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Stata FAQ
How can I do profile analysis in Stata?
Profile analysis is performed using the manova command. The "trick" in doing profile analysis
is to do transformations of the dependent variables, using the ytransform option,
to allow for the testing of piecewise parallelism.Example
This example profile analysis has four groups on three variables, labeled y1, y2, and y3. The plot of the profiles is shown below.Please note that in Stata 11 the _cons is the last element in the design matrix. Stata 10 has the _cons as the first element. This will make a difference in xm matrix below.input id y1 y2 y3 grp 1 19 20 18 1 2 20 21 19 1 3 19 22 22 1 4 18 19 21 1 5 16 18 20 1 6 17 22 19 1 7 20 19 20 1 8 15 19 19 1 9 12 14 12 2 10 15 15 17 2 11 15 17 15 2 12 13 14 14 2 13 14 16 13 2 14 15 14 17 3 15 13 14 15 3 16 12 15 15 3 17 12 13 13 3 18 8 9 10 4 19 10 10 12 4 20 11 10 10 4 21 11 7 12 4 end tabstat y1 y2 y3, by(grp) Summary statistics: mean by categories of: grp grp | y1 y2 y3 ---------+------------------------------ 1 | 18 20 19.75 2 | 13.8 15.2 14.2 3 | 13 14 15 4 | 10 9 11 ---------+------------------------------ Total | 14.52381 15.61905 15.85714 ---------------------------------------- /* preliminary one-way manova */ manova y1 y2 y3 = grp Number of obs = 21 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 0.0479 3 9.0 36.7 10.12 0.0000 a | P 1.1609 9.0 51.0 3.58 0.0016 a | L 15.6417 9.0 41.0 23.75 0.0000 a | R 15.3753 3.0 17.0 87.13 0.0000 u |-------------------------------------------------- Residual | 17 -----------+-------------------------------------------------- Total | 20 -------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F manovatest, showorder Order of columns in the design matrix 1: (grp==1) 2: (grp==2) 3: (grp==3) 4: (grp==4) 5: _cons
/* test of parallelism */
matrix c1 = (1,-1,0\0,1,-1)
manovatest grp, ytrans(c1)
Transformations of the dependent variables
(1) y1 - y2
(2) y2 - y3
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 0.5633 3 6.0 32.0 1.77 0.1364 e
| P 0.4873 6.0 34.0 1.83 0.1234 a
| L 0.6853 6.0 30.0 1.71 0.1522 a
| R 0.5088 3.0 17.0 2.88 0.0662 u
|--------------------------------------------------
Residual | 17
--------------------------------------------------------------
e = exact, a = approximate, u = upper bound on F
/* test of levels (group differencs) */
mat c2 = (1,1,1)
manovatest grp, ytrans(c2)
Transformation of the dependent variables
(1) y1 + y2 + y3
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 0.0740 3 3.0 17.0 70.93 0.0000 e
| P 0.9260 3.0 17.0 70.93 0.0000 e
| L 12.5165 3.0 17.0 70.93 0.0000 e
| R 12.5165 3.0 17.0 70.93 0.0000 e
|--------------------------------------------------
Residual | 17
--------------------------------------------------------------
e = exact, a = approximate, u = upper bound on
/* test of flatness */
matrix xm = (.25,.25,.25,.25,1)
/* the xm matrix used to select the contrast */
/* Stata 11: matrix xm = (.25,.25,.25,.25,1) */
/* Stata 10: matrix xm = (1,.25,.25,.25,.25) */
manovatest, test(xm) ytrans(c1)
Transformations of the dependent variables
(1) y1 - y2
(2) y2 - y3
Test constraint
(1) .25*1.grp + .25*2.grp + .25*3.grp + .25*4.grp + _cons = 0
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
-----------+--------------------------------------------------
manovatest | W 0.5637 1 2.0 16.0 6.19 0.0102 e
| P 0.4363 2.0 16.0 6.19 0.0102 e
| L 0.7739 2.0 16.0 6.19 0.0102 e
| R 0.7739 2.0 16.0 6.19 0.0102 e
|--------------------------------------------------
Residual | 17
--------------------------------------------------------------
e = exact, a = approximate, u = upper bound on FThe content of this web site should not be construed as an endorsement of any particular web site, book, or software product by the University of California.