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Applied Longitudinal Data Analysis Chapter 3: Modeling change and event occurrence | SPSS Textbook Examples

The entire program is at aldaspssch3.sps

Table 3.1, page 48

compute filter = 0.
if any(id,68,70,71,72,902,904,906,908) filter = 1.
filter by filter.
list id age cog program.
execute.
use all.
      ID      AGE      COG  PROGRAM

   68.00     1.00   103.00     1.00
   68.00     1.50   119.00     1.00
   68.00     2.00    96.00     1.00
   70.00     1.00   106.00     1.00
   70.00     1.50   107.00     1.00
   70.00     2.00    96.00     1.00
   71.00     1.00   112.00     1.00
   71.00     1.50    86.00     1.00
   71.00     2.00    73.00     1.00
   72.00     1.00   100.00     1.00
   72.00     1.50    93.00     1.00
   72.00     2.00    87.00     1.00
  902.00     1.00   119.00      .00
  902.00     1.50    93.00      .00
  902.00     2.00    99.00      .00
  904.00     1.00   112.00      .00
  904.00     1.50    98.00      .00
  904.00     2.00    79.00      .00
  906.00     1.00    89.00      .00
  906.00     1.50    66.00      .00
  906.00     2.00    81.00      .00
  908.00     1.00   117.00      .00
  908.00     1.50    90.00      .00
  908.00     2.00    76.00      .00


Number of cases read:  24    Number of cases listed:  24

Figure 3.1, page 50

compute filter = 0.
if any(id,68,70,71,72,902,904,906,908) filter = 1.
filter by filter.

formats age (f4.1) cog (f4.0) id (f2.0).

GGRAPH
  /GRAPHDATASET NAME="iGraphDataset" VARIABLES= cog age id 
  /GRAPHSPEC SOURCE=INLINE 
INLINETEMPLATE=["<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "iGraphDataset" ) )
DATA: cog=col( source(s), name( "cog" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: text.title( label( "Figure 3.1" ) )
GUIDE: axis( dim( 1 ), label( "age" ), delta(.5) )
GUIDE: axis( dim( 2 ), label( "cog" ), delta(25) )
GUIDE: axis( dim( 3 ), label( "id" ), opposite() )
SCALE: linear( dim( 1 ), min(1), max(2) )
SCALE: linear( dim( 2 ), min(50), max(150) )
ELEMENT: point( position( ( age * cog * id ) ) )
ELEMENT: line( position(smooth.linear( age * cog * id) ))
END GPL.

use all.

Image

Figure 3.3, page 57

NOTE:  We did not reproduce the stem-and-leaf plots at the bottom
of this figure.

formats age (f4.1) cog (f4.0).
GGRAPH
  /GRAPHDATASET NAME="iGraphDataset" VARIABLES= cog age id 
  /GRAPHSPEC SOURCE=INLINE .
BEGIN GPL
SOURCE: s=userSource( id( "iGraphDataset" ) )
DATA: cog=col( source(s), name( "cog" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: axis( dim( 1 ), label( "age" ), delta(.5) )
GUIDE: axis( dim( 2 ), label( "cog" ), delta(25) )
GUIDE: legend(aesthetic(aesthetic.shape), null())
SCALE: linear( dim( 1 ), min(1), max(2) )
SCALE: linear( dim( 2 ), min(50), max(150) )
ELEMENT: line( position(smooth.linear( age * cog ) ), shape(id))
END GPL.

Image

Figure 3.4, page 59

use all.
value labels program 0 "program = 0" 1 "program = 1".

GGRAPH
  /GRAPHDATASET NAME="iGraphDataset" VARIABLES= cog age id program 
  /GRAPHSPEC SOURCE=INLINE .
BEGIN GPL
SOURCE: s=userSource( id( "iGraphDataset" ) )
DATA: cog=col( source(s), name( "cog" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
DATA: program=col( source(s), name( "program" ), unit.category() )
GUIDE: text.title( label( "Figure 3.4" ) )
GUIDE: axis( dim( 1 ), label( "age" ), delta(.5) )
GUIDE: axis( dim( 2 ), label( "cog" ), delta(25) )
GUIDE: axis( dim( 3 ), label( " " ), opposite() )
GUIDE: legend(aesthetic(aesthetic.shape), null())
SCALE: linear( dim( 1 ), min( 1 ), max( 2 ) )
SCALE: linear( dim( 2 ), min( 50 ), max( 150 ) )
ELEMENT: line( position(smooth.linear( age * cog * program) ), shape(id))
ELEMENT: line( position(summary.mean( age * cog * program)), color(color.red) )
END GPL.

Image

Table 3.3, page 69

We first need to create a centered age variable, which we called
time1.  We also need to include the /criteria = mxstep(50)
subcommand so that the model converges.

compute time1 = age - 1.
execute.

mixed cog with time1 program 
 /print = solution
 /method = ml
 /criteria = mxstep(50) 
 /fixed = time1 program time1*program
 /random intercept time1 | subject(id) covtype(un).

Image

Image

Figure 3.5, page 71

To create the graph, you need to get the predicted values from the
regression.  First, we create an interaction term, timeprog, using
the compute command.  Next, we run the regression using time,
program and timeprog as predictors and saving the predicted
values with the /save subcommand (pred is the SPSS keyword for
saving the predicted values).  Finally, we use pred, the name of
the new variable given in the parentheses, in the ggraph
command.

compute timeprog = time*program.
regression
 /dependent cog
 /method = enter time program timeprog
 /save pred (pred).

value labels program 0 "non-participants" 1 "participants".

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=age MEAN(pred)[name="MEAN_pred"] program  
  /GRAPHSPEC SOURCE=INLINE. 
BEGIN GPL 
SOURCE: s=userSource(id("graphdataset")) 
DATA: age=col(source(s), name("age")) 
DATA: MEAN_pred=col(source(s), name("MEAN_pred")) 
DATA: program=col(source(s), name("program"), unit.category()) 
GUIDE: axis(dim(1), label("age"), delta(.5)) 
GUIDE: axis(dim(2), label("Mean Fixed Predicted Values"), delta(25)) 
GUIDE: legend(aesthetic(aesthetic.color.interior), label("program")) 
SCALE: linear(dim(1), min(1), max(2)) 
SCALE: linear(dim(2), min(50), max(150)) 
ELEMENT: line(position(age*MEAN_pred), shape(program))
END GPL.

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