------------------------------------------------------------------------------------------------------------------------------------------------------
name: STATA_Chapter8
log: C:\Dropbox\PilesOfVariance\Chapter8\STATA\STATA_Chapter8_Output.smcl
log type: smcl
opened on: 21 Oct 2014, 17:37:30
.
. display as result "Chapter 8: Descriptive Statistics for Time-Invariant Variables"
Chapter 8: Descriptive Statistics for Time-Invariant Variables
. preserve
. collapse women baseage mood stressor, by(personid)
. summarize women baseage mood stressor
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
women | 105 .7333333 .4443376 0 1
baseage | 105 80.12967 6.105009 69.70294 95.30732
mood | 105 1.205143 .2719591 1 2.7
stressor | 105 .4542857 .3391246 0 1
. restore
.
. display as result "Chapter 8: Descriptive Statistics for Time-Varying Variables"
Chapter 8: Descriptive Statistics for Time-Varying Variables
. summarize symptoms mood stressor wpmood wpstressor
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
symptoms | 509 1.273084 1.324394 0 5
mood | 509 1.203143 .3799994 1 3.6
stressor | 509 .4518664 .4981674 0 1
wpmood | 509 .0004715 .2729248 -.72 1.28
wpstressor | 509 .0039293 .3671344 -.8 .8
.
. display as result "Eq 8.1: Conditional Baseline Model"
Eq 8.1: Conditional Baseline Model
. display as result "Just Sex and Age in the Model for the Means"
Just Sex and Age in the Model for the Means
. mixed symptoms c.women c.age80 c.women#c.age80, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -716.28897
Iteration 1: log likelihood = -716.28897
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(3) = 10.85
Log likelihood = -716.28897 Prob > chi2 = 0.0126
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5306419 .2451324 -2.16 0.030 -1.011093 -.0501912
age80 | .0999995 .0370575 2.70 0.007 .0273681 .1726309
|
c.women#c.age80 | -.1104279 .0422013 -2.62 0.009 -.1931409 -.027715
|
_cons | 1.712739 .2107731 8.13 0.000 1.299632 2.125847
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | 1.072738 .1667213 .7910477 1.454737
-----------------------------+------------------------------------------------
var(Residual) | .6151891 .0433293 .5358663 .706254
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 271.56 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -716.289 6 1444.578 1460.502
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. estimates store FitSexAge,
. predict PredSexAge, xb,
.
. display as result "Ch 8: Empty Means, Random Intercept Model for Negative Mood Predictor"
Ch 8: Empty Means, Random Intercept Model for Negative Mood Predictor
. mixed mood , ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -186.85043
Iteration 1: log likelihood = -186.85043
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(0) = .
Log likelihood = -186.85043 Prob > chi2 = .
------------------------------------------------------------------------------
mood | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.204359 .0261434 46.07 0.000 1.153118 1.255599
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .0523986 .010103 .0359086 .076461
-----------------------------+------------------------------------------------
var(Residual) | .0930473 .0065628 .081034 .1068416
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 84.78 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -186.8504 3 379.7009 387.6627
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. estat icc,
Intraclass correlation
------------------------------------------------------------------------------
Level | ICC Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid | .3602617 .0494757 .2699308 .4617044
------------------------------------------------------------------------------
. estat wcorrelation, covariance,
Covariances for personid = 102:
obs | 1 2 3 4 5
-------------+----------------------------------------
1 | 0.145
2 | 0.052 0.145
3 | 0.052 0.052 0.145
4 | 0.052 0.052 0.052 0.145
5 | 0.052 0.052 0.052 0.052 0.145
. estat wcorrelation,
Standard deviations and correlations for personid = 102:
Standard deviations:
obs | 1 2 3 4 5
-------------+----------------------------------------
sd | 0.381 0.381 0.381 0.381 0.381
Correlations:
obs | 1 2 3 4 5
-------------+----------------------------------------
1 | 1.000
2 | 0.360 1.000
3 | 0.360 0.360 1.000
4 | 0.360 0.360 0.360 1.000
5 | 0.360 0.360 0.360 0.360 1.000
.
. display as result "Eq 8.3: Adding BP and WP Effects of Negative Mood to the Model for the Means"
Eq 8.3: Adding BP and WP Effects of Negative Mood to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood"
Using Person-Mean-Centering for Negative Mood
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -702.83657
Iteration 1: log likelihood = -702.83657
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(5) = 43.71
Log likelihood = -702.83657 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5181351 .2175057 -2.38 0.017 -.9444384 -.0918317
age80 | .0668994 .0334941 2.00 0.046 .0012521 .1325467
|
c.women#c.age80 | -.0917624 .0376373 -2.44 0.015 -.16553 -.0179947
|
pmmood2 | 1.970103 .3687321 5.34 0.000 1.247401 2.692804
wpmood | .1591032 .1277162 1.25 0.213 -.091216 .4094225
_cons | 3.265483 .3458012 9.44 0.000 2.587725 3.943241
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .8161533 .1313714 .5953315 1.118883
-----------------------------+------------------------------------------------
var(Residual) | .6127342 .0431742 .5336974 .7034758
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 212.44 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -702.8366 8 1421.673 1442.905
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of BP and WP Negative Mood Fixed Effects
. test (c.pmmood2=0) (c.wpmood=0)
( 1) [symptoms]pmmood2 = 0
( 2) [symptoms]wpmood = 0
chi2( 2) = 30.18
Prob > chi2 = 0.0000
. * Age Slope for Men
. lincom c.age80*1 + c.women#c.age80*0
( 1) [symptoms]age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0668994 .0334941 2.00 0.046 .0012521 .1325467
------------------------------------------------------------------------------
. * Age Slope for Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.024863 .018099 -1.37 0.170 -.0603363 .0106103
------------------------------------------------------------------------------
. * Contextual Negative Mood Effect
. lincom c.wpmood*-1 + c.pmmood2*1
( 1) [symptoms]pmmood2 - [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.810999 .3909927 4.63 0.000 1.044668 2.577331
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=2 WPmood=-1
. lincom _cons*1 + c.pmmood2*0 + c.wpmood*-1
( 1) - [symptoms]wpmood + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3.10638 .3687701 8.42 0.000 2.383604 3.829156
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=2 WPmood=0
. lincom _cons*1 + c.pmmood2*0 + c.wpmood*0
( 1) [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3.265483 .3458012 9.44 0.000 2.587725 3.943241
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=2 WPmood=1
. lincom _cons*1 + c.pmmood2*0 + c.wpmood*1
( 1) [symptoms]wpmood + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3.424586 .3684948 9.29 0.000 2.70235 4.146823
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=3 WPmood=-1
. lincom _cons*1 + c.pmmood2*1 + c.wpmood*-1
( 1) [symptoms]pmmood2 - [symptoms]wpmood + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 5.076482 .697874 7.27 0.000 3.708675 6.44429
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=3 WPmood=0
. lincom _cons*1 + c.pmmood2*1 + c.wpmood*0
( 1) [symptoms]pmmood2 + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 5.235586 .6855762 7.64 0.000 3.891881 6.57929
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=3 WPmood=1
. lincom _cons*1 + c.pmmood2*1 + c.wpmood*1
( 1) [symptoms]pmmood2 + [symptoms]wpmood + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 5.394689 .6968675 7.74 0.000 4.028854 6.760524
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=1 WPmood=0
. lincom _cons*1 + c.pmmood2*-1 + c.wpmood*0
( 1) - [symptoms]pmmood2 + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.29538 .2026549 6.39 0.000 .898184 1.692577
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=2 WPmood=0
. lincom _cons*1 + c.pmmood2*0 + c.wpmood*0
( 1) [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3.265483 .3458012 9.44 0.000 2.587725 3.943241
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=3 WPmood=0
. lincom _cons*1 + c.pmmood2*1 + c.wpmood*0
( 1) [symptoms]pmmood2 + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 5.235586 .6855762 7.64 0.000 3.891881 6.57929
------------------------------------------------------------------------------
. * Predicted Symptoms for PMmood=4 WPmood=0
. lincom _cons*1 + c.pmmood2*2 + c.wpmood*0
( 1) 2*[symptoms]pmmood2 + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 7.205688 1.045169 6.89 0.000 5.157195 9.254182
------------------------------------------------------------------------------
. estimates store FitBPWPMood,
. lrtest FitBPWPMood FitSexAge,
Likelihood-ratio test LR chi2(2) = 26.90
(Assumption: FitSexAge nested in FitBPWPMood) Prob > chi2 = 0.0000
. predict PredBPWPMood, xb,
. corr symptoms PredBPWPMood
(obs=509)
| symptoms PredBP~d
-------------+------------------
symptoms | 1.0000
PredBPWPMood | 0.4424 1.0000
.
