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
- :
- How to do three-way Full Factorial ANOVA with unba...

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

Jan 4, 2017 6:59 PM
(865 views)

Hello ,

I am trying to do the yield data analysis with three factors (2 yeras, 2 locations, 8 varieties). However the data is not a balanced data as following:

In 2012, the 8 varieties were tested only in location A, but in 2013, the 8 varieties were tested in locations A and B.

How do I still run a full-factorial 3-way anaysis using Mixed Model if I want to treat "year" as random effect? because I wtill wish to see if there are any interactions between the three factors.

Thank you very much for your help!!

1 ACCEPTED SOLUTION

Accepted Solutions

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

Jan 5, 2017 4:34 AM
(1499 views)

Solution

You can fit a model with all of the desired effects except for the **Year*****Location** interaction effect.

- Select
**Analyze**>**Fit Model**. - Select the
**Year**,**Location**, and**Variety**data columns. - Click
**Macros**and select**Factorial to Degree**. - Select the
**Year*****Location**effect and click**Remove**. - Select the
**Year**and the**Year*****Variety**terms, then click the red triangle next to**Attributes**and select**Random**. - Click Run.

That is all you cah do with this data.

19 REPLIES

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

Jan 5, 2017 4:34 AM
(1500 views)

You can fit a model with all of the desired effects except for the **Year*****Location** interaction effect.

- Select
**Analyze**>**Fit Model**. - Select the
**Year**,**Location**, and**Variety**data columns. - Click
**Macros**and select**Factorial to Degree**. - Select the
**Year*****Location**effect and click**Remove**. - Select the
**Year**and the**Year*****Variety**terms, then click the red triangle next to**Attributes**and select**Random**. - Click Run.

That is all you cah do with this data.

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

Jan 5, 2017 4:57 AM
(845 views)

Mark,

You are not qualifying anything about the Year*Location*Variety term. That term will remain viable in this design?

Jim

Jim

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

Jan 5, 2017 5:27 AM
(842 views)

I did not consider the three-factor interaction effect. Two of the three two-factor interaction effects may be estimated if the only missing values are for Location = B when Year = 2012. To be sure, I made a full factorial design for these factors as they were described and had not trouble estimating the parameters if the Year*Location term was omitted.

Who believes in three-factor interactions anyway?

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

Jan 5, 2017 5:33 AM
(837 views)

Hello Mark,

Thank you very much for your answer. I assume I would have to remove the **Year*****Location*variety** term from the model as well?

Will I still be able to see the effect from year on the yield then? Thank you

Juno

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

Jan 5, 2017 5:44 AM
(832 views)

The estimability of parameters depends on the design the 32-run full factorial design (2*2*8) supports estimation of all possible terms, assuming that all are treated as nominal factors. The missing data prohibits the estimation of the Year*Location effect and the Year*Location*Variety effect.

Further, if you treat Year as a random effect, then you won't want to estimate the Year*Variety effect. You can estimate the Year effect regardless of how you model it (fixed or random effect).

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

Jan 5, 2017 7:32 AM
(805 views)

Hello Mark,

Thank you so much!

Will I still be able to get the LSD for all the varieties from this combined analysis so I can make pairwise comparisons?

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

Jan 5, 2017 8:14 AM
(797 views)

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

Jan 5, 2017 8:19 AM
(794 views)

I forgot that you are fitting a mixed model. My nstructions suited the case of a model with only fixed effects.

These commands are still available but they are located elsewhere. Open the **Effect Details** outline and you will find the same information for each term as you would in the Leverage Plot (except for the plot itself).

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

Jan 5, 2017 9:14 AM
(783 views)

Hello Mark,

Thank you very much for explaing it so clear.

I understand that if I run the Mixed Model with "year" as random effect, I won't be able to obtain a common LSD, but still can do pairwise comparisons.

However, will I be able to get a LSD if I run it by "**Year**" ?

Y : **Yield**

By **Year**

Model Effcts: **Varieties**, **Locations**, **Varieties*Locations **

Thank you!