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Oct 9, 2015 9:07 AM
(781 views)

Is there a way to graph Confidence (say .9) in a bunch of Std Deviations other than digging through each of their distributions? Maybe I'm just brain-dead this morning.

*Edit* Right now I'm just doing this but I feel like there is a better way.

x = current report();

est = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[1]/NumberColBoxItem[2]/text()"));

LCI = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[2]/NumberColBoxItem[2]/text()"));

UCI = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[3]/NumberColBoxItem[2]/text()"));

New Table("Sigma GRR",

new column("Estimates", Character, SetValues(est)),

new column("LCI", Character, SetValues(LCI)),

new column("UCI", Character, SetValues(UCI)),

);

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Solution

Hi, Vince!

Not sure this is what you're after, since there are many, many ways to do this, but you could always "roll your own"...

Names Default to Here(1);

// Open the dataset in question.

BigClass_dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

// Summarize the standard deviation by age.

BigClassSummary_dt = BigClass_dt << Summary(

Group( :age ),

Std Dev( :height ),

output table name("Big Class Summary"));

// Set alpha as a table variable.

BigClassSummary_dt << Set Table Variable("alpha", 0.10);

// Calculate the Confidence Intervals

BigClassSummary_dt << New Column( "LCI",

numeric, continuous,

Formula( Root(((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( 1 - :alpha / 2, :N Rows - 1 ), 2 )),

eval Formula);

BigClassSummary_dt << New Column( "UCI",

numeric, continuous,

Formula( Root( ((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( :alpha / 2, :N Rows - 1 ), 2 ) ),

eval Formula);

// Revel in your fly statistical swerve!

5 REPLIES

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Hi, Vince!

Not sure this is what you're after, since there are many, many ways to do this, but you could always "roll your own"...

Names Default to Here(1);

// Open the dataset in question.

BigClass_dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

// Summarize the standard deviation by age.

BigClassSummary_dt = BigClass_dt << Summary(

Group( :age ),

Std Dev( :height ),

output table name("Big Class Summary"));

// Set alpha as a table variable.

BigClassSummary_dt << Set Table Variable("alpha", 0.10);

// Calculate the Confidence Intervals

BigClassSummary_dt << New Column( "LCI",

numeric, continuous,

Formula( Root(((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( 1 - :alpha / 2, :N Rows - 1 ), 2 )),

eval Formula);

BigClassSummary_dt << New Column( "UCI",

numeric, continuous,

Formula( Root( ((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( :alpha / 2, :N Rows - 1 ), 2 ) ),

eval Formula);

// Revel in your fly statistical swerve!

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Oct 12, 2015 4:03 AM
(602 views)

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Oct 12, 2015 6:55 AM
(602 views)

Yeah, smoore2, bootstrapping is one of the "many, many ways" to do this, and it has some advantages.

I wish JMP had implemented a better approach to their bootstrap estimation. I ask Santa for this every Christmas!

I had to write an addin to get the Bias Corrected accelerated (BCa) estimations from the R "boot" package, which is a more defensible method, in my opinion.

Maybe if more individuals lifted their voice in unison, Santa would hear us??

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Oct 12, 2015 12:52 PM
(602 views)

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Oct 13, 2015 5:52 AM
(602 views)