LISTSERV at the University of Georgia
Menubar Imagemap
Home Browse Manage Request Manuals Register
Previous messageNext messagePrevious in topicNext in topicPrevious by same authorNext by same authorPrevious page (December 2004, week 1)Back to main SAS-L pageJoin or leave SAS-L (or change settings)ReplyPost a new messageSearchProportional fontNon-proportional font
Date:         Thu, 2 Dec 2004 14:55:59 -0800
Reply-To:     cassell.david@EPAMAIL.EPA.GOV
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         "David L. Cassell" <cassell.david@EPAMAIL.EPA.GOV>
Subject:      Re: gplot question
In-Reply-To:  <200412020118.iB21I4GF001122@listserv.cc.uga.edu>
Content-type: text/plain; charset=US-ASCII

Ben <benpub7@YAHOO.COM> replied: > thanks, ya huang. it is one solution. > > but what I intended to get is kind of like regression lines, for every x > values, the y has percentiles, they are functions of x, > p10=f1(x), p20=f2(x), p50=f3(x) .......

Okay, I'm still not clear on what you really want. It seems that you want percentiles for all values of X, turned into additional contours on the plot. But how do you plan to interpret P10 for the lowest value of X, given that you are thereby (implicitly) extrapolating out past the range of X in order to do so? Or do you have a large number of Y values for every X, in which case you could actually generate boxplots for each X using PROC BOXPLOT ?

One simple alternative you might want to look into is in the SYMBOL statement. You can specify regression curves using INTERPOL=RxCLMyy where x is L (for a linear fit) or Q (for quadratic) or C (for cubic), CLM gives confidencel imits for the mean regression line, and yy is a number between 50 and 99 that represents the confidence level. yy=95 is typical.

HTH, David -- David Cassell, CSC Cassell.David@epa.gov Senior computing specialist mathematical statistician


Back to: Top of message | Previous page | Main SAS-L page