. display as result "Eq 8.5: Adding Smushed Effect of Negative Mood to the Model for the Means"
Eq 8.5: Adding Smushed Effect of Negative Mood to the Model for the Means
. display as result "Using Grand-Mean-Centering for Negative Mood"
Using Grand-Mean-Centering for Negative Mood
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.mood2, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -712.74141
Iteration 1: log likelihood = -712.74141
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(4) = 18.85
Log likelihood = -712.74141 Prob > chi2 = 0.0008
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5323768 .2372403 -2.24 0.025 -.9973592 -.0673944
age80 | .0953524 .0359149 2.65 0.008 .0249605 .1657444
|
c.women#c.age80 | -.1083282 .040858 -2.65 0.008 -.1884084 -.0282479
|
mood2 | .3294958 .1217344 2.71 0.007 .0909007 .5680909
_cons | 1.975617 .2260668 8.74 0.000 1.532534 2.4187
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .9962318 .1584999 .729348 1.360774
-----------------------------+------------------------------------------------
var(Residual) | .6149156 .0434739 .5353483 .7063089
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 234.47 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -712.7414 7 1439.483 1458.061
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Age Slope for Men
. lincom c.age80*1 + c.women#c.age80*0
( 1) [symptoms]age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0953524 .0359149 2.65 0.008 .0249605 .1657444
------------------------------------------------------------------------------
. * Age Slope for Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0129758 .0195592 -0.66 0.507 -.0513111 .0253596
------------------------------------------------------------------------------
.
. display as result "Eq 8.7: Adding a Contextual Effect of Negative Mood to the Model for the Means"
Eq 8.7: Adding a Contextual Effect of Negative Mood to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood"
Using Person-Mean-Centering for Negative Mood
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.mood2 c.pmmood2, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -702.83657
Iteration 1: log likelihood = -702.83657
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(5) = 43.71
Log likelihood = -702.83657 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5181351 .2175057 -2.38 0.017 -.9444384 -.0918317
age80 | .0668994 .0334941 2.00 0.046 .0012521 .1325467
|
c.women#c.age80 | -.0917624 .0376373 -2.44 0.015 -.16553 -.0179947
|
mood2 | .1591032 .1277162 1.25 0.213 -.091216 .4094225
pmmood2 | 1.810999 .3909927 4.63 0.000 1.044668 2.577331
_cons | 3.265483 .3458012 9.44 0.000 2.587725 3.943241
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .8161533 .1313714 .5953315 1.118883
-----------------------------+------------------------------------------------
var(Residual) | .6127342 .0431742 .5336974 .7034758
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 212.44 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -702.8366 8 1421.673 1442.905
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Age Slope for Men
. lincom c.age80*1 + c.women#c.age80*0
( 1) [symptoms]age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0668994 .0334941 2.00 0.046 .0012521 .1325467
------------------------------------------------------------------------------
. * Age Slope for Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.024863 .018099 -1.37 0.170 -.0603363 .0106103
------------------------------------------------------------------------------
. * Between-Person Negative Mood Effect
. lincom c.mood2*1 + c.pmmood2*1
( 1) [symptoms]mood2 + [symptoms]pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.970103 .3687321 5.34 0.000 1.247401 2.692804
------------------------------------------------------------------------------
. estimates store FitBPWPMood,
. predict PredBPWPMood2, xb,
. corr symptoms PredBPWPMood2
(obs=509)
| symptoms PredBP~2
-------------+------------------
symptoms | 1.0000
PredBPWPMo~2 | 0.4424 1.0000
.
. display as result "Eq 8.9: Adding a Random Effect of Negative Mood to the Model for the Variance"
Eq 8.9: Adding a Random Effect of Negative Mood to the Model for the Variance
. display as result "Using Person-Mean-Centering for Negative Mood"
Using Person-Mean-Centering for Negative Mood
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood, ///
> || personid: wpmood, variance mle covariance(unstructured),
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -703.81763
Iteration 1: log likelihood = -702.11277 (not concave)
Iteration 2: log likelihood = -702.07622 (backed up)
Iteration 3: log likelihood = -702.07504
Iteration 4: log likelihood = -702.07504
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(5) = 43.15
Log likelihood = -702.07504 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5361315 .2162353 -2.48 0.013 -.9599449 -.1123181
age80 | .0663307 .0331976 2.00 0.046 .0012647 .1313967
|
c.women#c.age80 | -.092706 .0373305 -2.48 0.013 -.1658724 -.0195395
|
pmmood2 | 1.961056 .3674752 5.34 0.000 1.240818 2.681294
wpmood | .1588549 .1349588 1.18 0.239 -.1056596 .4233693
_cons | 3.272504 .3445128 9.50 0.000 2.597271 3.947736
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Unstructured |
var(wpmood) | .0623289 .1402441 .0007576 5.128021
var(_cons) | .8171862 .1314572 .5961999 1.120083
cov(wpmood,_cons) | .1325926 .1355438 -.1330683 .3982535
-----------------------------+------------------------------------------------
var(Residual) | .6066193 .0445772 .5252499 .700594
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(3) = 213.97 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -702.075 10 1424.15 1450.69
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. estat recovariance, relevel(personid),
Random-effects covariance matrix for level personid
| wpmood _cons
-------------+----------------------
wpmood | .0623289
_cons | .1325926 .8171862
. estat recovariance, relevel(personid) correlation,
Random-effects correlation matrix for level personid
| wpmood _cons
-------------+----------------------
wpmood | 1
_cons | .5875086 1
. estimates store FitRandPMC,
. lrtest FitRandPMC FitBPWPMood,
Likelihood-ratio test LR chi2(2) = 1.52
(Assumption: FitBPWPMood nested in FitRandPMC) Prob > chi2 = 0.4670
Note: The reported degrees of freedom assumes the null hypothesis is not on the boundary of the parameter space. If this is not true, then the
reported test is conservative.
.
. display as result "Eq 8.9: Adding a Random Effect of Negative Mood to the Model for the Variance"
Eq 8.9: Adding a Random Effect of Negative Mood to the Model for the Variance
. display as result "Using Grand-Mean-Centering for Negative Mood"
Using Grand-Mean-Centering for Negative Mood
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.mood2 c.pmmood2, ///
> || personid: mood2, variance mle covariance(unstructured),
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -702.57971
Iteration 1: log likelihood = -701.76714
Iteration 2: log likelihood = -701.70351
Iteration 3: log likelihood = -701.70168
Iteration 4: log likelihood = -701.70167
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(5) = 40.91
Log likelihood = -701.70167 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5157588 .215236 -2.40 0.017 -.9376136 -.0939041
age80 | .0620166 .0334933 1.85 0.064 -.0036291 .1276624
|
c.women#c.age80 | -.0870082 .0376347 -2.31 0.021 -.1607707 -.0132456
|
mood2 | .1711301 .1347346 1.27 0.204 -.0929448 .435205
pmmood2 | 1.927591 .3966332 4.86 0.000 1.150204 2.704977
_cons | 3.366619 .3693147 9.12 0.000 2.642775 4.090463
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Unstructured |
var(mood2) | .0627431 .1379603 .0008432 4.668737
var(_cons) | 1.099692 .2844727 .6623364 1.825842
cov(mood2,_cons) | .207262 .1692534 -.1244686 .5389925
-----------------------------+------------------------------------------------
var(Residual) | .6075802 .0445893 .5261813 .7015713
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(3) = 214.71 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -701.7017 10 1423.403 1449.943
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. estat recovariance, relevel(personid),
Random-effects covariance matrix for level personid
| mood2 _cons
-------------+----------------------
mood2 | .0627431
_cons | .207262 1.099692
. estat recovariance, relevel(personid) correlation,
Random-effects correlation matrix for level personid
| mood2 _cons
-------------+----------------------
mood2 | 1
_cons | .7890441 1
. estimates store FitRandGMC,
. lrtest FitRandGMC FitBPWPMood,
Likelihood-ratio test LR chi2(2) = 2.27
(Assumption: FitBPWPMood nested in FitRandGMC) Prob > chi2 = 0.3215
Note: The reported degrees of freedom assumes the null hypothesis is not on the boundary of the parameter space. If this is not true, then the
reported test is conservative.
.
