. use "progesterone.dta"
. local i 1
. forvalues k=1(1)22 {
2. gen bf_`i'=cond(time + 8 - `k' > 0, time + 8 - `k', 0)
3. local ++i
4. }
. xtmixed logp group time c.group#c.time c.group#c.bf_15 || _all: bf_*, ///
noconstant covariance(identity) || id: time, covariance(unstr) reml
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log restricted-likelihood = -1060.4749
Iteration 1: log restricted-likelihood = -1060.4749
Computing standard errors:
Mixed-effects REML regression Number of obs = 1130
-----------------------------------------------------------
| No. of Observations per Group
Group Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
_all | 1 1130 1130.0 1130
id | 51 9 22.2 24
-----------------------------------------------------------
Wald chi2(4) = 169.65
Log restricted-likelihood = -1060.4749 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
logp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
group | .1625838 .2322847 0.70 0.484 -.2926859 .6178535
time | .0165396 .0752332 0.22 0.826 -.1309147 .1639939
|
c.group#|
c.time | -.0478925 .0119262 -4.02 0.000 -.0712673 -.0245176
|
c.group#|
c.bf_15 | .2961574 .023245 12.74 0.000 .2505981 .3417168
|
_cons | -.810215 .571292 -1.42 0.156 -1.929927 .3094968
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
_all: Identity |
sd(bf_1..bf_22)(1) | .1272941 .0283529 .0822651 .1969704
-----------------------------+------------------------------------------------
id: Unstructured |
sd(time) | .0322555 .0042943 .0248474 .0418723
sd(_cons) | .8109165 .0839387 .6620145 .9933098
corr(time,_cons) | .146714 .162931 -.1767088 .4415417
-----------------------------+------------------------------------------------
sd(Residual) | .5348411 .0118756 .5120645 .5586307
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(4) = 1313.88 Prob > chi2 = 0.0000
(1) bf_1 bf_2 bf_3 bf_4 bf_5 bf_6 bf_7 bf_8 bf_9 bf_10 bf_11 bf_12 bf_13 bf_14 bf_15 bf_16 bf_17 bf_18 bf_19 bf_20 bf_21 bf_22
. predict b*, reffects level(_all)
. predict b_slope b_intercept, reffects level(id)
. describe b1-b22 b_slope b_intercept
storage display value
variable name type format label variable label
-----------------------------------------------------------------------------
b1 float %9.0g BLUP r.e. for _all: bf_1
b2 float %9.0g BLUP r.e. for _all: bf_2
b3 float %9.0g BLUP r.e. for _all: bf_3
b4 float %9.0g BLUP r.e. for _all: bf_4
b5 float %9.0g BLUP r.e. for _all: bf_5
b6 float %9.0g BLUP r.e. for _all: bf_6
b7 float %9.0g BLUP r.e. for _all: bf_7
b8 float %9.0g BLUP r.e. for _all: bf_8
b9 float %9.0g BLUP r.e. for _all: bf_9
b10 float %9.0g BLUP r.e. for _all: bf_10
b11 float %9.0g BLUP r.e. for _all: bf_11
b12 float %9.0g BLUP r.e. for _all: bf_12
b13 float %9.0g BLUP r.e. for _all: bf_13
b14 float %9.0g BLUP r.e. for _all: bf_14
b15 float %9.0g BLUP r.e. for _all: bf_15
b16 float %9.0g BLUP r.e. for _all: bf_16
b17 float %9.0g BLUP r.e. for _all: bf_17
b18 float %9.0g BLUP r.e. for _all: bf_18
b19 float %9.0g BLUP r.e. for _all: bf_19
b20 float %9.0g BLUP r.e. for _all: bf_20
b21 float %9.0g BLUP r.e. for _all: bf_21
b22 float %9.0g BLUP r.e. for _all: bf_22
b_slope float %9.0g BLUP r.e. for id: time
b_intercept float %9.0g BLUP r.e. for id: _cons
. list b1-b22 in 1
+--------------------------------------------------------------------------------------------------------+
1. | b1 | b2 | b3 | b4 | b5 | b6 | b7 | b8 | b9 |
| -.0188028 | -.041519 | .0152155 | .0209336 | .1021353 | .0111908 | .073038 | .1204596 | .1345333 |
|----------------------+---------------------------------------------------------------------------------|
| b10 | b11 | b12 | b13 | b14 | b15 | b16 | b17 | b18 |
| .0857372 | -.1222687 | -.0596927 | -.1860888 | -.1398948 | -.1364576 | -.0712703 | .0815137 | .0401205 |
|--------------------------------------------------------------------------------------------------------|
| b19 | b20 | b21 | b22 |
| -.0421866 | -.0159566 | -.059587 | -.0945905 |
+--------------------------------------------------------------------------------------------------------+
. list id b_intercept b_slope if (time == 0)
+----------------------------+
| id b_inter~t b_slope |
|----------------------------|
9. | 1 1.042622 .0480065 |
33. | 2 .0632171 .0039163 |
55. | 3 .4821929 -.002078 |
96. | 5 .3447452 .0072103 |
113. | 6 -.1755748 -.0011255 |
|----------------------------|
135. | 7 -.773533 -.0172502 |
155. | 8 -1.546915 .0181355 |
178. | 9 .3440427 -.0033976 |
200. | 10 -.5586036 -.0518299 |
239. | 12 -.0961074 -.0061136 |
|----------------------------|
261. | 13 1.590588 .0276235 |
281. | 14 1.256927 .0462825 |
304. | 15 -.8486717 -.04028 |
313. | 16 .4617817 -.0251528 |
336. | 17 1.265509 -.0205968 |
|----------------------------|
359. | 18 -.0611754 .0272686 |
380. | 19 -.489815 .0068096 |
398. | 20 .2480679 -.0169893 |
418. | 21 -1.659388 -.0081858 |
440. | 22 -.4382117 .0123051 |
|----------------------------|
461. | 23 -1.40461 -.0430066 |
484. | 24 .9932583 -.0116396 |
508. | 25 .2524926 -.0016533 |
532. | 26 -.868067 -.0249842 |
556. | 27 -.3389031 .0188855 |
|----------------------------|
579. | 28 -.3572452 .0161927 |
603. | 29 .4262464 -.0129535 |
625. | 30 -.0119385 .0198855 |
649. | 31 .3262692 .0049711 |
673. | 32 .2951903 .0005212 |
|----------------------------|
697. | 33 -.0786787 .0157303 |
720. | 34 -.0141162 .0158396 |
744. | 35 .5447627 .0214898 |
767. | 36 .2349261 -.0259866 |
791. | 37 -1.106814 -.0165725 |
|----------------------------|
815. | 38 1.112673 .0112458 |
839. | 39 .3800662 -.0408465 |
863. | 40 .1573762 .0051524 |
880. | 41 .0826845 .0213903 |
904. | 42 -1.421825 .0630073 |
|----------------------------|
925. | 43 -.7307276 -.0148385 |
949. | 44 -.4392257 .0056186 |
973. | 45 -.9719065 .004091 |
997. | 46 2.067266 -.0169515 |
1020. | 47 -.0666351 .012473 |
|----------------------------|
1044. | 48 -.6482108 -.106966 |
1068. | 49 .4693395 .0030355 |
1092. | 50 .0196799 .0269524 |
1115. | 51 -.2001556 -.0092421 |
+----------------------------+