/* discriminant analysis example in Stata */ /* requires Stata 10 or later */ input y1 y2 y3 grp 19.6 5.15 9.5 1 15.4 5.75 9.1 1 22.3 4.35 3.3 1 ... 19.8 2.85 2.3 3 16.5 6.55 3.3 3 17.4 6.60 1.9 3 end candisc y1 y2 y3, group(grp) Canonical linear discriminant analysis | | Like- | Canon. Eigen- Variance | lihood Fcn | Corr. value Prop. Cumul. | Ratio F df1 df2 Prob>F ----+---------------------------------+------------------------------------ 1 | 0.6866 .891988 0.9942 0.9942 | 0.5258 3.5382 6 56 0.0049 e 2 | 0.0722 .005242 0.0058 1.0000 | 0.9948 .07601 2 29 0.9270 e --------------------------------------------------------------------------- Ho: this and smaller canon. corr. are zero; e = exact F Standardized canonical discriminant function coefficients | function1 function2 -------------+---------------------- y1 | -1.09906 -.2473492 y2 | .0209204 -.7741557 y3 | -1.109885 .5411163 Canonical structure | function1 function2 -------------+---------------------- y1 | -.4469698 -.5912455 y2 | -.1799598 -.702846 y3 | -.461776 .5722312 Group means on canonical variables grp | function1 function2 -------------+---------------------- 1 | -1.272352 -.0041395 2 | .6829395 -.0824017 3 | .5894128 .0865412 Resubstitution classification summary +---------+ | Key | |---------| | Number | | Percent | +---------+ | Classified True grp | 1 2 3 | Total -------------+------------------------+------- 1 | 9 0 2 | 11 | 81.82 0.00 18.18 | 100.00 | | 2 | 2 5 4 | 11 | 18.18 45.45 36.36 | 100.00 | | 3 | 2 5 4 | 11 | 18.18 45.45 36.36 | 100.00 -------------+------------------------+------- Total | 13 10 10 | 33 | 39.39 30.30 30.30 | 100.00 | | Priors | 0.3333 0.3333 0.3333 | estat anova Univariate ANOVA summaries | Adj. Variable | Model MS Resid MS Total MS R-sq R-sq F Pr > F -------------+------------------------------------------------------------- y1 | 52.924238 293.96544 278.90037 .1526 .0961 2.701 0.0835 y2 | 3.9751512 126.28728 118.64277 .0305 -.0341 .4722 0.6282 y3 | 81.829694 426.3709 404.83707 .161 .1051 2.879 0.0718 --------------------------------------------------------------------------- Number of obs = 33 Model df = 2 Residual df = 30 estat correlations Pooled within-group correlation matrix | y1 y2 y3 -------------+------------------------------ y1 | 1.00000 y2 | 0.03400 1.00000 y3 | -0.58689 0.14732 1.00000 estat errorrate Error rate estimated by error count | grp | 1 2 3 | Total -------------+---------------------------------+---------- Error rate | .1818182 .5454545 .6363636 | .4545455 -------------+---------------------------------+---------- Priors | .3333333 .3333333 .3333333 | estat grdistance Mahalanobis squared distances between groups | grp grp | 1 2 3 -------------+-------------------------------- 1 | 0 2 | 3.829291 0 3 | 3.474393 .037289 0 estat grmeans Group means | grp | 1 2 3 -------------+--------------------------------- y1 | 18.11818 15.52727 15.34545 y2 | 6.190909 5.581818 5.372727 y3 | 8.681818 5.109091 5.636364 estat manova Number of obs = 33 W = Wilks' lambda L = Lawley-Hotelling trace P = Pillai's trace R = Roy's largest root Source | Statistic df F(df1, df2) = F Prob>F -----------+-------------------------------------------------- grp | W 0.5258 2 6.0 56.0 3.54 0.0049 e | P 0.4767 6.0 58.0 3.02 0.0122 a | L 0.8972 6.0 54.0 4.04 0.0021 a | R 0.8920 3.0 29.0 8.62 0.0003 u |-------------------------------------------------- Residual | 30 -----------+-------------------------------------------------- Total | 32 -------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F