. display as result "Eq 8.10: Adding Contextual and WP Effects of Stressors to the Model for the Means"
Eq 8.10: Adding Contextual and WP Effects of Stressors to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -696.4038
Iteration 1: log likelihood = -696.4038
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(7) = 62.25
Log likelihood = -696.4038 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.5124974 .2054052 -2.50 0.013 -.9150842 -.1099105
age80 | .072878 .0317028 2.30 0.022 .0107417 .1350143
|
c.women#c.age80 | -.0936186 .0355652 -2.63 0.008 -.1633251 -.0239122
|
pmmood2 | 1.476796 .3742349 3.95 0.000 .7433088 2.210282
wpmood | .133879 .1305171 1.03 0.305 -.1219298 .3896878
pmstressor40 | .9432441 .3031312 3.11 0.002 .3491179 1.53737
stressor | .0855467 .0969043 0.88 0.377 -.1043822 .2754757
_cons | 2.783694 .3518661 7.91 0.000 2.09405 3.473339
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .7135228 .1172055 .5171146 .9845298
-----------------------------+------------------------------------------------
var(Residual) | .6115965 .0431013 .5326941 .7021858
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 187.75 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -696.4038 10 1412.808 1439.347
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of Stressor Fixed Effects
. test (c.pmstressor40=0) (c.stressor=0)
( 1) [symptoms]pmstressor40 = 0
( 2) [symptoms]stressor = 0
chi2( 2) = 13.58
Prob > chi2 = 0.0011
. * Intercept for Women
. lincom _cons*1 + c.women*1
( 1) [symptoms]women + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.271197 .3230023 7.03 0.000 1.638124 2.90427
------------------------------------------------------------------------------
. * Age Slope for Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0207407 .0171221 -1.21 0.226 -.0542993 .012818
------------------------------------------------------------------------------
. * Contextual Negative Mood Effect
. lincom c.wpmood*-1 + c.pmmood2*1
( 1) [symptoms]pmmood2 - [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.342917 .3969366 3.38 0.001 .5649351 2.120898
------------------------------------------------------------------------------
. * Between-Person Stressor Effect
. lincom c.stressor*1 + c.pmstressor40*1
( 1) [symptoms]pmstressor40 + [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.028791 .2873826 3.58 0.000 .4655313 1.59205
------------------------------------------------------------------------------
. * Contextual Negative Mood Effect Per Day
. lincom c.pmmood2*.2
( 1) .2*[symptoms]pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .2953591 .074847 3.95 0.000 .1486618 .4420565
------------------------------------------------------------------------------
. estimates store FitBPWPStressor,
. lrtest FitBPWPStressor FitBPWPMood,
Likelihood-ratio test LR chi2(2) = 12.87
(Assumption: FitBPWPMood nested in FitBPWPStres~r) Prob > chi2 = 0.0016
. lrtest FitBPWPStressor FitSexAge,
Likelihood-ratio test LR chi2(4) = 39.77
(Assumption: FitSexAge nested in FitBPWPStres~r) Prob > chi2 = 0.0000
. predict PredBPWPStressor, xb,
. corr symptoms PredBPWPStressor
(obs=509)
| symptoms PredBP~r
-------------+------------------
symptoms | 1.0000
PredBPWPSt~r | 0.5029 1.0000
.
. display as result "Ch 8: Adding Random WP Effect of Stressors to the Model for the Variance"
Ch 8: Adding Random WP Effect of Stressors to the Model for the Variance
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor, ///
> || personid: stressor, variance mle covariance(unstructured),
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -694.39369
Iteration 1: log likelihood = -692.5
Iteration 2: log likelihood = -692.49004
Iteration 3: log likelihood = -692.49004
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(7) = 64.14
Log likelihood = -692.49004 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
women | -.3894029 .1934764 -2.01 0.044 -.7686097 -.010196
age80 | .0696287 .0302738 2.30 0.021 .0102932 .1289642
|
c.women#c.age80 | -.0911835 .0338363 -2.69 0.007 -.1575015 -.0248655
|
pmmood2 | 1.586619 .3762597 4.22 0.000 .8491638 2.324075
wpmood | .1396613 .1303736 1.07 0.284 -.1158663 .3951889
pmstressor40 | .9331686 .2939699 3.17 0.002 .3569981 1.509339
stressor | .0774159 .1000749 0.77 0.439 -.1187274 .2735591
_cons | 2.784866 .3472852 8.02 0.000 2.1042 3.465532
---------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Unstructured |
var(stressor) | .0656054 .0476753 .0157897 .2725876
var(_cons) | .4973896 .1095432 .3230214 .7658825
cov(stressor,_cons) | .1806417 .0587607 .0654729 .2958105
-----------------------------+------------------------------------------------
var(Residual) | .6067299 .0427596 .5284532 .6966014
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(3) = 195.58 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -692.49 12 1408.98 1440.828
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. estat recovariance, relevel(personid),
Random-effects covariance matrix for level personid
| stressor _cons
-------------+----------------------
stressor | .0656054
_cons | .1806417 .4973896
. estat recovariance, relevel(personid) correlation,
Random-effects correlation matrix for level personid
| stressor _cons
-------------+----------------------
stressor | 1
_cons | .9999998 1
. estimates store FitBPWPStressorR,
. lrtest FitBPWPStressorR FitBPWPStressor,
Likelihood-ratio test LR chi2(2) = 7.83
(Assumption: FitBPWPStres~r nested in FitBPWPStres~R) Prob > chi2 = 0.0200
Note: The reported degrees of freedom assumes the null hypothesis is not on the boundary of the parameter space. If this is not true, then the
reported test is conservative.
.
. display as result "Eq 8.11: Adding 4 Sex*Negative Mood and Sex*Stressors Interactions to the Model for the Means"
Eq 8.11: Adding 4 Sex*Negative Mood and Sex*Stressors Interactions to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor ///
> c.women#c.pmmood2 c.women#c.wpmood c.women#c.pmstressor40 c.women#c.stressor, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -691.93162
Iteration 1: log likelihood = -691.93162
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(11) = 75.19
Log likelihood = -691.93162 Prob > chi2 = 0.0000
----------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
women | -.6743644 .8371644 -0.81 0.421 -2.315176 .9664477
age80 | .0641499 .0335941 1.91 0.056 -.0016934 .1299931
|
c.women#c.age80 | -.0843482 .0374857 -2.25 0.024 -.1578189 -.0108776
|
pmmood2 | 1.814308 .9205988 1.97 0.049 .0099669 3.618648
wpmood | -.1555315 .2729226 -0.57 0.569 -.69045 .379387
pmstressor40 | 1.984957 .6665917 2.98 0.003 .6784612 3.291453
stressor | .2435261 .1721403 1.41 0.157 -.0938626 .5809148
|
c.women#c.pmmood2 | -.4318512 1.001718 -0.43 0.666 -2.395183 1.531481
|
c.women#c.wpmood | .3855131 .3106587 1.24 0.215 -.2233668 .994393
|
c.women#c.pmstressor40 | -1.276958 .7441283 -1.72 0.086 -2.735423 .1815068
|
c.women#c.stressor | -.2336123 .2080174 -1.12 0.261 -.6413188 .1740943
|
_cons | 2.915829 .7655367 3.81 0.000 1.415404 4.416253
----------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6630245 .1099578 .4790313 .9176883
-----------------------------+------------------------------------------------
var(Residual) | .6080567 .0428379 .5296348 .6980905
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 176.93 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -691.9316 14 1411.863 1449.019
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of 4 Sex Interactions
. test (c.women#c.pmmood2=0) (c.women#c.wpmood=0) (c.women#c.pmstressor40=0) (c.women#c.stressor=0)
( 1) [symptoms]c.women#c.pmmood2 = 0
( 2) [symptoms]c.women#c.wpmood = 0
( 3) [symptoms]c.women#c.pmstressor40 = 0
( 4) [symptoms]c.women#c.stressor = 0
chi2( 4) = 9.16
Prob > chi2 = 0.0572
. * Intercept: Men
. lincom _cons*1 + c.women*0
( 1) [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.915829 .7655367 3.81 0.000 1.415404 4.416253
------------------------------------------------------------------------------
. * Intercept: Women
. lincom _cons*1 + c.women*1
( 1) [symptoms]women + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.241464 .3388183 6.62 0.000 1.577393 2.905536
------------------------------------------------------------------------------
. * Intercept: Sex Diff
. lincom c.women*1
( 1) [symptoms]women = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.6743644 .8371644 -0.81 0.421 -2.315176 .9664477
------------------------------------------------------------------------------
. * Age Slope: Men
. lincom c.age80*1 + c.women#c.age80*0
