Analysis of Covariance
Linear Model
Hypotheses
Assumptions
Selecting a Covariate
Schematic with Example Data
a1 a2 a3 a4
Y X Y X Y X Y X
3 42
6 57
3 33
3 47
1 32
2 35
2 33
2 39
4 47
5 49
4 42
3 41
2 38
3 43
4 48
3 45
7 61
8 65
7 64
6 56
5 52
6 58
5 53
6 54
7 65
8 74
9 80
8 73
10 85
10 82
9 78
11 89
ANCOVA Summary Table
| Source | SS | df | MS | F | Error Term | |
| 1 | Covariate | 33.950 | 1 | 33.950 | 130.09 | [3] |
| 2 | A | 1.793 | 3 | 0.598 | 2.29 | [3] |
| 3 | Error | 7.047 | 27 | 0.261 | ||
| Adj Total | 8.840 | 30 | ||||
| Grand Total | 235.500 | 31 |
| Source | SS | df | MS | F | |
| A | 194.5 | 3 | 64.833 | 44.28 | |
| Error | 41.0 | 28 | 1.464 | ||
| Total | 235.5 | 31 |
Comparing ANCOVA with Randomized Block Designs
Some Stata Tricks
| One Factor Design with one Covariate: | |
| anova y a | analysis of variance |
| anova y x a | analysis of covariance |
| anova y x a x*a | tests homogeneity of slopes |
| Two Factor Design with One Covariate: | |
| anova y a b a*b | analysis of variance |
| anova y x a b a*b | analysis of covariance |
| anova y x a b a*b x*a*b | tests homogeneity of slopes |
| One Factor Design with Two Covariates: | |
| anova y a | analysis of variance |
| anova y x z a | analysis of covariance |
| anova y x a x*a | homogeneity of x slopes |
| anova y z a z*a | homogeneity of z slopes |
| Two Factor Design with Two Covariates: | |
| anova y a b a*b | analysis of variance |
| anova y x z a b a*b | analysis of covariance |
| anova y x a b a*b x*a*b | homogeneity of x slopes |
| anova y z a b a*b z*a*b | homogeneity of z slopes |
| Note: Don't forget the | cont option in the ancova |
Stata Example
input x y a x1 x2 x3
42 3 1 1 1 1
57 6 1 1 1 1
33 3 1 1 1 1
47 3 1 1 1 1
32 1 1 1 1 1
35 2 1 1 1 1
33 2 1 1 1 1
39 2 1 1 1 1
47 4 2 -1 1 1
49 5 2 -1 1 1
42 4 2 -1 1 1
41 3 2 -1 1 1
38 2 2 -1 1 1
43 3 2 -1 1 1
48 4 2 -1 1 1
45 3 2 -1 1 1
61 7 3 0 -2 1
65 8 3 0 -2 1
64 7 3 0 -2 1
56 6 3 0 -2 1
52 5 3 0 -2 1
58 6 3 0 -2 1
53 5 3 0 -2 1
54 6 3 0 -2 1
65 7 4 0 0 -3
74 8 4 0 0 -3
80 9 4 0 0 -3
73 8 4 0 0 -3
85 10 4 0 0 -3
82 10 4 0 0 -3
78 9 4 0 0 -3
89 11 4 0 0 -3
end
anova y a c.x
Number of obs = 32 R-squared = 0.9701
Root MSE = .510876 Adj R-squared = 0.9656
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 228.453154 4 57.1132885 218.83 0.0000
|
a | 1.79283521 3 .597611737 2.29 0.1010
x | 33.9531542 1 33.9531542 130.09 0.0000
|
Residual | 7.04684582 27 .26099429
-----------+----------------------------------------------------
Total | 235.5 31 7.59677419
margins a, asbalanced
Predictive margins Number of obs = 32
Expression : Linear prediction, predict()
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
a |
1 | 5.310127 .2881078 18.43 0.000 4.745446 5.874807
2 | 5.325664 .2413402 22.07 0.000 4.852646 5.798682
3 | 5.767353 .1855126 31.09 0.000 5.403755 6.130951
4 | 5.096856 .3869503 13.17 0.000 4.338448 5.855265
------------------------------------------------------------------------------
/* pairwise comparisons using anovalator */
/* these tests have not been adjusted for multiplicity */
anovalator a, pair quietly
anovalator pairwise comparisons for a
Comparison Coef. Std. Err. z P>|z| [95% Conf. Interval]
1 vs 2 -.0155375 .26343 -.059 0.953 -.5318595 .5007846
1 vs 3 -.457227 .369347 -1.24 0.216 -1.181147 .2666942
