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why am I getting this pattern 'predicted' responses in factorial model?

jmpjill

New Contributor

Joined:

Nov 1, 2017

jmp.pngFit Model(
	Y( :Name( "box cox lambda = -0.2" ) ),
	Effects(
		:Fry,
		:Treatment,
		:Label,
		:Fry * :Treatment,
		:Fry * :Label,
		:Treatment * :Label,
		:Fry * :Treatment * :Label
	),
	Random Effects(
		:panelist# & Random,
		:panelist# * :Fry & Random,
		:panelist# * :Treatment & Random,
		:panelist# * :Label & Random,
		:panelist# * :Fry * :Treatment & Random,
		:panelist# * :Fry * :Label & Random,
		:panelist# * :Treatment * :Label & Random,
		:panelist# * :Fry * :Treatment * :Label & Random
	),
	Personality( Standard Least Squares ),
	Method( REML ),
	Emphasis( Minimal Report ),
	Run(
		:Name( "box cox lambda = -0.2" ) << {Analysis of Variance( 0 ),
		Lack of Fit( 0 ), Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
		Plot Residual by Predicted( 0 ), Plot Effect Leverage( 0 )}
	)
);

Hi,

 

 

I am analyzing some sensory data from three replicate frying studies followed by storage of fried food, using both the fit model platform and the full-factorial repeated measures add-in, for my data that is a 2 treatments  x 2 storage times  x N panelists . There are 3 replicate fry studies (nominal). I have tried both entering the fry replicate as a fixed factor or not entering it (entering it improves the model R2 but doesn't change the problem I'm seeing). The data are continuous, but since they were highly skewed, I did a Box-Cox transformation (the problem is the same regardless of whether I transform the data). The predicted values always seem to be a discrete number, almost like they are put into a category depending on the variables, resulting in the patterns shown above. I have a feeling this is a simple error, but I do not know where. Please help!

 

  

1 REPLY
jmpjill

New Contributor

Joined:

Nov 1, 2017

Replying to myself to add the picture, Ijmp.png do not see it in the previous message.