( 1) [symptoms]age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0641499 .0335941 1.91 0.056 -.0016934 .1299931
------------------------------------------------------------------------------
. * Age Slope: Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0201983 .0166318 -1.21 0.225 -.052796 .0123993
------------------------------------------------------------------------------
. * Age Slope: Sex Diff
. lincom c.women#c.age80*1
( 1) [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0843482 .0374857 -2.25 0.024 -.1578189 -.0108776
------------------------------------------------------------------------------
. * BP Negative Mood: Men
. lincom c.pmmood2*1 + c.women#c.pmmood2*0
( 1) [symptoms]pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.814308 .9205988 1.97 0.049 .0099669 3.618648
------------------------------------------------------------------------------
. * BP Negative Mood: Women
. lincom c.pmmood2*1 + c.women#c.pmmood2*1
( 1) [symptoms]pmmood2 + [symptoms]c.women#c.pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.382456 .3948894 3.50 0.000 .6084873 2.156425
------------------------------------------------------------------------------
. * BP Negative Mood: Sex Diff
. lincom c.women#c.pmmood2*1
( 1) [symptoms]c.women#c.pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.4318512 1.001718 -0.43 0.666 -2.395183 1.531481
------------------------------------------------------------------------------
. * Contextual Negative Mood: Men
. lincom c.wpmood*-1 + c.women#c.wpmood *0 + c.pmmood2*1 + c.women#c.pmmood2*0
( 1) [symptoms]pmmood2 - [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.969839 .9533275 2.07 0.039 .1013514 3.838327
------------------------------------------------------------------------------
. * Contextual Negative Mood: Women
. lincom c.wpmood*-1 + c.women#c.wpmood*-1 + c.pmmood2*1 + c.women#c.pmmood2*1
( 1) [symptoms]pmmood2 - [symptoms]wpmood + [symptoms]c.women#c.pmmood2 - [symptoms]c.women#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.152475 .4234686 2.72 0.006 .3224915 1.982458
------------------------------------------------------------------------------
. * Contextual Negative Mood: Sex Diff
. lincom c.women#c.wpmood*-1 + c.women#c.pmmood2*1
( 1) [symptoms]c.women#c.pmmood2 - [symptoms]c.women#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.8173643 1.043149 -0.78 0.433 -2.861898 1.227169
------------------------------------------------------------------------------
. * WP Negative Mood: Men
. lincom c.wpmood*1 + c.women#c.wpmood*0
( 1) [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.1555315 .2729226 -0.57 0.569 -.69045 .379387
------------------------------------------------------------------------------
. * WP Negative Mood: Women
. lincom c.wpmood*1 + c.women#c.wpmood*1
( 1) [symptoms]wpmood + [symptoms]c.women#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .2299816 .1483984 1.55 0.121 -.0608739 .5208371
------------------------------------------------------------------------------
. * WP Negative Mood: Sex Diff
. lincom c.women#c.wpmood*1
( 1) [symptoms]c.women#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .3855131 .3106587 1.24 0.215 -.2233668 .994393
------------------------------------------------------------------------------
. * BP Stressors: Men
. lincom c.stressor*1 + c.women#c.stressor*0 + c.pmstressor40*1 + c.women#c.pmstressor40*0
( 1) [symptoms]pmstressor40 + [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.228483 .6435076 3.46 0.001 .9672314 3.489735
------------------------------------------------------------------------------
. * BP Stressors: Women
. lincom c.stressor*1 + c.women#c.stressor*1 + c.pmstressor40*1 + c.women#c.pmstressor40*1
( 1) [symptoms]pmstressor40 + [symptoms]stressor + [symptoms]c.women#c.pmstressor40 + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .7179129 .3097227 2.32 0.020 .1108675 1.324958
------------------------------------------------------------------------------
. * BP Stressors: Sex Diff
. lincom c.women#c.stressor*1 + c.women#c.pmstressor40*1
( 1) [symptoms]c.women#c.pmstressor40 + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.51057 .714164 -2.12 0.034 -2.910306 -.1108345
------------------------------------------------------------------------------
. * Contextual Stressors: Men
. lincom c.pmstressor40*1 + c.women#c.pmstressor40*0
( 1) [symptoms]pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.984957 .6665917 2.98 0.003 .6784612 3.291453
------------------------------------------------------------------------------
. * Contextual Stressors: Women
. lincom c.pmstressor40*1 + c.women#c.pmstressor40*1
( 1) [symptoms]pmstressor40 + [symptoms]c.women#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .7079991 .3307301 2.14 0.032 .0597799 1.356218
------------------------------------------------------------------------------
. * Contextual Stressors: Sex Diff
. lincom c.women#c.pmstressor40*1
( 1) [symptoms]c.women#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.276958 .7441283 -1.72 0.086 -2.735423 .1815068
------------------------------------------------------------------------------
. * WP Stressors: Men
. lincom c.stressor*1 + c.women#c.stressor*0
( 1) [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .2435261 .1721403 1.41 0.157 -.0938626 .5809148
------------------------------------------------------------------------------
. * WP Stressors: Women
. lincom c.stressor*1 + c.women#c.stressor*1
( 1) [symptoms]stressor + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0099138 .116786 0.08 0.932 -.2189825 .2388101
------------------------------------------------------------------------------
. * WP Stressors: Sex Diff
. lincom c.women#c.stressor*1
( 1) [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.2336123 .2080174 -1.12 0.261 -.6413188 .1740943
------------------------------------------------------------------------------
. * Contextual Stressors per Day
. lincom c.pmstressor40*.2
( 1) .2*[symptoms]pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .3969914 .1333183 2.98 0.003 .1356922 .6582905
------------------------------------------------------------------------------
. estimates store FitSex4,
. lrtest FitSex4 FitBPWPStressor,
Likelihood-ratio test LR chi2(4) = 8.94
(Assumption: FitBPWPStres~r nested in FitSex4) Prob > chi2 = 0.0625
. predict PredSex4, xb,
. corr symptoms PredSex4
(obs=509)
| symptoms PredSex4
-------------+------------------
symptoms | 1.0000
PredSex4 | 0.5302 1.0000
.
. display as result "Eq 8.11reduced: Adding Only 2 Sex*Stressors Interactions to the Model for the Means"
Eq 8.11reduced: Adding Only 2 Sex*Stressors Interactions to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor ///
> c.women#c.pmstressor40 c.women#c.stressor, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -692.80347
Iteration 1: log likelihood = -692.80347
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(9) = 73.23
Log likelihood = -692.80347 Prob > chi2 = 0.0000
----------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
women | -.3470959 .2189255 -1.59 0.113 -.776182 .0819901
age80 | .0708268 .0307897 2.30 0.021 .0104801 .1311735
|
c.women#c.age80 | -.0915464 .0345356 -2.65 0.008 -.159235 -.0238578
|
pmmood2 | 1.455996 .3633745 4.01 0.000 .7437948 2.168197
wpmood | .141083 .1305914 1.08 0.280 -.1148715 .3970375
pmstressor40 | 2.128526 .6100547 3.49 0.000 .9328409 3.324211
stressor | .215455 .171007 1.26 0.208 -.1197126 .5506227
|
c.women#c.pmstressor40 | -1.45622 .6757515 -2.15 0.031 -2.780669 -.1317719
|
c.women#c.stressor | -.1894119 .205369 -0.92 0.356 -.5919278 .2131039
|
_cons | 2.641646 .3479223 7.59 0.000 1.959731 3.323561
----------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6647392 .1103734 .4800844 .9204177
-----------------------------+------------------------------------------------
var(Residual) | .6102511 .0430032 .5315279 .7006337
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 176.19 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -692.8035 12 1409.607 1441.454
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of 2 Sex Interactions
. test (c.women#c.pmstressor40=0) (c.women#c.stressor=0)
( 1) [symptoms]c.women#c.pmstressor40 = 0
( 2) [symptoms]c.women#c.stressor = 0
chi2( 2) = 7.39
Prob > chi2 = 0.0248
. estimates store FitSex2,
. lrtest FitSex2 FitBPWPStressor,
Likelihood-ratio test LR chi2(2) = 7.20
(Assumption: FitBPWPStres~r nested in FitSex2) Prob > chi2 = 0.0273
. predict PredSex2, xb,
. corr symptoms PredSex2
(obs=509)
| symptoms PredSex2
-------------+------------------
symptoms | 1.0000
PredSex2 | 0.5292 1.0000
.
