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Re: Follow-up to piecewise regression question

Posted by parisec on May 03, 2012; 4:50pm
URL: http://spssx-discussion.165.s1.nabble.com/Follow-up-to-piecewise-regression-question-tp5668949p5683759.html

Lightbulb moment here...
 
based on my results, it looks like the issue is that the
 
| NOINT SSTYPE(3)

is applying the "no intercept" only to the first variable listed.
 
Without NOINT, all variables get refcats. But trying to do 2 separate /FIXED lines is not possible.
  From: Parise, Carol A.
Sent: Thursday, May 03, 2012 9:23 AM
To: 'R B'; [hidden email]
Subject: RE: Follow-up to piecewise regression question

This is weird. I included 2 additonal fixed factors in the model but only posted the output for age: num_sex and racenum10.

When i run your code with the additional variables after the age variables on the /FIXED line:

MIXED  timehrs BY num_sex racenum10 age3 WITH newage
  /FIXED=age3 age3*newage num_sex racenum10 | NOINT SSTYPE(3)

...I get what you posted - no reference category where age has 3 parameters and the actual age intercepts make logical sense for the data. It also correctly leaves sex=1 as the refcat

Estimates of Fixed Effectsa

Parameter

Estimate

Std. Error

df

t

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

[age3=1.00]

24.620714

.730377

13,805

33.710

.000

23.189075

26.052353

[age3=2.00]

18.324952

.720691

13,805

25.427

.000

16.912300

19.737603

[age3=3.00]

24.449104

1.072402

13,805

22.798

.000

22.347050

26.551158

[age3=1.00] * NewAge

.036716

.022293

13,805

1.647

.100

-.006981

.080413

[age3=2.00] * NewAge

.203924

.016284

13,805

12.523

.000

.172004

.235843

[age3=3.00] * NewAge

.082119

.019039

13,805

4.313

.000

.044800

.119438

[num_sex=0]

1.107044

.123436

13,805

8.969

.000

.865092

1.348996

[num_sex=1]

0

0

.

.

.

.

.

a. Dependent Variable: timehrs.

Model Dimensiona

 

Number of Levels

Number of Parameters

Fixed Effects

age3

3

3

age3 * NewAge

3

3

num_sex

2

1

a. Dependent Variable: timehrs.

 
When i run it with the 2 additional variables *first* on the /FIXED line:
MIXED
  timehrs by num_sex racenum10 age3 with newAge
   /FIXED = num_sex racenum10 age3 age3*newage |NOINT SSTYPE(3)

...I get what I posted where age3 has only 2 parameters and sex is included without a reference category

Estimates of Fixed Effectsa

Parameter

Estimate

Std. Error

df

t

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

[num_sex=0]

25.556148

1.073820

13,805

23.799

.000

23.451315

27.660981

[num_sex=1]

24.449104

1.072402

13,805

22.798

.000

22.347050

26.551158

[age3=1.00]

.171610

1.293038

13,805

.133

.894

-2.362920

2.706140

[age3=2.00]

-6.124152

1.282973

13,805

-4.773

.000

-8.638953

-3.609351

[age3=3.00]

0

0

.

.

.

.

.

[age3=1.00] * NewAge

.036716

.022293

13,805.000

1.647

.100

-.006981

.080413

[age3=2.00] * NewAge

.203924

.016284

13,805

12.523

.000

.172004

.235843

[age3=3.00] * NewAge

.082119

.019039

13,805

4.313

.000

.044800

.119438

a. Dependent Variable: timehrs.

Model Dimension

 

Number of Levels

Number of Parameters

Fixed Effects

num_sex

2

2

age3

3

2

age3 * NewAge

3

3

 
 
I then ran
 
MIXED  timehrs BY num_sex racenum10 age3 WITH newage
  /FIXED=racenum10 age3 age3*newage num_sex  | NOINT SSTYPE(3)
 
....and guess what - racenum10 no longer had a refcat but both sex and age did.
 
i never thought it mattered what order the fixed factors were place on the /FIXED line but this completely changed the model parameters.
 
