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Documentation Example 25 for PROC CALIS

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/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: CALEX07                                             */
/*   TITLE: Documentation Example 25 for PROC CALIS             */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: latent growth curve model                           */
/*   PROCS: CALIS                                               */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: yiyung                UPDATE: August 27, 2007       */
/*     REF: PROC CALIS, Example 25                              */
/*    MISC:                                                     */
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data growth;
   input y1 y2 y3 y4 y5;
   datalines;
17.6  21.4  25.6  32.1  37.7
13.2  14.3  18.9  20.3  25.4
11.6  13.5  17.4  22.1  39.6
10.7  11.1  13.2  18.2  21.4
18.7  23.7  28.6  31.5  34.0
18.3  19.2  20.5  23.2  25.9
 9.2  13.5  17.8  19.2  21.1
18.3  23.5  27.9  30.2  34.6
11.2  15.6  20.8  22.7  30.4
17.0  22.9  26.9  31.9  35.6
10.4  13.6  18.0  25.6  29.3
17.7  19.0  22.5  28.5  30.7
14.5  19.4  21.1  28.8  31.5
20.0  21.4  28.9  30.2  35.6
14.6  19.3  21.7  28.5  32.0
11.7  15.2  19.1  23.7  28.7
;
proc calis method=ml data=growth nostand noparmname;
   lineqs
      y1 = 0. * Intercept + f_alpha                + e1,
      y2 = 0. * Intercept + f_alpha  +  1 * f_beta + e2,
      y3 = 0. * Intercept + f_alpha  +  2 * f_beta + e3,
      y4 = 0. * Intercept + f_alpha  +  3 * f_beta + e4,
      y5 = 0. * Intercept + f_alpha  +  4 * f_beta + e5;
   variance
      f_alpha f_beta,
      e1-e5 = 5 * evar;
   mean
      f_alpha f_beta;
   cov
      f_alpha f_beta;
   fitindex on(only)=[chisq df probchi];
run;
proc calis method=ml data=growth nostand noparmname;
   lineqs
      y1 = 0. * Intercept + f_alpha                + e1,
      y2 = 0. * Intercept + f_alpha  +  1 * f_beta + e2,
      y3 = 0. * Intercept + f_alpha  +  2 * f_beta + e3,
      y4 = 0. * Intercept + f_alpha  +  3 * f_beta + e4,
      y5 = 0. * Intercept + f_alpha  +  4 * f_beta + e5;
   variance
      f_alpha f_beta,
      e1-e5;
   mean
      f_alpha f_beta;
   cov
      f_alpha f_beta;
   fitindex on(only)=[chisq df probchi];
run;