1 vs 4 .21327 .621578 .343 0.732 -1.005023 1.431564
2 vs 3 -.441689 .325894 -1.36 0.175 -1.080441 .1970623
2 vs 4 .228808 .563495 .406 0.685 -.8756423 1.333258
3 vs 4 .670497 .393934 1.7 0.089 -.1016129 1.442607
/* test for homogeneity of regression slopes */
anova y a c.x a#c.x
Number of obs = 32 R-squared = 0.9719
Root MSE = .525009 Adj R-squared = 0.9637
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 228.884782 7 32.6978259 118.63 0.0000
|
a | .355072259 3 .11835742 0.43 0.7338
x | 25.8488494 1 25.8488494 93.78 0.0000
a#x | .431627333 3 .143875778 0.52 0.6713
|
Residual | 6.61521849 24 .275634104
-----------+----------------------------------------------------
Total | 235.5 31 7.59677419
Stata Example Continued
regress y x x1 x2 x3 Source | SS df MS Number of obs = 32 ---------+------------------------------ F( 4, 27) = 218.83 Model | 228.453154 4 57.1132885 Prob > F = 0.0000 Residual | 7.04684582 27 .26099429 R-squared = 0.9701 ---------+------------------------------ Adj R-squared = 0.9656 Total | 235.50 31 7.59677419 Root MSE = .51088 [remainder of output omitted] regress y x Source | SS df MS Number of obs = 32 ---------+------------------------------ F( 1, 30) = 769.24 Model | 226.660319 1 226.660319 Prob > F = 0.0000 Residual | 8.83968103 30 .294656034 R-squared = 0.9625 ---------+------------------------------ Adj R-squared = 0.9612 Total | 235.50 31 7.59677419 Root MSE = .54282 [remainder of output omitted] regress y x1 x2 x3 Source | SS df MS Number of obs = 32 ---------+------------------------------ F( 3, 28) = 44.28 Model | 194.50 3 64.8333333 Prob > F = 0.0000 Residual | 41.00 28 1.46428571 R-squared = 0.8259 ---------+------------------------------ Adj R-squared = 0.8072 Total | 235.50 31 7.59677419 Root MSE = 1.2101 [remainder of output omitted] Regression Results Summarized Model: M0 R-square 0.9701 Model: M1 R-square 0.9625 Model: M2 R-square 0.8259
F-ratios Using Regression
with 1 and 27 degrees of freedom
with 3 and 27 degrees of freedom
Example with Two Covariates
input id y c1 c2 grp
1 6 1 6 1
2 9 1 7 1
3 8 2 15 1
4 8 3 13 1
5 12 3 18 1
6 12 4 9 1
7 10 4 16 1
8 8 5 10 1
9 12 5 16 1
10 13 6 18 1
11 13 4 12 2
12 16 4 12 2
13 15 5 17 2
14 16 6 9 2
15 19 6 20 2
16 17 8 18 2
17 19 8 16 2
18 23 9 20 2
19 19 10 10 2
20 22 10 17 2
21 20 7 8 3
22 22 7 14 3
23 24 9 11 3
24 26 9 11 3
25 24 10 16 3
26 25 11 20 3
27 28 11 19 3
28 27 12 19 3
29 29 13 12 3
30 26 13 16 3
31 27 7 16 4
32 28 8 10 4
33 25 8 13 4
34 27 9 7 4
35 31 9 15 4
36 29 10 20 4
37 32 10 16 4
38 30 12 21 4
39 32 12 15 4
40 33 14 21 4
end
tabstat y c1 c2, by(grp) stat(n mean sd) col(stat)
Summary for variables: y c1 c2
by categories of: grp
grp | N mean sd
---------+------------------------------
1 | 10 9.8 2.347576
| 10 3.4 1.712698
| 10 12.8 4.491968
---------+------------------------------
2 | 10 17.9 3.107339
| 10 7 2.309401
| 10 15.1 4.040077
---------+------------------------------
3 | 10 25.1 2.726414
| 10 10.2 2.20101
| 10 14.6 4.060651
---------+------------------------------
4 | 10 29.4 2.633122
| 10 9.9 2.18327
| 10 15.4 4.599517
---------+------------------------------
Total | 40 20.55 7.977372
| 40 7.625 3.439495
| 40 14.475 4.260658
----------------------------------------
anova y grp /* 0 covariates */
Number of obs = 40 R-squared = 0.8929
