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- Help with 'Fit Model' for two-way ANOVA with interaction effects

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Jun 7, 2014 10:16 AM
(3710 views)

The JMP 10 Pro sample data set, 'Snapdragon', is listed as an example for conducting two-way ANOVA. I am having trouble using the 'Fit Model' platform to show results on interaction effects. It works very nicely for displaying the results of the additive model, but when the two effects are crossed and added, the output does not display any results. Any insight into why this problem is occurring would be appreciated. Thanks.

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Not enough observations to estimate the interaction effect. Add some replication and you'll be able to run the full factorial model. Try the script below to see the difference.

dt=Open**(** "$SAMPLE_DATA/Snapdragon.jmp" **)**<< **Concatenate****(**

Data Table**(** "Snapdragon.jmp" **)**,

Data Table**(** "Snapdragon.jmp" **)**,

Output Table**(** "ExpandedSnapdragon" **)**

**)**;

dt<<**New Column****(** "NewY",

Numeric,

Continuous,

Format**(** "Best", **12** **)**,

Formula**(** :Y + Random Normal**()** **)**,

Set Selected

**)**;

dt<<**Fit Model****(**

Y**(** :NewY **)**,

Effects**(** :Soil, :Block, :Soil * :Block **)**,

Personality**(** Standard Least Squares **)**,

Emphasis**(** Effect Leverage **)**,

Run**(**

:NewY << **{****Lack of Fit****(** **0** **)**, Plot Actual by Predicted**(** **1** **)**,

Plot Regression**(** **0** **)**, Plot Residual by Predicted**(** **1** **)**,

Plot Effect Leverage**(** **1** **)}**

**)**

**)**;

2 REPLIES

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Not enough observations to estimate the interaction effect. Add some replication and you'll be able to run the full factorial model. Try the script below to see the difference.

dt=Open**(** "$SAMPLE_DATA/Snapdragon.jmp" **)**<< **Concatenate****(**

Data Table**(** "Snapdragon.jmp" **)**,

Data Table**(** "Snapdragon.jmp" **)**,

Output Table**(** "ExpandedSnapdragon" **)**

**)**;

dt<<**New Column****(** "NewY",

Numeric,

Continuous,

Format**(** "Best", **12** **)**,

Formula**(** :Y + Random Normal**()** **)**,

Set Selected

**)**;

dt<<**Fit Model****(**

Y**(** :NewY **)**,

Effects**(** :Soil, :Block, :Soil * :Block **)**,

Personality**(** Standard Least Squares **)**,

Emphasis**(** Effect Leverage **)**,

Run**(**

:NewY << **{****Lack of Fit****(** **0** **)**, Plot Actual by Predicted**(** **1** **)**,

Plot Regression**(** **0** **)**, Plot Residual by Predicted**(** **1** **)**,

Plot Effect Leverage**(** **1** **)}**

**)**

**)**;

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Thanks MS. Your script was very helpful.

Since there is only one replicate for each soil*block combination, I can see why JMP was unable to estimate an interaction effect.