. display as result "Eq 8.12: Adding 4 Negative Mood*Stressors Interactions to the Model for the Means"
Eq 8.12: Adding 4 Negative Mood*Stressors Interactions to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor ///
> c.women#c.pmstressor40 c.women#c.stressor ///
> c.pmmood2#c.stressor c.pmmood2#c.pmstressor40 c.wpmood#c.stressor c.wpmood#c.pmstressor40, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -691.29896
Iteration 1: log likelihood = -691.29896
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(13) = 77.19
Log likelihood = -691.29896 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
women | -.3511016 .2177536 -1.61 0.107 -.7778908 .0756877
age80 | .0681633 .030686 2.22 0.026 .0080198 .1283068
|
c.women#c.age80 | -.0916529 .0343412 -2.67 0.008 -.1589604 -.0243454
|
pmmood2 | 1.828212 .5103852 3.58 0.000 .8278756 2.828549
wpmood | -.0028165 .271323 -0.01 0.992 -.5345999 .5289669
pmstressor40 | .8713139 1.212914 0.72 0.473 -1.505954 3.248582
stressor | .2225034 .3984303 0.56 0.577 -.5584058 1.003412
|
c.women#c.pmstressor40 | -1.432064 .6733628 -2.13 0.033 -2.751831 -.1122974
|
c.women#c.stressor | -.1711089 .2066281 -0.83 0.408 -.5760925 .2338748
|
c.pmmood2#c.stressor | .0115253 .4388817 0.03 0.979 -.8486669 .8717175
|
c.pmmood2#c.pmstressor40 | -1.469969 1.237363 -1.19 0.235 -3.895156 .9552182
|
c.wpmood#c.stressor | .0590131 .4027296 0.15 0.884 -.7303223 .8483486
|
c.wpmood#c.pmstressor40 | .4427065 .5342181 0.83 0.407 -.6043417 1.489755
|
_cons | 2.983885 .4721862 6.32 0.000 2.058417 3.909353
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6546796 .108856 .4726029 .9069039
-----------------------------+------------------------------------------------
var(Residual) | .6078401 .0428283 .5294364 .6978544
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 174.82 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -691.299 16 1414.598 1457.061
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of 4 Negative Mood*Stressors Interactions
. test (c.pmmood2#c.stressor=0) (c.pmmood2#c.pmstressor40=0) (c.wpmood#c.stressor=0) (c.wpmood#c.pmstressor40=0)
( 1) [symptoms]c.pmmood2#c.stressor = 0
( 2) [symptoms]c.pmmood2#c.pmstressor40 = 0
( 3) [symptoms]c.wpmood#c.stressor = 0
( 4) [symptoms]c.wpmood#c.pmstressor40 = 0
chi2( 4) = 3.02
Prob > chi2 = 0.5542
. * Intercept: Men
. lincom _cons*1 + c.women*0
( 1) [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.983885 .4721862 6.32 0.000 2.058417 3.909353
------------------------------------------------------------------------------
. * Intercept: Women
. lincom _cons*1 + c.women*1
( 1) [symptoms]women + [symptoms]_cons = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.632784 .4509347 5.84 0.000 1.748968 3.516599
------------------------------------------------------------------------------
. * Intercept: Sex Diff
. lincom c.women*1
( 1) [symptoms]women = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.3511016 .2177536 -1.61 0.107 -.7778908 .0756877
------------------------------------------------------------------------------
. * Age Slope: Men
. lincom c.age80*1 + c.women#c.age80*0
( 1) [symptoms]age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0681633 .030686 2.22 0.026 .0080198 .1283068
------------------------------------------------------------------------------
. * Age Slope: Women
. lincom c.age80*1 + c.women#c.age80*1
( 1) [symptoms]age80 + [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0234895 .0166462 -1.41 0.158 -.0561154 .0091363
------------------------------------------------------------------------------
. * Age Slope: Sex Diff
. lincom c.women#c.age80*1
( 1) [symptoms]c.women#c.age80 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0916529 .0343412 -2.67 0.008 -.1589604 -.0243454
------------------------------------------------------------------------------
. * BP Negative Mood
. lincom c.pmmood2*1
( 1) [symptoms]pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.828212 .5103852 3.58 0.000 .8278756 2.828549
------------------------------------------------------------------------------
. * Contextual Negative Mood
. lincom c.wpmood*-1 + c.pmmood2*1
( 1) [symptoms]pmmood2 - [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.831029 .5451401 3.36 0.001 .7625738 2.899484
------------------------------------------------------------------------------
. * WP Negative Mood
. lincom c.wpmood*1
( 1) [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0028165 .271323 -0.01 0.992 -.5345999 .5289669
------------------------------------------------------------------------------
. * BP Stressors: Men
. lincom c.stressor*1 + c.women#c.stressor*0 + c.pmstressor40*1 + c.women#c.pmstressor40*0
( 1) [symptoms]pmstressor40 + [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.093817 1.141565 0.96 0.338 -1.14361 3.331244
------------------------------------------------------------------------------
. * BP Stressors: Women
. lincom c.stressor*1 + c.women#c.stressor*1 + c.pmstressor40*1 + c.women#c.pmstressor40*1
( 1) [symptoms]pmstressor40 + [symptoms]stressor + [symptoms]c.women#c.pmstressor40 + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.5093559 .9994258 -0.51 0.610 -2.468195 1.449483
------------------------------------------------------------------------------
. * BP Stressors: Sex Diff
. lincom c.women#c.stressor*1 + c.women#c.pmstressor40*1
( 1) [symptoms]c.women#c.pmstressor40 + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.603173 .6402568 -2.50 0.012 -2.858053 -.3482929
------------------------------------------------------------------------------
. * Contextual Stressors: Men
. lincom c.pmstressor40*1 + c.women#c.pmstressor40*0
( 1) [symptoms]pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .8713139 1.212914 0.72 0.473 -1.505954 3.248582
------------------------------------------------------------------------------
. * Contextual Stressors: Women
. lincom c.pmstressor40*1 + c.women#c.pmstressor40*1
( 1) [symptoms]pmstressor40 + [symptoms]c.women#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.5607504 1.073882 -0.52 0.602 -2.66552 1.544019
------------------------------------------------------------------------------
. * Contextual Stressors: Sex Diff
. lincom c.women#c.pmstressor40*1
( 1) [symptoms]c.women#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.432064 .6733628 -2.13 0.033 -2.751831 -.1122974
------------------------------------------------------------------------------
. * WP Stressors: Men
. lincom c.stressor*1 + c.women#c.stressor*0
( 1) [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .2225034 .3984303 0.56 0.577 -.5584058 1.003412
------------------------------------------------------------------------------
. * WP Stressors: Women
. lincom c.stressor*1 + c.women#c.stressor*1
( 1) [symptoms]stressor + [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0513945 .3762531 0.14 0.891 -.6860481 .7888371
------------------------------------------------------------------------------
. * WP Stressors: Sex Diff
. lincom c.women#c.stressor*1
( 1) [symptoms]c.women#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.1711089 .2066281 -0.83 0.408 -.5760925 .2338748
------------------------------------------------------------------------------
. * Contextual Stressors per Day
. lincom c.pmstressor40*.2
( 1) .2*[symptoms]pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .1742628 .2425828 0.72 0.473 -.3011908 .6497164
------------------------------------------------------------------------------
. * BP Negative Mood by BP Stressors
. lincom c.pmmood2#c.stressor*1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.stressor + [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.458444 1.147902 -1.27 0.204 -3.70829 .7914028
------------------------------------------------------------------------------
. * BP Negative Mood by Contextual Stressors
. lincom c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.469969 1.237363 -1.19 0.235 -3.895156 .9552182
------------------------------------------------------------------------------
. * BP Negative Mood by WP Stressors
. lincom c.pmmood2#c.stressor*1
( 1) [symptoms]c.pmmood2#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0115253 .4388817 0.03 0.979 -.8486669 .8717175
------------------------------------------------------------------------------
. * Contextual Negative Mood by BP Stressors
. lincom c.wpmood#c.stressor*-1 + c.pmmood2#c.stressor*1 + c.wpmood#c.pmstressor40*-1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.stressor + [symptoms]c.pmmood2#c.pmstressor40 - [symptoms]c.wpmood#c.stressor - [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.960163 1.222791 -1.60 0.109 -4.356789 .4364631
------------------------------------------------------------------------------
. * Contextual Negative Mood by Contextual Stressors
. lincom c.wpmood#c.pmstressor40*-1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 - [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.912675 1.34067 -1.43 0.154 -4.540341 .7149904
------------------------------------------------------------------------------
. * Contextual Negative Mood by WP Stressors
. lincom c.wpmood#c.stressor*-1 + c.pmmood2#c.stressor*1
( 1) [symptoms]c.pmmood2#c.stressor - [symptoms]c.wpmood#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
------------------------------------------------------------------------------
. * WP Negative Mood by BP Stressors
. lincom c.wpmood#c.stressor*1 + c.wpmood#c.pmstressor40*1
( 1) [symptoms]c.wpmood#c.stressor + [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .5017196 .424508 1.18 0.237 -.3303008 1.33374
------------------------------------------------------------------------------
. * WP Negative Mood by Contextual Stressors
. lincom c.wpmood#c.pmstressor40*1
( 1) [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .4427065 .5342181 0.83 0.407 -.6043417 1.489755
------------------------------------------------------------------------------
. * WP Negative Mood by WP Stressors
. lincom c.wpmood#c.stressor*1
( 1) [symptoms]c.wpmood#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0590131 .4027296 0.15 0.884 -.7303223 .8483486
------------------------------------------------------------------------------
. estimates store FitMoodStressor4,
. lrtest FitMoodStressor4 FitSex2,
Likelihood-ratio test LR chi2(4) = 3.01
(Assumption: FitSex2 nested in FitMoodStres~4) Prob > chi2 = 0.5563
. predict PredMoodStressor4, xb,
. corr symptoms PredMoodStressor4
(obs=509)
| symptoms PredMo~4
-------------+------------------
symptoms | 1.0000
PredMoodSt~4 | 0.5344 1.0000
.
. display as result "Ch 8: Checking Inter-Variable Interactions via PMC Negative Mood by PMC Stressors"
Ch 8: Checking Inter-Variable Interactions via PMC Negative Mood by PMC Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.wpstressor ///
> c.women#c.pmstressor40 c.women#c.wpstressor ///
> c.pmmood2#c.wpstressor c.pmmood2#c.pmstressor40 c.wpmood#c.wpstressor c.wpmood#c.pmstressor40, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -691.29896
Iteration 1: log likelihood = -691.29896
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(13) = 77.19
Log likelihood = -691.29896 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
women | -.4195451 .2010571 -2.09 0.037 -.8136098 -.0254804
age80 | .0681633 .030686 2.22 0.026 .0080198 .1283068
|
c.women#c.age80 | -.0916529 .0343412 -2.67 0.008 -.1589604 -.0243454
|
pmmood2 | 1.832822 .4723335 3.88 0.000 .9070656 2.758579
wpmood | .0207887 .1728954 0.12 0.904 -.31808 .3596574
pmstressor40 | 1.093817 1.141565 0.96 0.338 -1.14361 3.331244
wpstressor | .2225034 .3984303 0.56 0.577 -.5584057 1.003412
|
c.women#c.pmstressor40 | -1.603173 .6402568 -2.50 0.012 -2.858053 -.3482929
|
c.women#c.wpstressor | -.1711089 .2066281 -0.83 0.408 -.5760925 .2338748
|
c.pmmood2#c.wpstressor | .0115253 .4388816 0.03 0.979 -.8486669 .8717175
|
c.pmmood2#c.pmstressor40 | -1.458444 1.147902 -1.27 0.204 -3.70829 .7914028
|
c.wpmood#c.wpstressor | .0590132 .4027296 0.15 0.884 -.7303223 .8483487
|
c.wpmood#c.pmstressor40 | .5017196 .424508 1.18 0.237 -.3303008 1.33374
|
_cons | 3.072886 .4388621 7.00 0.000 2.212733 3.93304
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6546796 .108856 .4726029 .9069039
-----------------------------+------------------------------------------------
var(Residual) | .6078401 .0428283 .5294364 .6978544
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 174.82 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -691.299 16 1414.598 1457.061
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * BP Negative Mood by BP Stressors
. lincom c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.458444 1.147902 -1.27 0.204 -3.70829 .7914028
------------------------------------------------------------------------------
. * BP Negative Mood by Contextual Stressors
. lincom c.pmmood2#c.wpstressor*-1 + c.pmmood2#c.pmstressor40*1
( 1) - [symptoms]c.pmmood2#c.wpstressor + [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.469969 1.237363 -1.19 0.235 -3.895156 .9552182
------------------------------------------------------------------------------
. * BP Negative Mood by WP Stressors
. lincom c.pmmood2#c.wpstressor*1
( 1) [symptoms]c.pmmood2#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0115253 .4388816 0.03 0.979 -.8486669 .8717175
------------------------------------------------------------------------------
. * Contextual Negative Mood by BP Stressors
. lincom c.wpmood#c.pmstressor40*-1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 - [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.960163 1.222791 -1.60 0.109 -4.356789 .4364631
------------------------------------------------------------------------------
. * Contextual Negative Mood by Contextual Stressors
. lincom c.wpmood#c.pmstressor40*-1 + c.pmmood2#c.pmstressor40*1 + c.pmmood2#c.wpstressor*-1 + c.wpmood#c.wpstressor*1
( 1) - [symptoms]c.pmmood2#c.wpstressor + [symptoms]c.pmmood2#c.pmstressor40 + [symptoms]c.wpmood#c.wpstressor - [symptoms]c.wpmood#c.pmstressor40 =
0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.912675 1.34067 -1.43 0.154 -4.540341 .7149904
------------------------------------------------------------------------------
. * Contextual Negative Mood by WP Stressors
. lincom c.wpmood#c.wpstressor*-1 + c.pmmood2#c.wpstressor*1
( 1) [symptoms]c.pmmood2#c.wpstressor - [symptoms]c.wpmood#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
------------------------------------------------------------------------------
. * WP Negative Mood by BP Stressors
. lincom c.wpmood#c.pmstressor40*1
( 1) [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .5017196 .424508 1.18 0.237 -.3303008 1.33374
------------------------------------------------------------------------------
. * WP Negative Mood by Contextual Stressors
. lincom c.wpmood#c.wpstressor*-1 + c.wpmood#c.pmstressor40*1
( 1) - [symptoms]c.wpmood#c.wpstressor + [symptoms]c.wpmood#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .4427064 .5342181 0.83 0.407 -.6043417 1.489755
------------------------------------------------------------------------------
. * WP Negative Mood by WP Stressors
. lincom c.wpmood#c.wpstressor*1
( 1) [symptoms]c.wpmood#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0590132 .4027296 0.15 0.884 -.7303223 .8483487
------------------------------------------------------------------------------
.
. display as result "Ch 8: Checking Inter-Variable Interactions via GMC Negative Mood by GMC Stressors"
Ch 8: Checking Inter-Variable Interactions via GMC Negative Mood by GMC Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.mood2 c.pmstressor40 c.stressor ///
> c.women#c.pmstressor40 c.women#c.stressor ///
> c.pmmood2#c.stressor c.pmmood2#c.pmstressor40 c.mood2#c.stressor c.mood2#c.pmstressor40, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -691.29896
Iteration 1: log likelihood = -691.29896
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(13) = 77.19
Log likelihood = -691.29896 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
women | -.3511016 .2177536 -1.61 0.107 -.7778908 .0756877
age80 | .0681633 .030686 2.22 0.026 .0080198 .1283068
|
c.women#c.age80 | -.0916529 .0343412 -2.67 0.008 -.1589604 -.0243454
|
pmmood2 | 1.831029 .5451401 3.36 0.001 .7625738 2.899484
mood2 | -.0028166 .271323 -0.01 0.992 -.5345999 .5289668
pmstressor40 | .8713139 1.212914 0.72 0.473 -1.505954 3.248582
stressor | .2225034 .3984303 0.56 0.577 -.5584058 1.003412
|
c.women#c.pmstressor40 | -1.432064 .6733628 -2.13 0.033 -2.751831 -.1122974
|
c.women#c.stressor | -.1711089 .2066281 -0.83 0.408 -.5760925 .2338748
|
c.pmmood2#c.stressor | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
|
c.pmmood2#c.pmstressor40 | -1.912675 1.34067 -1.43 0.154 -4.540341 .7149904
|
c.mood2#c.stressor | .0590131 .4027296 0.15 0.884 -.7303223 .8483486
|
c.mood2#c.pmstressor40 | .4427065 .5342181 0.83 0.407 -.6043417 1.489755
|
_cons | 2.983885 .4721862 6.32 0.000 2.058417 3.909353
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6546796 .108856 .4726029 .9069039
-----------------------------+------------------------------------------------
var(Residual) | .6078401 .0428283 .5294364 .6978544
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 174.82 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -691.299 16 1414.598 1457.061
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * BP Negative Mood by BP Stressors
. lincom c.mood2#c.pmstressor40*1 + c.pmmood2#c.pmstressor40*1 + c.stressor#c.pmmood2*1 + c.mood2#c.stressor*1
( 1) [symptoms]c.pmmood2#c.stressor + [symptoms]c.pmmood2#c.pmstressor40 + [symptoms]c.mood2#c.stressor + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.458444 1.147902 -1.27 0.204 -3.70829 .7914028
------------------------------------------------------------------------------
. * BP Negative Mood by Contextual Stressors
. lincom c.mood2#c.pmstressor40*1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.469969 1.237363 -1.19 0.235 -3.895156 .9552182
------------------------------------------------------------------------------
. * BP Negative Mood by WP Stressors
. lincom c.pmmood2#c.stressor*1 + c.mood2#c.stressor*1
( 1) [symptoms]c.pmmood2#c.stressor + [symptoms]c.mood2#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0115253 .4388817 0.03 0.979 -.8486669 .8717175
------------------------------------------------------------------------------
. * Contextual Negative Mood by BP Stressors
. lincom c.pmmood2#c.stressor*1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.stressor + [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.960163 1.222791 -1.60 0.109 -4.356789 .4364631
------------------------------------------------------------------------------
. * Contextual Negative Mood by Contextual Stressors
. lincom c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.912675 1.34067 -1.43 0.154 -4.540341 .7149904
------------------------------------------------------------------------------
. * Contextual Negative Mood by WP Stressors
. lincom c.pmmood2#c.stressor*1
( 1) [symptoms]c.pmmood2#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
------------------------------------------------------------------------------
. * WP Negative Mood by BP Stressors
. lincom c.mood2#c.pmstressor40*1 + c.mood2#c.stressor*1
( 1) [symptoms]c.mood2#c.stressor + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .5017196 .424508 1.18 0.237 -.3303008 1.33374
------------------------------------------------------------------------------
. * WP Negative Mood by Contextual Stressors
. lincom c.mood2#c.pmstressor40*1
( 1) [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .4427065 .5342181 0.83 0.407 -.6043417 1.489755
------------------------------------------------------------------------------
. * WP Negative Mood by WP Stressors
. lincom c.mood2#c.stressor*1
( 1) [symptoms]c.mood2#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0590131 .4027296 0.15 0.884 -.7303223 .8483486
------------------------------------------------------------------------------
.
