|Date: ||Fri, 5 Feb 2010 07:41:54 -0600|
|Reply-To: ||Bhupinder Farmaha <bhupi80singh@YAHOO.CO.IN>|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||Bhupinder Farmaha <bhupi80singh@YAHOO.CO.IN>|
|Subject: ||Re: Normality problem|
|Content-Type: ||text/plain; charset="utf-8"|
From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Peter Flom
Sent: Friday, February 05, 2010 5:23 AM
Subject: Re: Normality problem
Bhupinder Farmaha <bhupi80singh@YAHOO.CO.IN> wrote
>I need your suggestion about appropriate measure to tackle non-normal data.
>I ran an agronomic experiment for three years. I am modeling it using Proc
>Mixed. I found that data do not meet the assumption of normality. The
>problem I found that response variables values are quite different for one
>year compared to other two years. The part of the reason it is weather
>driven. I have tried log transformation but it didn't work at all. I don't
>know if I have to give less weights to data from that year or what can be
>the other approach. It is clear from the some of the preliminary that data
>have this problem because crop is adversely affected in one particular years
>compared to others.
>I would appreciate any feedback on this.
What is it that you are trying to model?
What is your DV and what are your IVs?
From what you say, it sounds like your DV is something related to crops. Maybe crop yield?
But a couple things:
1) The fact that the DV is different year to year is not necessarily a problem for PROC MIXED.
2) The fact that the data (I'm guessing you mean the DV) is not normal is not necessarily a problem either, you need to look at the residuals.
3) I'm not sure what you mean by "weight the data"; if the problem is outliers, then you might want some form of robust regression, but I don't know if there is a way to do robust mixed regression in SAS. It might be possible in NLMIXED.
4) Have you looked at PROC GLIMMIX and PROC NLMIXED?
Peter L. Flom, PhD
Website: http://www DOT statisticalanalysisconsulting DOT com/
I am trying to model grain quality parameters like protein and oil content. The dependent variables are fertilizer and management practices.
I did look at the residuals and it show some kind of pattern.
I didn't look up at GLIMMMIX and NLMIXED.