How can I make cumulative probability plot like the one below? Appreciate your help.
Till cumulative probability will be available via Summary Statistics in Graph Builder ( ) ....
Plot quantile by actual can be used as a workaround:
It's similar to CDF - but with access to the data points.
disadvantages:
- many black points!
- no split plots
- ...
Names Default to Here(1);
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
Oneway(
Y( :weight ),
X( :age ),
All Graphs( 0 ),
Plot Quantile by Actual( 1 ),
CDF Plot( 1 ),
Local Data Filter( Close Outline( 1 ) )
)
[acually, intended to be a reply to
https://community.jmp.com/t5/Discussions/how-to-make-cumulative-probability-plots-in-JMP/m-p/527722/...
... hm, at this position it's more or less a replica o the previous post - which triggered me to understand the meaning of
@r30363 wrote:Left side Y-axis with standard deviation, right side Y-axis with cumulative probability
:]
Hi @r30363 , via OneWay, I could offer left side cumulative probability - and right side standard deviation (not 1,2,3, but 0.67 = 75%, 1.28=90%, ...)
Names Default to Here(1);
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
Oneway(
Y( :weight ),
X( :sex ),
All Graphs( 0 ),
Plot Quantile by Actual( 1 ),
Normal Quantile Label( 1 ) // by default
)
@r30363 wrote:if I want to do Left side Y-axis with standard deviation, right side Y-axis with cumulative probability, how to add a right Y in graph builder?
The left and right Y axis are not linked. So, after changing one of them one has to click on "reset Normal Quantile Scale" - or is there a Plot Scale Change Handler in Jmp? *)
Names Default To Here( 1 );
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
New Column( "normal Quantiles" );
Graph Builder(
Transform Column( "Cumulative Probability", Formula( Col Rank( :height, :sex ) / (Col Number( :height, :sex ) + 1) ) ),
Show Control Panel( 0 ),
Variables( X( :height ), Y( :normalQuantiles ), Y( :Cumulative Probability, Position( 1 ), Side( "Right" ) ), Overlay( :sex ) ),
Elements( Points( X, Y( 2 ) ), Smoother( X, Y( 2 ) ) ),
SendToReport(
Dispatch(
{},
"Cumulative Probability",
ScaleBox,
{Scale( "Normal Probability" ), Format( "Best", 12 ), Min( 0.01 ), Max( 0.99 ), Inc( 0.1 ), Minor Ticks( 1 )}
)
)
);
New Namespace(
"reset"
);
reset:NormalQuantileScale = Function( {},
myScript = (Current Report() << XPath( "//AxisBox" ))[3] << get script();
myMin = Normal Quantile( myScript["min"] );
myMax = Normal Quantile( myScript["max"] );
(Current Report() << XPath( "//AxisBox" ))[2] << Min( myMin ) << Max( myMax ) << inc( 0.5 );
);
Current Report()[List Box( 2 )] << append( Button Box( "reset Normal Quantile Scale", reset:NormalQuantileScale() ) );
reset:NormalQuantileScale()asking for a friend/colleague:
With the current version of Jmp, is there an easy way to generate a split grouped probability plot?
I can generate such a plot via Graph Builder, but I have to implement a hand-made functionality which re-generates the Transform column once the user selects different columns or different entries in the Local Data Filter - and it's extremely slow!
Accepting that the graph won't be as interactive as what a Jmp user is used from Graph Builder, one can use Live Distribution/Compare Groups - with a By variable. It can be much faster than Graph Builder - but there are cases where you have to wait literally "hours" till the plot shows up [see below].
The original report doesn't look like expected - but with several commands to the report (*) the plot gets almost as nice as in Minitab.
The benefit compared to the Graph Builder approach: more Distributions/scales to select from
The disadvantages compared to Minitab:
- not stable
- very slow
Open( "$SAMPLE_DATA/Cars.jmp" );Graph Builder(
Transform Column(
"CDF",
Formula(
Col Rank(
:Wt,
:Doors,
:Year
) / (Col Number(
:Wt,
:Doors,
:Year
) + 1)
)
),
Variables( X( :Wt ), Y( :CDF ), Group Y( :Year ), Overlay( :Doors ) ),
Elements( Line Of Fit( X, Y, ), Points( X, Y, Legend( 14 ) ) )
);
myld = Life Distribution(
Perspective( Compare Groups ),
Y( :Wt ),
Grouping( :Doors ),
Confidence Interval Method( Wald ),
Select Distribution( Distribution, Normal ),
Select Scale( Normal ),
<< Show Statistics( 0 ),
By( :Year)
);
(myld << XPath("(//OutlineBox[text()='Statistics'])")) << Visibility( "Collapse" );
(myld << xpath("//OutlineBox[text()='Individual Group']")) << Visibility( "Collapse" );
((myld << xpath ("//RadioBox")) << parent) << Visibility( "Collapse" );
(myld << xpath("//OutlineBox[text()='Compare Distribution']//IfBox"))<< Visibility( "Collapse" );
//(myld << xpath("//OutlineBox[text()='Compare Distribution']")) <<Set Title( "" );
// current Report()[ListBox(1)] << Set Horizontal( 1 );
(myld << xpath("//OutlineBox")) <<Set Title( "" );
myABS = (myld << xpath ("//OutlineBox//AxisBox"));
Transform Each({ABList},myABs[1::Nitems(myABs)-1],ABList[2]) << Label Row(
{Automatic Tick Marks( 0 ), Show Major Ticks( 0 ),
Show Minor Ticks( 0 ), Show Major Labels( 0 )} );
myTEBs = (myld << xpath ("//OutlineBox//TextEditBox"));
Transform Each({TEBList},myTEBs[1::Nitems(myTEBs)-1],TEBList[2])<<Set Text( "" ) ;
At the moment, Life Distribution is definitely the most versatile/elegant way in Jmp to create a probability plot.
The confidence intervals look very nice.
On the other hand, it takes a while for Life Distribution to open - depending on the number of data points, it can take hours:
TS-00122043
