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Jan 26, 2012 4:11 PM
(1246 views)

Hi, I’m hoping to get some help with JSL scripting when there is data missing in a data table.

We are using JSL to execute a query against a database that pulls data into a data table that looks like:

Plate | Compound | M1 | M2 | M3 | M4 |

A121 | cmp001 | 85.203 | 30.022 | 36.797 | 0.0629 |

A122 | cmp002 | 84.031 | 34.902 | 31.596 | 0.063 |

A123 | cmp003 | 89.028 | 30.677 | 34.903 | 0.063 |

A124 | cmp004 | 91.2455 | 35.277 | 35.327 | 0.0631 |

A125 | cmp005 | 91.257 | 36.927 | 38.836 | 0.063 |

where M’s are all different measurements. We have a JSL script that makes several plots from the data and also does a fit model of

Fit Model(

Y( :M1, :M2, :M3, :M4 ),

Effects( :Plate, :Compound, :Plate * :Compound ),

Personality( Standard Least Squares ),

Emphasis( Effect Leverage ),

Run(

:M1 << {Lack of Fit( 0 ), Plot Actual by Predicted( 1 ),

Plot Regression( 0 ), Plot Residual by Predicted( 1 ),

Plot Effect Leverage( 1 ), Show VIF( 1 ), {:Strain <<

{LSMeans Tukey HSD( 0.05, Crosstab Report( 0 ) )}}},

:M2 << {Lack of Fit( 0 ), Plot Actual by Predicted( 1 ),

Plot Regression( 0 ), Plot Residual by Predicted( 1 ),

Plot Effect Leverage( 1 ), Show VIF( 1 ), {:Strain <<

{LSMeans Tukey HSD( 0.05, Crosstab Report( 0 ) )}}},

:M3 << {Lack of Fit( 0 ), Plot Actual by Predicted( 1 ),

Plot Regression( 0 ), Plot Residual by Predicted( 1 ),

Plot Effect Leverage( 1 ), Show VIF( 1 ), {:Strain <<

{LSMeans Tukey HSD( 0.05, Crosstab Report( 0 ) )}}},

:M4 << {Lack of Fit( 0 ), Plot Actual by Predicted( 1 ),

Plot Regression( 0 ), Plot Residual by Predicted( 1 ),

Plot Effect Leverage( 1 ), Show VIF( 1 ), {:Strain <<

{LSMeans Tukey HSD( 0.05, Crosstab Report( 0 ) )}}},

)

)

Where we are running into trouble is that occasionally we have a failed measurement system and the entire M3 or M4 column will be full of missing values (count = 0). When this occurs, the Fit Model crashes the JSLscript.

What is the best way to script a Fit Model against a data table where occasionally a column will be have no data? Is there an easy way of first looking at which columns contain data and then passing that as a list to the Fit Model platform? I don't really feel like writing a ton of IF statements to check to see which columns are full vs empty and then running a different fit model script across it as we actually have over 10 different measurements and any one of them can be missing on any day.

Thanks for your help.

1 REPLY

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At least in JMP 9, Fit Model( ) accepts a list argument. Eval() must be used if the list is a variable.

Heres an example on how to build a list with non-empty columns (does not check if full but is easy to add):

Ys = **{**:M1, :M2, :M3, :M4**}**;

For**(** i = N Items**(** Ys **)**, i > **0**, i--,

Summarize**(** n = Count**(** Ys**[**i**]** **)** **)**;

If**(** n == **0**,

Remove From**(** Ys, i **)**

**)**;

**)**;

Fit Model**(**

Y**(** Eval**(** Ys **)** **)**,

Effects**(****...**