<SPAN class="lia-inline-image-display-wrapper lia-image-align-inline" style="width: 527px;"><IMG src="https://us.v-cdn.net/6038925/uploads/images/8190i686AF667FEB86918/jmp.png" alt="jmp.png" title="jmp.png" /></SPAN>Fit 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!
Replying to myself to add the picture, I
do not see it in the previous message.
