
 2  b  b
 3  b  b
 1  b  b
;


* Fit Reference Cell Two-Way ANOVA Model;
proc transreg data=ReferenceCell;
   model identity(y) = class(x1 | x2);
   output coefficients replace predicted residuals;
run;

* Print the Results;
proc print;
run;

proc contents position;
   ods select position;
run;

title 'Output Data Set for Curve Fitting Example';

data a;
   do x = 1 to 100;
      y = log(x) + sin(x / 10) + normal(7);
      output;
   end;
run;

proc transreg;
   model identity(y) = spline(x / nknots=9);
   output predicted out=b;
run;

proc contents position;
   ods select position;
run;

title 'METHOD=MORALS Output Data Set Example';

data x;
   input y1 y2 x1 $ x2 $;
   datalines;
11 1 a a
10 4 b a
 5 2 a b
 5 9 b b
 4 3 c c
 3 6 b a
 1 8 a b
;


* Fit Reference Cell Two-Way ANOVA Model;
proc transreg data=x noprint solve;
   model spline(y1 y2) = opscore(x1 x2 / name=(n1 n2));
   output coefficients predicted residuals;
   id x1 x2;
run;

* Print the Results;
proc print;
run;

proc contents position;
   ods select position;
run;

data htex;
   do i = 0.5 to 10 by 0.5;
      x1 = log(i);
      x2 = sqrt(i) + sin(i);
      x3 = 0.05 * i * i + cos(i);
      y  = x1 - x2 + x3 + 3 * normal(7);
      x1 = x1 + normal(7);
      x2 = x2 + normal(7);
      x3 = x3 + normal(7);
      output;
   end;
run;

proc transreg data=htex ss2 short;
   title 'Fit a Polynomial Regression Model with PROC TRANSREG';
   model identity(y) = spline(x1);
run;

data htex2;
   set htex;
   x1_1 = x1;
   x1_2 = x1 * x1;
   x1_3 = x1 * x1 * x1;
run;

proc reg;
   title 'Fit a Polynomial Regression Model with PROC REG';
   model y = x1_1 - x1_3;
run; quit;

title 'Two-Variable Polynomial Regression';

proc transreg data=htex ss2 solve;
   model identity(y) = spline(x1 x2);
run;

proc transreg noprint data=htex maxiter=0;
   /* Use PROC TRANSREG to prepare input to PROC REG */
   model identity(y) = pspline(x1 x2);
   output out=htex2;
run;

proc reg data=htex2;
   model y = x1_1-x1_3 x2_1-x2_3;
   test x1_1, x1_2, x1_3;
   test x2_1, x2_2, x2_3;
run; quit;

title 'Monotone Splines';

proc transreg data=htex ss2 short;
   model identity(y) = mspline(x1-x3 / nknots=3);
run;

title 'Transform Dependent and Independent Variables';

proc transreg data=htex ss2 solve short;
   model spline(y) = spline(x1-x3);
run;

data oneway;
   input y x $;
   datalines;
0 a
1 a
2 a
7 b
8 b
9 b
3 c
4 c
5 c
;


title 'Implicit Intercept Model';

proc transreg ss2 data=oneway short;
   model identity(y) = class(x / zero=none);
   output out=oneway2;
run;

proc reg data=oneway2;
   model y = xa xb xc;         /* Implicit Intercept ANOVA      */
   model y = xa xb xc / noint; /* Implicit Intercept Regression */
run; quit;

title 'Using PROC TRANSREG to Create a Design Matrix';

data a;
   do y = 1, 2;
      do a = 1 to 4;
         do b = 1 to 3;
            w = ceil(uniform(1) * 10 + 10);
            output;
         end;
      end;
   end;
run;

proc transreg data=a design;
   model class(a b / deviations);
   id y w;
   output out=coded;
run;

proc print;
   title2 'PROC TRANSREG Output Data Set';
run;

title2 'PROC LOGISTIC with Classification Variables';

proc logistic;
   freq w;
   model y = &_trgind;
run;

title2 'PROC CATMOD Should Produce the Same Results';

proc catmod data=a;
   model y = a b;
   weight w;
run;

title 'Choice Model Coding';

data design;
   array p[4];
   input p1-p4 @@;
   set = _n_;
   do brand = 1 to 4;
      price = p[brand];
      output;
   end;
   brand = .; price = 1.49; output; /* constant alternative */
   keep set brand price;
   datalines;
1.49 1.99 1.49 1.99 1.99 1.99 2.49 1.49 1.99 1.49 1.99 1.49
1.99 1.49 2.49 1.99 1.49 1.49 1.49 1.49 2.49 1.49 1.99 2.49
1.49 1.49 2.49 2.49 2.49 2.49 1.49 1.49 1.49 2.49 2.49 1.99
2.49 2.49 2.49 1.49 1.99 2.49 1.49 2.49 2.49 1.99 2.49 2.49
2.49 1.49 1.49 1.99 1.49 1.99 1.99 1.49 2.49 1.99 1.99 1.99
1.99 1.99 1.49 2.49 1.99 2.49 1.99 1.99 1.49 2.49 1.99 2.49
;


proc transreg data=design design norestoremissing nozeroconstant;
   model class(brand / zero=none) identity(price);
   output out=coded;
   by set;
run;

proc print data=coded(firstobs=21 obs=25);
   var set brand &_trgind;
run;