Carol
 

 
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of R B
Sent: Wednesday, May 02, 2012 1:22 PM
To: [hidden email]
Subject: Re: Follow-up to piecewise regression question

Carol,
 
The following "Estimates of Fixed Effects" Table was taken from the MIXED model analysis I ran initially on the talk.sav dataset from that website. Notice that the grand intercept has been removed and there are no reference categories. The main effects represent group-specific intercepts and the interaction effects represent group-specific slopes. I don't have much more time to dedicate to this topic, but I urge you to review the code I posted and to make sure that you are parameterizing your model the same way.
Ryan

 

Estimates of Fixed Effectsa

Parameter

Estimate

Std. Error

df

t

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

[group=.00]

8.076798

4.563993

196

1.770

.078

-.924042

17.077637

[group=1.00]

-24.972667

5.430149

196

-4.599

.000

-35.681687

-14.263646

[group=.00] * age

.681919

.437809

196

1.558

.121

-.181501

1.545340

[group=1.00] * age

3.629046

.286749

196.000

12.656

.000

3.063537

4.194554

a. Dependent Variable: talking on the phone.

 
On Wed, May 2, 2012 at 2:58 PM, R B <[hidden email]> wrote:
Carol,
 
Something's wrong with your analysis. Your "Estimates of Fixed Effects" should include three main effect terms (representing intercepts) and three interaction terms (representing slopes). Instead, it looks like you have a reference category for age. Did you run the analysis a couple different ways?
 
Ryan
On Wed, May 2, 2012 at 2:34 PM, Parise, Carol A. <[hidden email]> wrote:
 
Here is the result of using age in its original form and the \TEST statements.
 
 
What just dawned on me as i was looking at this is that since there is a significant interaction for age3*age, the main effect of age3 is no longer applicable - just like in any other anova.
 
The efffect of age on time, depends on which part of the age continum one is on.  So, there is no effect of being under age 38 on finish time but being between 38-51 means you get slower by .20 hrs for every year. If you are over 50, then you still slow down but not as much.
 
 
Estimates of Fixed Effectsa
Parameter Estimate Std. Error df t Sig. 95% Confidence Interval
            Lower Bound Upper Bound
[age3=1.00] .171610 1.293038 13,805 .133 .894 -2.362920 2.706140
[age3=2.00] -6.124152 1.282973 13,805 -4.773 .000 -8.638953 -3.609351
[age3=3.00] 0b 0 . . . . .
[age3=1.00] * NewAge .036716 .022293 13,805.000 1.647 .100 -.006981 .080413
[age3=2.00] * NewAge .203924 .016284 13,805 12.523 .000 .172004 .235843
[age3=3.00] * NewAge .082119 .019039 13,805 4.313 .000 .044800 .119438
 
 
/TEST = "diff in slopes between  <38 and 38-50" age3*newAge 1 -1  0
 
The estimate is simply 0.037-0.203 and it's significant which means that the slopes change from one age group to another. I believe that this provides the rationale for selecting this cutpoint in the data.
Contrast Estimatesa,b
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
              Lower Bound Upper Bound
L1 -.167208 .027493 13,805 0 -6.082 .000 -.221098 -.113318
a. diff in slopes between  <38 and 38-50
 
 
 
 
  /TEST = "diff in slopes between  <38 and 51+" age3*newAge 1  0 -1
The estimate is  0.203-0.082 and it's not significant which means that the slopes aren't really that different.
 
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
              Lower Bound Upper Bound
L1 -.045403 .029255 13,805 0 -1.552 .121 -.102747 .011940
a. diff in slopes between  <38 and 51+
 
 
 
 
  /TEST = "diff in slopes between 38-50 and 51+" age3*newAge  0  1 -1.
The difference in slopes between 38-50 and 51+ is significant which means that the cut point at age 50 is justfiable.
 
Contrast Estimatesa,b
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
              Lower Bound Upper Bound
L1 .121804 .024961 13,805 0 4.880 .000 .072877 .170731
a. diff in slopes between 38-50 and 51+
 
 
In the end, people under 38 and over 51 slow down by just around the same amount of time...but you slow down more in those middle ages.
 
Thanks again for taking the time to post. This was actually really fun to work though and see how it affected my results.
 
Carol
 
 
 
 
 


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