turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Discussions
- :
- Does JMP Pro FIT MIXED personality allow for 3-level hierarchical models ?

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

May 11, 2016 5:02 PM
(3115 views)

Does JMP Pro FIT MIXED personality allow for 3-level hierarchical models (e.g., subject nested within unit nested within division)? I would like to run the following random intercepts model:

PROC MIXED data=survey covtest noclprint method=REML;

class DEPT DIV ;

model SATISF = SUPV COWORKERS INST JOBFIT ;

random intercept / sub = DIV type = UN ;

random intercept / sub = DEPT(DIV) type = UN;

Would the corresponding JMP JSL script look something like this?

Fit Model(

Y( :SATISF ),

Effects( :SUPV, :COWORKERS, :INST, :JOBFIT ),

Random Effects(

Intercept[:Business Unit] & Random Coefficients( 2 ),

Intercept[:Business Unit, :DEPT] & Random Coefficients( 2 )

),

Personality( "Mixed Model" ),

Run( Repeated Effects Covariance Parameter Estimates( 0 ) )

)

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

I am curious about the covariance results you get from running the script as shown above.

Random effects in a three-level model are typically be modeled like this

Random Effects(

Intercept[:DIV] & Random Coefficients( 1 ),

Intercept[:DIV, DEPT] & Random Coefficients(** 2** ),

),

Since you don't estimate random slopes, an alternative specification would be just simply add DIV, and DEPT[DIV] as two random effects like the following (assuming DEPT nested within DIV) :

**Random Effects( :DIV, :DEPT[:DIV] ),**

7 REPLIES

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Correction to my original post:

Would the JMP JSL Script look something like this?

Fit Model(

Y( :SATISF ),

Effects( :SUPV, :COWORKERS, :INST, :JOBFIT ),

Random Effects(

Intercept[:DIV] & Random Coefficients( 1 ),

Intercept[:DIV, DEPT] & Random Coefficients( 1 ),

),

Personality( "Mixed Model" ),

Run( Repeated Effects Covariance Parameter Estimates( 0 ) )

);

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

I would suggest that you need to let JMP create the script for you. Go into: Analyze==>Fit Model. Generate your "Mixed Model". Yes JMP will handle 3 level hierarchical nesting. Once you have defined the model, then run the analysis. Under the red triangle at the top of the output, Go to: Script==>Write Script to Script Window. It will then generate the script required to create the model you defined.

Jim

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Hi Jim, Thanks for the response. However, that is what I did to get the script. I just want to make sure I specified the correct model.

I went to Analyze / Fit Model

- selected Personality = Mixed Model

-- specified the Fixed and Random Effects

-- then saved the Script to the Script Window, copied and pasted

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

I am curious about the covariance results you get from running the script as shown above.

Random effects in a three-level model are typically be modeled like this

Random Effects(

Intercept[:DIV] & Random Coefficients( 1 ),

Intercept[:DIV, DEPT] & Random Coefficients(** 2** ),

),

Since you don't estimate random slopes, an alternative specification would be just simply add DIV, and DEPT[DIV] as two random effects like the following (assuming DEPT nested within DIV) :

**Random Effects( :DIV, :DEPT[:DIV] ),**

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Thank you! This is very helpful. What do the numbers after "Random Coefficients" mean (e.g., 1 or 2)? I can't find any documentation for the JSL script for Mixed Models.

If I wanted to add random slopes at the DEPT level, would the script look like this?

** **

Y**(** :SATISF **)**,

Effects**(** :Factor1, :Factor2, :Factor3, :Factor4 **)**,

** **

Intercept**[**:DIV**]** & Random Coefficients**(** **1** **)**,

Intercept**[**:DIV, :DEPT**]** & Random Coefficients**(** **2** **)**,

:Factor1**[**:DIV, :DEPT**]** & Random Coefficients**(** **3** **)**

** **

**)**,

Personality**(** "Mixed Model" **)**,

Run**(** Repeated Effects Covariance Parameter Estimates**(** **0** **)** **)**

** **

**)**;

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

*What do the numbers after "Random Coefficients" mean (e.g., 1 or 2)?*

They indicate hierarchical levels, e.g., DIV, DEPT within DIV. They are assigned when using the Nest Random Coefficient tab to add random effects.

*If I wanted to add random slopes at the DEPT level, would the script look like this?*

Your random effects would look like this

Random Effects(

Intercept**[**:DIV**]** & Random Coefficients**(** **1** **)**,

Intercept**[**:DIV, :DEPT**]** & Random Coefficients**(** **2** **)**,

:Factor1**[**:DIV, :DEPT**]** & Random Coefficients**(** 2 **)**

**)**,

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Thank you!