mixed - Stata

mixed — Multilevel mixed-effects linear regression 31
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
mare: Identity
Residual: AR(2)
var(_cons) 7.092439 4.401937 2.101337 23.93843
phi1 .5386104 .0624899 .4161325 .6610883
phi2 .144671 .0632041 .0207933 .2685488
var(e) 14.25104 2.435238 10.19512 19.92054
LR test vs. linear regression: chi2(3) = 251.67 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
We picked an order of 2 as a guess, but we could have used LR tests of competing AR models to
determine the optimal order, because models of smaller order are nested within those of larger order.
Example 9
Fitzmaurice, Laird, and Ware (2011, chap. 7) analyzed data on 37 subjects who participated in an
exercise therapy trial.
. use http://www.stata-press.com/data/r13/exercise
(Exercise Therapy Trial)
. describe
Contains data from http://www.stata-press.com/data/r13/exercise.dta
obs: 259 Exercise Therapy Trial
vars: 4 24 Jun 2012 18:35
size: 1,036 (_dta has notes)
storage display value
variable name type format label variable label
id byte %9.0g Person ID
day byte %9.0g Day of measurement
program byte %9.0g 1 = reps increase; 2 = weights
increase
strength byte %9.0g Strength measurement
Sorted by: id day
Subjects (variable id) were placed on either an increased-repetition regimen (program==1) or a program
that kept the repetitions constant but increased weight (program==2). Muscle-strength measurements
(variable strength) were taken at baseline (day==0) and then every two days over the next twelve
days.
Following Fitzmaurice, Laird, and Ware (2011, chap. 7), and to demonstrate fitting residual-error
structures to data collected at uneven time points, we confine our analysis to those data collected at
baseline and at days 4, 6, 8, and 12. We fit a full two-way factorial model of strength on program
and day, with an unstructured residual-error covariance matrix over those repeated measurements
taken on the same subject: