Mixed Effects Model for log(FEV1) with Random Intercept and Slope for Log Height

Six Cities Study

 

The Mixed Procedure

 

Model Information

Data Set

WORK.FEV

Dependent Variable

logfev1

Covariance Structure

Unstructured

Subject Effect

id

Estimation Method

REML

Residual Variance Method

Profile

Fixed Effects SE Method

Model-Based

Degrees of Freedom Method

Containment

 

Class Level Information

Class

Levels

Values

id

299

not printed

 

Dimensions

Covariance Parameters

4

Columns in X

5

Columns in Z Per Subject

2

Subjects

299

Max Obs Per Subject

12

Observations Used

1993

Observations Not Used

0

Total Observations

1993

 

Iteration History

Iteration

Evaluations

-2 Res Log Like

Criterion

0

1

-2961.45030802

 

1

3

-4589.31545000

0.00003664

2

1

-4589.47260066

0.00000016

3

1

-4589.47325326

0.00000000

 

Convergence criteria met.

 

Estimated G Matrix

Row

Effect

id

Col1

Col2

1

Intercept

1

0.01330

-0.01855

2

loght

1

-0.01855

0.06849

 

Estimated G Correlation Matrix

Row

Effect

id

Col1

Col2

1

Intercept

1

1.0000

-0.6146

2

loght

1

-0.6146

1.0000

 

 

Estimated V Correlation Matrix for id 35

Row

Col1

Col2

Col3

Col4

Col5

Col6

Col7

Col8

Col9

Col10

Col11

Col12

1

1.0000

0.7223

0.7050

0.6706

0.6336

0.5776

0.5187

0.4968

0.4895

0.4895

0.4895

0.4823

2

0.7223

1.0000

0.7088

0.6842

0.6551

0.6085

0.5575

0.5382

0.5317

0.5317

0.5317

0.5253

3

0.7050

0.7088

1.0000

0.6957

0.6767

0.6423

0.6017

0.5857

0.5804

0.5804

0.5804

0.5750

4

0.6706

0.6842

0.6957

1.0000

0.6962

0.6790

0.6533

0.6423

0.6385

0.6385

0.6385

0.6347

5

0.6336

0.6551

0.6767

0.6962

1.0000

0.7004

0.6872

0.6804

0.6779

0.6779

0.6779

0.6754

6

0.5776

0.6085

0.6423

0.6790

0.7004

1.0000

0.7179

0.7164

0.7157

0.7157

0.7157

0.7148

7

0.5187

0.5575

0.6017

0.6533

0.6872

0.7179

1.0000

0.7378

0.7386

0.7386

0.7386

0.7392

8

0.4968

0.5382

0.5857

0.6423

0.6804

0.7164

0.7378

1.0000

0.7440

0.7440

0.7440

0.7451

9

0.4895

0.5317

0.5804

0.6385

0.6779

0.7157

0.7386

0.7440

1.0000

0.7455

0.7455

0.7468

10

0.4895

0.5317

0.5804

0.6385

0.6779

0.7157

0.7386

0.7440

0.7455

1.0000

0.7455

0.7468

11

0.4895

0.5317

0.5804

0.6385

0.6779

0.7157

0.7386

0.7440

0.7455

0.7455

1.0000

0.7468

12

0.4823

0.5253

0.5750

0.6347

0.6754

0.7148

0.7392

0.7451

0.7468

0.7468

0.7468

1.0000

 

Covariance Parameter Estimates

Cov Parm

Subject

Estimate

Standard Error

Z Value

Pr Z

UN(1,1)

id

0.01330

0.002132

6.24

<.0001

UN(2,1)

id

-0.01855

0.004666

-3.98

<.0001

UN(2,2)

id

0.06849

0.01267

5.40

<.0001

Residual

 

0.003533

0.000131

27.03

<.0001

 

Fit Statistics

-2 Res Log Likelihood

-4589.5

AIC (smaller is better)

-4581.5

AICC (smaller is better)

-4581.5

BIC (smaller is better)

-4566.7

 

 

Solution for Fixed Effects

Effect

Estimate

Standard Error

DF

t Value

Pr > |t|

Intercept

-0.2846

0.03901

297

-7.30

<.0001

age

0.02327

0.001247

1440

18.65

<.0001

loght

2.2523

0.04613

251

48.82

<.0001

baseage

-0.01630

0.007439

1440

-2.19

0.0286

logbht

0.1808

0.1455

1440

1.24

0.2142

 

Type 3 Tests of Fixed Effects

Effect

Num DF

Den DF

Chi-Square

F Value

Pr > ChiSq

Pr > F

age

1

1440

348.01

348.01

<.0001

<.0001

loght

1

251

2383.77

2383.77

<.0001

<.0001

baseage

1

1440

4.80

4.80

0.0285

0.0286

logbht

1

1440

1.54

1.54

0.2140

0.2142