turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Discussions
- :
- How can we determine the local maxima in a multi-p...

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Oct 14, 2016 6:46 AM
(2894 views)

We'd like to be able to determine the maximum values of multi-peaked distribution, such as the one below, with some algorithm or utility.

Any ideas how that can be done?

Solved! Go to Solution.

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Oct 14, 2016 7:56 AM
(5534 views)

Solution

If you get the number of peaks by inspection, you could try something like this:

NamesDefaultToHere(1);

// Make some random data (with known parameters)

d1 = J(40, 1, RandomNormal(0, 1));

d2 = J(60, 1, RandomNormal(4, 2));

d3 = J(30, 1, RandomNormal(-6, 1));

// Make a table

dt = AsTable(VConcat(d1, d2, d3));

dt << setName("A Mixture of Samples from Three Gaussians");

Column(dt, "Col1") << setName("Y");

// Do a 'Normal Mixtures' analysis

dt << Distribution(

Continuous Distribution(

Column( :Y ),

Vertical( 0 ),

Fit Distribution( Normal Mixtures( Clusters( 3 ) ) )

),

Histograms Only,

SendToReport(

Dispatch(

{"Y"},

"Distrib Histogram",

FrameBox,

{Frame Size( 600, 267 ), DispatchSeg( Hist Seg( 1 ), Bin Span( 2, 0 ) )}

)

)

);

1 REPLY

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Oct 14, 2016 7:56 AM
(5535 views)

If you get the number of peaks by inspection, you could try something like this:

NamesDefaultToHere(1);

// Make some random data (with known parameters)

d1 = J(40, 1, RandomNormal(0, 1));

d2 = J(60, 1, RandomNormal(4, 2));

d3 = J(30, 1, RandomNormal(-6, 1));

// Make a table

dt = AsTable(VConcat(d1, d2, d3));

dt << setName("A Mixture of Samples from Three Gaussians");

Column(dt, "Col1") << setName("Y");

// Do a 'Normal Mixtures' analysis

dt << Distribution(

Continuous Distribution(

Column( :Y ),

Vertical( 0 ),

Fit Distribution( Normal Mixtures( Clusters( 3 ) ) )

),

Histograms Only,

SendToReport(

Dispatch(

{"Y"},

"Distrib Histogram",

FrameBox,

{Frame Size( 600, 267 ), DispatchSeg( Hist Seg( 1 ), Bin Span( 2, 0 ) )}

)

)

);