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
- :
- rank transformation

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
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 28, 2010 9:26 AM
(794 views)

i want to compare the degree of defoliation of different station. so i sampled three trees per station and for each tree i sampled at the bottom, at the middle and at the top of the crown. i wanted to used a two-way ANOVA to see whether there is a difference between station and between the level in the tree. but my problem is that the variances are highly unequal.

so i was wondering how i could do a rank transformation on my data using jmp? then i would do a regular two-way ANOVA.

Thanks in advance

Adrien

5 REPLIES

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

Jul 29, 2010 2:53 PM
(502 views)

I'd sort your data by defoliation, then create a new column named "rank", and apply a formula to this column--the row function "sequence" will number the rows in this column from 1 to whatever you specify, adn because they are sorted by defoliation, will give you ranks.

This won't serve you if you have lots of tied observations, however.

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

Jul 31, 2010 12:55 AM
(502 views)

One transformation you might consider for your data is an arcsine square root transformation, which is the one usually recommended when your data is a proportion (see for example http://udel.edu/~mcdonald/stattransform.html ). This one goes some way towards normalising variances towards the extreme ends of the scale (i.e. 0% and 100%), though even there there's not much you can do to normalise the

If your data includes large numbers of instances of almost complete defoliation or almost no defoliation at all, you might consider analysing a binary variable (i.e. "defoliated" / "not defoliated") or an ordinal variable (1="not defoliated", 2="partially defoliated", 3="wholly defoliated") using a logistic regression (see for example http://udel.edu/~mcdonald/statlogistic.html ), which would still enable you to fit explanatory variables to your data like station, type of tree, age of tree, height of tree etc. Logistic regression is provided within the "Fit Model" platform: just change the personality to whatever type of logistic regression you intend to perform, and make sure that the modelling type of your response variable is set to "Nominal" or "Ordinal" (otherwise the two logistic regression options will be greyed out).

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

Aug 3, 2010 4:12 AM
(502 views)

thanks for your help, i tryed the acrsin square root transformation but the variances are still unequal.

and if i used a logistic regression i wouldn't be able to compare the degree of defoliation between the different level of the tree, right?

thanks again

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

Aug 1, 2010 8:20 AM
(502 views)

Second question (set). What do you want to see? Do you want to find out if the variation is different within tree or tree-to-tree or between station? Use control charts and variability charts to see the differences.

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

Aug 3, 2010 4:17 AM
(502 views)

and i want to see if where the defoliation is the most important ( top, middle bottom of the crown) and i also want to see which stations are significantly more defoliated then the other