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Aug 8, 2012

## Is there a way to calculate erfc(x) in JMP?

Title is pretty self explanatory. I am trying to do non-linear least squares with a transcendental function that includes the error function (complement). However when I try to define a predict column using the function I was sad to find that there is no erf(x) in the transcendental group of functions. Without being able to calculate the error function there is no way that I can perform this nonlinear least squares in JMP!  Does anyone know if it is possible to do in JMP? I can use an approximation using elementary functions, however I would like to avoid adding any error if at all necessary. Thanks!

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SurfaceThought

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Accepted Solutions

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Jun 23, 2011

Solution

## Re: Is there a way to calculate erfc(x) in JMP?

erf(X) = 2 * Normal Distribution( :X * Root( 2, 2 ) ) - 1

erfc(X) = 1 - erf(X) = 2 * Normal Distribution(-:X * Root( 2, 2 ) )

where "Normal Distribution()" is the cumulative normal distribution function available in JMP for use in either scripts or column formulas.

4 REPLIES

Super User

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Jun 23, 2011

Solution

## Re: Is there a way to calculate erfc(x) in JMP?

erf(X) = 2 * Normal Distribution( :X * Root( 2, 2 ) ) - 1

erfc(X) = 1 - erf(X) = 2 * Normal Distribution(-:X * Root( 2, 2 ) )

where "Normal Distribution()" is the cumulative normal distribution function available in JMP for use in either scripts or column formulas.

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Aug 8, 2012

## Re: Is there a way to calculate erfc(x) in JMP?

Alright I think this is very much on the right track, thank you so much. I just have one question, whats the purpose of the colon inside the Normal Distribution function?

Edit: Yeah that's definitely what I needed. You rock! thanks for the help!

Super User

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Jun 23, 2011

## Re: Is there a way to calculate erfc(x) in JMP?

The colon indicates that X is a column name.  If you are defining a column formula in terms of other columns, this is how you would reference it.

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Aug 8, 2012

## Re: Is there a way to calculate erfc(x) in JMP?

Oh okay great that's easy enough .

Thank You!