Iterate over elements of report

teoten

Hi, I'm new using JMP but I find it very useful and practical, so far I'm enjoying it a lot. I’m learning JSL because I need to automatize some of the analysis for my teammates and for efficiency. I am starting with something simple but I need help to move forward.

 

I need to customize the results of the distribution platform to perform a test of normality for several columns of a table, in order to repeat it as often as I or my colleagues need it. I managed to write a simple script that achieves this in an easy way. However I am having problems on how to edit particular elements of the report when a condition meet.

 

Here is the script that return values from the Shapiro-Wilk test:

 

// Select variables to analyze 
Names Default to Here( 1 );

dt = Current Data Table();

dialog = New Window( "Select Columns", << Modal,
	Panel Box( "Column Selection",
		Lineup Box( N Col( 1 ),
			Text Box( "Select columns for distribution" ),
			y col list = Col List Box( All )
		)
	),
	H List Box(
		Button Box( "OK",
			y col = y col list << Get Selected;
		),
		Button Box( "Cancel" )
	)
);

If( dialog["Button"] == -1,
	Throw( "User cancelled" )
);

// Create distribution
dist = dt << Distribution(Columns(evalList(y col)),Histograms Only);

// Add parameters
dist << Normal Quantile Plot( 1 );
dist << Fit Distribution(Normal(Goodness of Fit(1)));

How can I make JMP to automatically show customized summary statistics (inluding Skewness and Kurtosis) when the P value (Prob<W) is < 0.05.

 

When I try to edit something on the report, for example, remove the parameter estimates

distr["Parameter Estimates"] << Delete;

It does it only for the first element of the report. 

 

I would appreciate any help on how to manipulate elements of the report, in general such as remove all the parameter estimates, and in particular such as P < 0.05.

 

Thanks!

 

MathStatChem

When you use Distribution() with a list of columns, the report window contains the output from many 'independent' distribution analysis.  Each one of those analysis is a separate object.  Interacting with those objects can be different than interacting with the  report layer in the report window  When you do

 

dist << Normal Quantile Plot(1);

that message is broadcast to all of the distribution objects that are being displayed in the report window.  But when you do

 

distr=Report(dist);
distr["Parameter Estimates"] << Delete;

what JMP does is to go to the first instance of the report layer object that can be referenced using the "Parameter Estimates" string, which in this case is the Outline Box under the Fitted Normal results.   To do what you want to do, you would need iterate through each part of the report, get the result for the Shapiro-Wilk test from the report layer, and then decide to delete parts of the output report.  

 

I think in your case it would be easier to iterate the distribution analysis for each column one at a time, appending the results to a new report window.   Here is some example code that shows how to do that

 

// Select variables to analyze 
Names Default To Here( 1 );

dt = Current Data Table();

dialog = New Window( "Select Columns",
	<<Modal,
	Panel Box( "Column Selection",
		Lineup Box( N Col( 1 ),
			Text Box( "Select columns for distribution" ),
			y_col_list = Col List Box( All )
		)
	),
	H List Box(
		Button Box( "OK", y_col = y_col_list << Get Selected ),
		Button Box( "Cancel" )
	)
);

If( dialog["Button"] == -1,
	Throw( "User cancelled" )
);


// Create new empty report window
nw = New Window( "Distribution Analysis", hb = H List Box() );

// iterate through each column

For( ii = 1, ii <= N Items( y_col ), ii++, 
// Create distribution, display in report window by appending
	// to the H List Box
	hb << Append(
		dist = dt << Distribution( Columns( y_col[ii] ), Histograms Only )
	);
	
	// get reference to the report layer for the individual distribution analysis
	distr = Report( dist );
	
    // turn on Normal Quantile Plot and add the Normal distribution
    // fit with the goodness of fit test
	dist << Normal Quantile Plot( 1 );
	dist << Fit Distribution( Normal( Goodness of Fit( 1 ) ) );
	
	// instead of deleting the parameter estimates, just close the 
	// outline box that contains them
	distr["Parameter Estimates"]<<Close;

	// get the p-value for the shapiro wilks test from the report layer
	// CAUTION:  the  Goodness of Fit test output depends on 
	// the number of observations and other conditions, so this may not be
	// robust to all scenarios

	/* the report element that contains the p-value is in the second
	Number Col Box in the first Table Box underneath the 
	"Goodness-of-Fit Test" outline box. Using << get on that report 
	object returns a list, and use the [1] index to get the first element
	of that list*/

	swpval = (distr["Goodness-of-Fit Test"][Table Box( 1 )][Number Col Box( 2 )] <<
	get)[1];
	
	// check to see if GOF test p value is <0.05 and if so add custom summary stats
	If( swpval < 0.05,
		dist << Summary Statistics( 1 );
		dist << Customize Summary Statistics( Skewness( 1 ), Kurtosis( 1 ) );
	);
);