. display as result "Ch 8: Checking Inter-Variable Interactions via GMC Negative Mood by PMC Stressors"
Ch 8: Checking Inter-Variable Interactions via GMC Negative Mood by PMC Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.mood2 c.pmstressor40 c.wpstressor ///
> c.women#c.pmstressor40 c.women#c.wpstressor ///
> c.pmmood2#c.wpstressor c.pmmood2#c.pmstressor40 c.mood2#c.wpstressor c.mood2#c.pmstressor40, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -691.29896
Iteration 1: log likelihood = -691.29896
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(13) = 77.19
Log likelihood = -691.29896 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
women | -.4195451 .2010571 -2.09 0.037 -.8136098 -.0254804
age80 | .0681633 .030686 2.22 0.026 .0080198 .1283068
|
c.women#c.age80 | -.0916529 .0343412 -2.67 0.008 -.1589604 -.0243454
|
pmmood2 | 1.812034 .497873 3.64 0.000 .8362204 2.787847
mood2 | .0207887 .1728954 0.12 0.904 -.31808 .3596574
pmstressor40 | 1.093817 1.141565 0.96 0.338 -1.14361 3.331244
wpstressor | .2225034 .3984303 0.56 0.577 -.5584057 1.003413
|
c.women#c.pmstressor40 | -1.603173 .6402568 -2.50 0.012 -2.858053 -.3482929
|
c.women#c.wpstressor | -.1711089 .2066281 -0.83 0.408 -.5760925 .2338748
|
c.pmmood2#c.wpstressor | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
|
c.pmmood2#c.pmstressor40 | -1.960163 1.222791 -1.60 0.109 -4.356789 .4364631
|
c.mood2#c.wpstressor | .0590132 .4027296 0.15 0.884 -.7303223 .8483487
|
c.mood2#c.pmstressor40 | .5017196 .424508 1.18 0.237 -.3303008 1.33374
|
_cons | 3.072886 .4388621 7.00 0.000 2.212733 3.93304
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6546796 .108856 .4726029 .9069039
-----------------------------+------------------------------------------------
var(Residual) | .6078401 .0428283 .5294364 .6978544
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 174.82 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -691.299 16 1414.598 1457.061
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * BP Negative Mood by BP Stressors
. lincom c.mood2#c.pmstressor40*1 + c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.458444 1.147902 -1.27 0.204 -3.70829 .7914028
------------------------------------------------------------------------------
. * BP Negative Mood by Contextual Stressors
. lincom c.pmmood2#c.wpstressor*-1 + c.mood2#c.wpstressor*-1 + c.mood2#c.pmstressor40*1 + c.pmmood2#c.pmstressor40*1
( 1) - [symptoms]c.pmmood2#c.wpstressor + [symptoms]c.pmmood2#c.pmstressor40 - [symptoms]c.mood2#c.wpstressor + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.469969 1.237363 -1.19 0.235 -3.895156 .9552182
------------------------------------------------------------------------------
. * BP Negative Mood by WP Stressors
. lincom c.pmmood2#c.wpstressor*1 + c.mood2#c.wpstressor*1
( 1) [symptoms]c.pmmood2#c.wpstressor + [symptoms]c.mood2#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0115253 .4388816 0.03 0.979 -.8486669 .8717175
------------------------------------------------------------------------------
. * Contextual Negative Mood by BP Stressors
. lincom c.pmmood2#c.pmstressor40*1
( 1) [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.960163 1.222791 -1.60 0.109 -4.356789 .4364631
------------------------------------------------------------------------------
. * Contextual Negative Mood by Contextual Stressors
. lincom c.pmmood2#c.wpstressor*-1 + c.pmmood2#c.pmstressor40*1
( 1) - [symptoms]c.pmmood2#c.wpstressor + [symptoms]c.pmmood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.912675 1.34067 -1.43 0.154 -4.540341 .7149904
------------------------------------------------------------------------------
. * Contextual Negative Mood by WP Stressors
. lincom c.pmmood2#c.wpstressor*1
( 1) [symptoms]c.pmmood2#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.0474879 .540999 -0.09 0.930 -1.107826 1.012851
------------------------------------------------------------------------------
. * WP Negative Mood by BP Stressors
. lincom c.mood2#c.pmstressor40*1
( 1) [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .5017196 .424508 1.18 0.237 -.3303008 1.33374
------------------------------------------------------------------------------
. * WP Negative Mood by Contextual Stressors
. lincom c.mood2#c.wpstressor*-1 + c.mood2#c.pmstressor40*1
( 1) - [symptoms]c.mood2#c.wpstressor + [symptoms]c.mood2#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .4427065 .5342181 0.83 0.407 -.6043417 1.489755
------------------------------------------------------------------------------
. * WP Negative Mood by WP Stressors
. lincom c.mood2#c.wpstressor*1
( 1) [symptoms]c.mood2#c.wpstressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0590132 .4027296 0.15 0.884 -.7303223 .8483487
------------------------------------------------------------------------------
.
