Why is RSquare nothing? - Summary of Fit of Model

[#1]

Why is RSquare not being calculated for this model?

 RSquare is nothing..JPG

Here is the script the model gave me.  Why does it show "Mixture" instead of the actual components?

Fit Model(
	Y( :FW5% ),
	Effects(
		:FH & Mixture,
		:TotFlow & Mixture,
		:Pch & Mixture,
		:ExFlow & Mixture,
		:FH * :TotFlow,
		:FH * :Pch,
		:TotFlow * :Pch,
		:FH * :ExFlow,
		:TotFlow * :ExFlow,
		:FH * :TotFlow * :ExFlow,
		:TotFlow * :Pch * :ExFlow,
		:ExFlow * :ExFlow,
		:ExFlow * :ExFlow * :ExFlow,
		:FH * :TotFlow * :Pch * :ExFlow,
		:Pch * :ExFlow,
		:FH * :TotFlow * :Pch,
		Scheffe Cubic( :FH, :ExFlow ),
		Scheffe Cubic( :TotFlow, :ExFlow ),
		:FH * :Pch * :ExFlow
	),
	No Intercept,
	Center Polynomials( 0 ),
	Personality( "Standard Least Squares" ),
	History(
		Effects(
			:FH & Mixture,
			:TotFlow & Mixture,
			:Pch & Mixture,
			:ExFlow & Mixture,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:ExFlow * :ExFlow * :ExFlow,
			:FH * :TotFlow * :Pch * :ExFlow,
			:Pch * :ExFlow,
			Scheffe Cubic( :FH, :TotFlow ),
			Scheffe Cubic( :FH, :Pch ),
			:FH * :TotFlow * :Pch,
			Scheffe Cubic( :TotFlow, :Pch ),
			Scheffe Cubic( :FH, :ExFlow ),
			Scheffe Cubic( :TotFlow, :ExFlow ),
			:FH * :Pch * :ExFlow,
			Scheffe Cubic( :Pch, :ExFlow )
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:ExFlow * :ExFlow * :ExFlow,
			:FH * :TotFlow * :Pch * :ExFlow
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:ExFlow * :ExFlow * :ExFlow
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:ExFlow * :ExFlow * :ExFlow
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:Pch * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:ExFlow * :ExFlow * :ExFlow
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:Pch * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:Pch * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH,
			:FH * :TotFlow * :Pch * :ExFlow
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:Pch * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:FH * :FH
		),
		Effects(
			:FH,
			:TotFlow,
			:Pch,
			:ExFlow,
			:FH * :TotFlow,
			:FH * :Pch,
			:TotFlow * :Pch,
			:FH * :TotFlow * :Pch,
			:FH * :ExFlow,
			:TotFlow * :ExFlow,
			:FH * :TotFlow * :ExFlow,
			:Pch * :ExFlow,
			:FH * :Pch * :ExFlow,
			:TotFlow * :Pch * :ExFlow,
			:FH * :TotFlow * :Pch * :ExFlow,
			:ExFlow * :ExFlow,
			:Pch * :Pch,
			:FH * :FH
		)
	),
	Emphasis( "Effect Leverage" ),
	Run(
		:FW5% << {Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
		Plot Residual by Predicted( 1 ), Plot Effect Leverage( 1 )}
	),
	SendToReport(
		Dispatch(
			{"Response FW5%", "FH", "Leverage Plot"},
			"1",
			ScaleBox,
			{Format( "Fixed Dec", 12, 1 ), Min( -1.22802306518175 ),
			Max( 1.2593984962406 ), Inc( 0.5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "FH", "Leverage Plot"},
			"2",
			ScaleBox,
			{Min( -20 ), Max( 19 ), Inc( 5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "TotFlow", "Leverage Plot"},
			"1",
			ScaleBox,
			{Min( -2.5 ), Max( 2.43421052631579 ), Inc( 1 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "TotFlow", "Leverage Plot"},
			"2",
			ScaleBox,
			{Min( -20 ), Max( 38.5 ), Inc( 5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "Pch", "Leverage Plot"},
			"1",
			ScaleBox,
			{Min( -20 ), Max( 16.0902255639098 ), Inc( 5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "Pch", "Leverage Plot"},
			"2",
			ScaleBox,
			{Min( -20 ), Max( 25.5 ), Inc( 5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "ExFlow", "Leverage Plot"},
			"1",
			ScaleBox,
			{Min( -12.6872632709593 ), Max( 12.218045112782 ), Inc( 2 ),
			Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "ExFlow", "Leverage Plot"},
			"2",
			ScaleBox,
			{Min( -20 ), Max( 28.1 ), Inc( 5 ), Minor Ticks( 1 )}
		),
		Dispatch(
			{"Response FW5%", "FH*TotFlow", "Leverage Plot"},
			"2",
			ScaleBox,
			{Min( -20 ), Max( 34.6 ), Inc( 5 ), Minor Ticks( 1 )}
		)
	)
)

[#2]

R square is generally the ratio of the model sum of squares to the corrected total sum of squares. The corrected total sum of squares is computed with the average response. This intercept-only or reduced model is independent of any factor effects. The R square and F-ratio in the analysis of variance are used to compare the performance and significance, respectively, of the full model compared to the reduced model. The full model must contain the reduced model (all of its terms) for this comparison to be valid.

The effects of the mixture components in your full model  include the intercept. It means that the effects of the mixture components and the intercept are inseparable. An explicit intercept term is not included. (Enforced by the No Intercept argument in your script.) The lack of an intercept means that the R square does not exist and any computation that supposedly results in R square is invalid.

[#3 · Accepted solution]

To add to my colleague's post, and dive a little deeper, are the factors truly mixture factors? In other words, do they add up to a constant value (usually 1)? If not, that could also be causing your lack of an RSquare value. By having the "&Mixture" attribute turned on, JMP is expecting those factors to add up to a constant. That is why the "no-intercept" box is checked. However, the model would be a Scheffe model, not truly a no-intercept model as Mark correctly points out (the intercept is included with the main effects in a Scheffe model). But if those factors do NOT add up to a constant, then JMP reverts to the no-intercept model and is likely the cause of your RSquare value missing.