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Aug 10, 2015 9:21 AM
(5008 views)

First post here as I'm relatively new to JMP. I've been trying to run an 3 way effects test in the fit model option and am getting errors saying "Lost DFs". Also parameter estimates gives "biased" and "zeroed" results. In fit model I'm basically selecting my 3 effects which are year (2013, 2014), type (heirloom or standard) and variety (20 varieties with 4 samples each) and then choosing "full factorial" and then running the standard least squares with effect leverage selected as the emphasis. The data itself consists of 20 bean varieties with different test results like yield, height, weights, etc.... When I run a 2-way with year*type or year*variety there's no problem but when adding the third effect is when the problems happen... I guess I'm just wanting to know more about these errors and if there is something else I should be doing that I'm not.

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Aug 10, 2015 9:44 AM
(7174 views)

Solution

Basically I think what is happening when you see the "biased" and "zeroed" results is you are specifying more terms in the model than you have sufficient degrees of freedom to estimate. So JMP gives you these messages...kind of a gentle reminder to insure that the model you are specifying can indeed be estimated by the data you have collected. You may want to check out the "Fitting Linear Models" JMP Help book devoted to this very broad topic.

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Aug 10, 2015 9:44 AM
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Aug 10, 2015 10:13 AM
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Aug 10, 2015 11:11 AM
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Aug 11, 2015 5:57 AM
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Aug 17, 2015 7:51 AM
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I would recommend that you do series of Y by X fit on each of the variable and see what the outputs give you. Bear in mind that depending on your data type (numeric,,,continuous, character...nominal, etc) the selected analysis that will be conducted will vary and that depends on your data type.

Jenkins Macedo

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Sep 1, 2015 10:36 AM
(4239 views)

There is useful information in the "biased" and "zeroed" messages. Lets say there are two terms in your model A and B (I know you are looking at 3-way interactions but this is just easier for explanation). If the 2 terms are collinear (aliased, correlated, take your pick of terminology) then statistically the effects of A and B can not be estimated independently of each other. This problem can be overcome by putting either A or B (but not both) in the model. Effectively this is what JMP is doing for you - if it puts A in the model and excludes B then A will be labelled as biased and B will be labelled as zeroed.

-Dave