. display as result "Eq 8.13: Adding 5 Intra-Variable Interactions to the Model for the Means"
Eq 8.13: Adding 5 Intra-Variable Interactions to the Model for the Means
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor ///
> c.women#c.pmstressor40 c.women#c.stressor ///
> c.pmmood2#c.pmmood2 c.wpmood#c.wpmood c.pmmood2#c.wpmood c.pmstressor40#c.pmstressor40 c.pmstressor40#c.stressor, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -689.16765
Iteration 1: log likelihood = -689.16765
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(14) = 84.61
Log likelihood = -689.16765 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
women | -.3248923 .2161278 -1.50 0.133 -.7484949 .0987104
age80 | .0709475 .0300748 2.36 0.018 .0120019 .1298931
|
c.women#c.age80 | -.0896596 .0336311 -2.67 0.008 -.1555754 -.0237438
|
pmmood2 | -.2980258 .8016478 -0.37 0.710 -1.869227 1.273175
wpmood | .3733039 .2595401 1.44 0.150 -.1353855 .8819932
pmstressor40 | 1.889804 .6325322 2.99 0.003 .6500637 3.129544
stressor | .1979126 .1752048 1.13 0.259 -.1454825 .5413076
|
c.women#c.pmstressor40 | -1.302228 .6618457 -1.97 0.049 -2.599421 -.0050339
|
c.women#c.stressor | -.1737726 .2053261 -0.85 0.397 -.5762044 .2286592
|
c.pmmood2#c.pmmood2 | -1.836474 .7166967 -2.56 0.010 -3.241174 -.4317744
|
c.wpmood#c.wpmood | -.265682 .3132974 -0.85 0.396 -.8797336 .3483697
|
c.pmmood2#c.wpmood | .2766872 .3967688 0.70 0.486 -.5009653 1.05434
|
c.pmstressor40#c.pmstressor40 | -.3855933 .9842148 -0.39 0.695 -2.314619 1.543432
|
c.pmstressor40#c.stressor | .1756553 .4860966 0.36 0.718 -.7770765 1.128387
|
_cons | 2.567988 .373348 6.88 0.000 1.83624 3.299737
-----------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6218688 .1042789 .4476761 .8638406
-----------------------------+------------------------------------------------
var(Residual) | .6081879 .042848 .5297475 .698243
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 166.48 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -689.1676 17 1412.335 1457.453
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
. * Multivariate Test of 5 Intra-Variable Interactions
. test (c.pmmood2#c.pmmood2=0) (c.wpmood#c.wpmood=0) (c.pmmood2#c.wpmood=0) (c.pmstressor40#c.pmstressor40=0) (c.pmstressor40#c.stressor=0)
( 1) [symptoms]c.pmmood2#c.pmmood2 = 0
( 2) [symptoms]c.wpmood#c.wpmood = 0
( 3) [symptoms]c.pmmood2#c.wpmood = 0
( 4) [symptoms]c.pmstressor40#c.pmstressor40 = 0
( 5) [symptoms]c.pmstressor40#c.stressor = 0
chi2( 5) = 7.44
Prob > chi2 = 0.1898
. * BP Negative Mood: Linear
. lincom c.pmmood2*1
( 1) [symptoms]pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.2980258 .8016478 -0.37 0.710 -1.869227 1.273175
------------------------------------------------------------------------------
. * BP Negative Mood: Quadratic
. lincom c.pmmood2#c.pmmood2*1
( 1) [symptoms]c.pmmood2#c.pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -1.836474 .7166967 -2.56 0.010 -3.241174 -.4317744
------------------------------------------------------------------------------
. * Contextual Negative Mood: Linear
. lincom c.wpmood*-1 + c.pmmood2*1
( 1) [symptoms]pmmood2 - [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.6713297 .8487013 -0.79 0.429 -2.334754 .9920944
------------------------------------------------------------------------------
. * Contextual Negative Mood: Quadratic
. lincom c.pmmood2#c.wpmood*-1 + c.pmmood2#c.pmmood2*1 + c.wpmood#c.wpmood*1
( 1) [symptoms]c.pmmood2#c.pmmood2 + [symptoms]c.wpmood#c.wpmood - [symptoms]c.pmmood2#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -2.378843 .930602 -2.56 0.011 -4.20279 -.554897
------------------------------------------------------------------------------
. * WP Negative Mood: Linear
. lincom c.wpmood*1
( 1) [symptoms]wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .3733039 .2595401 1.44 0.150 -.1353855 .8819932
------------------------------------------------------------------------------
. * WP Negative Mood: Quadratic
. lincom c.wpmood#c.wpmood*1
( 1) [symptoms]c.wpmood#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.265682 .3132974 -0.85 0.396 -.8797336 .3483697
------------------------------------------------------------------------------
. * BP by WP Negative Mood
. lincom c.pmmood2#c.wpmood*1
( 1) [symptoms]c.pmmood2#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .2766872 .3967688 0.70 0.486 -.5009653 1.05434
------------------------------------------------------------------------------
. * Contextual by WP Negative Mood
. lincom c.pmmood2#c.wpmood*1 + c.wpmood#c.wpmood*-2
( 1) - 2*[symptoms]c.wpmood#c.wpmood + [symptoms]c.pmmood2#c.wpmood = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .8080511 .7629095 1.06 0.290 -.687224 2.303326
------------------------------------------------------------------------------
. * BP Stressor: Linear
. lincom c.pmstressor40*1 + c.stressor*1
( 1) [symptoms]pmstressor40 + [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 2.087717 .6195876 3.37 0.001 .8733472 3.302086
------------------------------------------------------------------------------
. * BP Stressor: Quadratic
. lincom c.pmstressor40#c.pmstressor40*1 + c.pmstressor40#c.stressor*1
( 1) [symptoms]c.pmstressor40#c.pmstressor40 + [symptoms]c.pmstressor40#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.209938 .8568925 -0.24 0.806 -1.889416 1.46954
------------------------------------------------------------------------------
. * Contextual Stressors: Linear
. lincom c.pmstressor40*1
( 1) [symptoms]pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 1.889804 .6325322 2.99 0.003 .6500637 3.129544
------------------------------------------------------------------------------
. * Contextual Stressors: Quadratic
. lincom c.pmstressor40#c.pmstressor40*1
( 1) [symptoms]c.pmstressor40#c.pmstressor40 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.3855933 .9842148 -0.39 0.695 -2.314619 1.543432
------------------------------------------------------------------------------
. * WP Stressors: Linear
. lincom c.stressor*1
( 1) [symptoms]stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .1979126 .1752048 1.13 0.259 -.1454825 .5413076
------------------------------------------------------------------------------
. * BP/Contextual by WP Stressors
. lincom c.pmstressor40#c.stressor*1
( 1) [symptoms]c.pmstressor40#c.stressor = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .1756553 .4860966 0.36 0.718 -.7770765 1.128387
------------------------------------------------------------------------------
. * BP Negative Mood: Linear at 1
. lincom c.pmmood2*1 + c.pmmood2#c.pmmood2*-2
( 1) [symptoms]pmmood2 - 2*[symptoms]c.pmmood2#c.pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3.374923 .8137582 4.15 0.000 1.779986 4.969859
------------------------------------------------------------------------------
. * BP Negative Mood: Linear at 3
. lincom c.pmmood2*1 + c.pmmood2#c.pmmood2*2
( 1) [symptoms]pmmood2 + 2*[symptoms]c.pmmood2#c.pmmood2 = 0
------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -3.970974 2.175387 -1.83 0.068 -8.234655 .2927065
------------------------------------------------------------------------------
. estimates store FitIntra5,
. lrtest FitIntra5 FitSex2,
Likelihood-ratio test LR chi2(5) = 7.27
(Assumption: FitSex2 nested in FitIntra5) Prob > chi2 = 0.2012
. predict PredIntra5, xb,
. corr symptoms PredIntra5
(obs=509)
| symptoms PredIn~5
-------------+------------------
symptoms | 1.0000
PredIntra5 | 0.5511 1.0000
.
. display as result "Eq 8.13reduced: Baseline Homogeneous Variance Model with Significant Fixed Effects Only"
Eq 8.13reduced: Baseline Homogeneous Variance Model with Significant Fixed Effects Only
. display as result "Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors"
Using Person-Mean-Centering for Negative Mood, Grand-Mean-Centering for Stressors
. mixed symptoms c.women c.age80 c.women#c.age80 ///
> c.pmmood2 c.wpmood c.pmstressor40 c.stressor ///
> c.women#c.pmstressor40 c.women#c.stressor ///
> c.pmmood2#c.pmmood2 c.pmmood2#c.wpmood, ///
> || personid: , variance mle covariance(unstructured),
Note: single-variable random-effects specification in personid equation; covariance structure set to identity
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -689.65489
Iteration 1: log likelihood = -689.65489
Computing standard errors:
Mixed-effects ML regression Number of obs = 509
Group variable: personid Number of groups = 105
Obs per group: min = 2
avg = 4.8
max = 5
Wald chi2(11) = 83.47
Log likelihood = -689.65489 Prob > chi2 = 0.0000
----------------------------------------------------------------------------------------
symptoms | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
women | -.3299839 .2141103 -1.54 0.123 -.7496324 .0896646
age80 | .0722215 .0299903 2.41 0.016 .0134416 .1310014
|
c.women#c.age80 | -.0902982 .0336368 -2.68 0.007 -.156225 -.0243713
|
pmmood2 | -.3358238 .7998091 -0.42 0.675 -1.903421 1.231773
wpmood | .2631281 .2212778 1.19 0.234 -.1705684 .6968246
pmstressor40 | 1.925533 .6005177 3.21 0.001 .7485403 3.102526
stressor | .2204782 .171048 1.29 0.197 -.1147698 .5557261
|
c.women#c.pmstressor40 | -1.321445 .6617654 -2.00 0.046 -2.618482 -.0244091
|
c.women#c.stressor | -.1823979 .205298 -0.89 0.374 -.5847745 .2199787
|
c.pmmood2#c.pmmood2 | -1.775342 .7131734 -2.49 0.013 -3.173136 -.3775479
|
c.pmmood2#c.wpmood | .258876 .3962417 0.65 0.514 -.5177434 1.035495
|
_cons | 2.450104 .3473127 7.05 0.000 1.769384 3.130824
----------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
personid: Identity |
var(_cons) | .6230866 .1044651 .4485782 .8654834
-----------------------------+------------------------------------------------
var(Residual) | .6093481 .0429281 .530761 .6995712
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 166.66 Prob >= chibar2 = 0.0000
. estat ic, n(105),
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 105 . -689.6549 14 1407.31 1444.465
-----------------------------------------------------------------------------
Note: N=105 used in calculating BIC
.
. ****** END CHAPTER 8 MODELS ******
.
. * Close log
. log close STATA_Chapter8
name: STATA_Chapter8
log: C:\Dropbox\PilesOfVariance\Chapter8\STATA\STATA_Chapter8_Output.smcl
log type: smcl
closed on: 21 Oct 2014, 17:37:39
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