Root MSE = 2.71723 Adj R-squared = 0.8840
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 2216.1 3 738.7 100.05 0.0000
|
grp | 2216.1 3 738.7 100.05 0.0000
|
Residual | 265.8 36 7.38333333
-----------+----------------------------------------------------
Total | 2481.9 39 63.6384615
anova y grp c.c1 /* 1 covariate */
Number of obs = 40 R-squared = 0.9594
Root MSE = 1.69598 Adj R-squared = 0.9548
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 2381.22741 4 595.306852 206.97 0.0000
|
grp | 415.841199 3 138.613733 48.19 0.0000
c1 | 165.127408 1 165.127408 57.41 0.0000
|
Residual | 100.672592 35 2.87635976
-----------+----------------------------------------------------
Total | 2481.9 39 63.6384615
anova y grp c.c1 c.c2 /* 2 covariates */
Number of obs = 40 R-squared = 0.9624
Root MSE = 1.65656 Adj R-squared = 0.9569
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 2388.59757 5 477.719513 174.08 0.0000
|
grp | 420.189396 3 140.063132 51.04 0.0000
c1 | 98.974038 1 98.974038 36.07 0.0000
c2 | 7.37015734 1 7.37015734 2.69 0.1105
|
Residual | 93.3024343 34 2.74418925
-----------+----------------------------------------------------
Total | 2481.9 39 63.6384615
margins grp, asbalanced
Predictive margins Number of obs = 40
Expression : Linear prediction, predict()
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
grp |
1 | 13.78338 .7820854 17.62 0.000 12.25052 15.31624
2 | 18.38456 .537869 34.18 0.000 17.33035 19.43876
3 | 22.77973 .646886 35.21 0.000 21.51186 24.0476
4 | 27.25234 .6098128 44.69 0.000 26.05713 28.44755
------------------------------------------------------------------------------
/* pairwise comparisons using anovalator */
/* these tests have not been adjusted for multiplicity */
anovalator grp, pair quietly
anovalator pairwise comparisons for grp
Comparison Coef. Std. Err. z P>|z| [95% Conf. Interval]
1 vs 2 -4.60118 .88207 -5.22 0.000 -6.330038 -2.872323
1 vs 3 -8.99635 1.21035 -7.43 0.000 -11.36863 -6.624072
1 vs 4 -13.469 1.16021 -11.6 0.000 -15.74298 -11.19494
2 vs 3 -4.39517 .891386 -4.93 0.000 -6.142288 -2.648057
2 vs 4 -8.86778 .852674 -10.4 0.000 -10.53902 -7.196539
3 vs 4 -4.47261 .746185 -5.99 0.000 -5.93513 -3.010083
anova y grp c.c1 c.c1#grp /* check homogeneity of regression for c2 */
Number of obs = 40 R-squared = 0.9598
Root MSE = 1.76482 Adj R-squared = 0.9511
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 2382.23359 7 340.319084 109.27 0.0000
|
grp | 70.1635717 3 23.3878572 7.51 0.0006
c1 | 152.279387 1 152.279387 48.89 0.0000
grp#c1 | 1.00618243 3 .335394144 0.11 0.9550
|
Residual | 99.6664092 32 3.11457529
-----------+----------------------------------------------------
Total | 2481.9 39 63.6384615
anova y grp c.c2 c.c2#grp /* check homogeneity of regression for c2 */
Number of obs = 40 R-squared = 0.9228
Root MSE = 2.44624 Adj R-squared = 0.9060
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 2290.40886 7 327.201265 54.68 0.0000
|
grp | 182.057287 3 60.6857623 10.14 0.0001
c2 | 73.6130056 1 73.6130056 12.30 0.0014
grp#c2 | .785330753 3 .261776918 0.04 0.9876
|
Residual | 191.491142 32 5.98409817
-----------+----------------------------------------------------
Total | 2481.9 39 63.6384615
Linear Statistical Models Course
Phil Ender, 17sep10, 13may06, 11apr06, 25May00