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
- :
- Multiple correspondence analysis

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

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Apr 30, 2017 12:55 AM
(1857 views)

I have produced some interesting multiple correspondent analysis graphs using a 600,000 case data set. The plots show products and issues fairly clearly for a small number of examples. I do not have control over the examples--or the number of them illustrated. I would like a few more. Most important, I would like to control the scale of the figures that are plotted. I want to move the extreme points out to break apart the overlaps between near concepts. Presumably, if I had access to the actual material plotted, I could transform the axes, stretching the ends. Does anyone have insight into how to access the coordinates? When I try to use the coordinates as saved, I cannot seem to reproduce the two-dimensional plot.

Thanks

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

I have re-created the **Car Poll** example at the beginning of chapter 4 about *Multiple Correspondence Analysis* in the *Consumer Research* guide book for JMP 13:

I used the **Correspondence Analysis** > **Save Coordinates** command in the red triangle menu at the top to produce the same plot in Graph Builder. I used **Rows** > **Color or Mark by Column** and then manually adjusted the axes (scaling, add reference lines at 0) to finish:

So you should be able to re-produce the same plot as well. We can walk through the steps here or you can study this platform on your own with the guide book (select **Help** > **Books** > **Consumer Research**).

The MCA method is also known among the myriad multi-dimensional scaling methods as 'optimal scaling.' I am not sure what you mean when you say that you "want to move the extreme points out to break apart the overlaps between near concepts." The point of MCA is to show variable association between the individual levels. This plot is an analytical graphic and the coordinates are determined by the data based on their associations.

Note that you can manually drag the labels from their original positions if the plot is cluttered in one particular area where the associations are strong and they overlap.

Learn it once, use it forever!

1 REPLY

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

I have re-created the **Car Poll** example at the beginning of chapter 4 about *Multiple Correspondence Analysis* in the *Consumer Research* guide book for JMP 13:

I used the **Correspondence Analysis** > **Save Coordinates** command in the red triangle menu at the top to produce the same plot in Graph Builder. I used **Rows** > **Color or Mark by Column** and then manually adjusted the axes (scaling, add reference lines at 0) to finish:

So you should be able to re-produce the same plot as well. We can walk through the steps here or you can study this platform on your own with the guide book (select **Help** > **Books** > **Consumer Research**).

The MCA method is also known among the myriad multi-dimensional scaling methods as 'optimal scaling.' I am not sure what you mean when you say that you "want to move the extreme points out to break apart the overlaps between near concepts." The point of MCA is to show variable association between the individual levels. This plot is an analytical graphic and the coordinates are determined by the data based on their associations.

Note that you can manually drag the labels from their original positions if the plot is cluttered in one particular area where the associations are strong and they overlap.

Learn it once, use it forever!