Note:This FAQ is for Stata 10 and older versions of Stata.
In Stata 11, the margins command can be used to calculate least square means.
How do I calculate least square means in Stata?
| Title |
|
Use of adjust |
| Author |
Weihua Guan, StataCorp
|
Currently there is no convenient command in Stata to calculate the least
square means, but one may use the
adjust command to
compute them manually. LSMEANS are just the predicted linear combination xb
while holding the other covariates at values 1/n, where n is the number of
categories in the corresponding discrete covariates. For continuous
covariates, one just holds their values at their means when making the
prediction.
Following is an example using the auto dataset:
. sysuse auto, clear
(1978 automobile data))
. anova mpg foreign rep78
Number of obs = 69 R-squared = 0.2825
Root MSE = 5.16246 Adj R-squared = 0.2256
| | Source | | Partial SS df MS F Prob>F |
| | | | |
| | Model | | 661.18952 5 132.2379 4.96 0.0007 |
| | | | |
| | foreign | | 111.77375 1 111.77375 4.19 0.0447 |
| | rep78 | | 179.18901 4 44.797252 1.68 0.1655 |
| | | | |
| | Residual | | 1679.0134 63 26.651006 |
| | | | |
| | Total | | 2340.2029 68 34.414749 |
.
xi: regress mpg i.foreign i.rep78
i.foreign _Iforeign_0-1 (naturally coded; _Iforeign_0 omitted)
i.rep78 _Irep78_1-5 (naturally coded; _Irep78_1 omitted)
| Source | | SS df MS | Number of obs = 69
| | | F(5, 63) = 4.96 |
| Model | | 661.189524 5 132.237905 | Prob > F = 0.0007 |
| Residual | | 1679.01337 63 26.6510059 | R-squared = 0.2825 |
| | | Adj R-squared = 0.2256 |
| Total | | 2340.2029 68 34.4147485 | Root MSE = 5.1625 |
|
| mpg | | Coefficient Std. err. t P>|t| [95% conf. interval] |
| | | |
| _Iforeign_1 | | 3.556584 1.736681 2.05 0.045 .0861046 7.027064 |
| _Irep78_2 | | -1.875 4.081284 -0.46 0.648 -10.0308 6.280795 |
| _Irep78_3 | | -1.922325 3.774126 -0.51 0.612 -9.464315 5.619665 |
| _Irep78_4 | | -1.111626 3.944633 -0.28 0.779 -8.994346 6.771095 |
| _Irep78_5 | | 3.453704 4.215132 0.82 0.416 -4.969565 11.87697 |
| _cons | | 21 3.650411 5.75 0.000 13.70524 28.29476 |
|
.
adjust _Irep78_2=.2 _Irep78_3=.2 _Irep78_4=.2 _Irep78_5=.2, by(foreign) se
| |
| Dependent variable: mpg Command: regress
Variable left as is: _Iforeign_1
Covariates set to value: _Irep78_2 = .2, _Irep78_3 = .2, _Irep78_4 = .2,
_Irep78_5 = .2 |
| |
|
| Car | | |
| origin | | xb stdp |
| | | |
| Domestic | | 20.709 (1.04909) |
| Foreign | | 24.2655 (1.55104) |
|
Key: xb = Linear Prediction
stdp = Standard Error
There are five outcomes in the variable rep78. The values of the
dummy variables _Irep* are held as 1/5 = 0.2.
Given that adjust is just another form of predict, we could
also use predict to reproduce the results.
