Re: LME- log-normal distribution of parameters

["David Parkhurst" <parkhurs@ariel.ucs.indiana.edu>]

You also want to be very careful, when transforming data to logs, that the logarithms make sense scientifically.  In situations involving amounts of mass or energy, they often don't make sense.  There is a law of conservation of mass, but no such law for logarithms of mass.  Next time you reconcile your monthly checkbook statement, try converting all amounts to logs first---things just won't add up.  So think this through for your own data.

 

Dave Parkhurst

----- Original Message -----

From: Pravin

To: david.thompson@mnr.gov.on.ca ; s-news@wubios.wustl.edu

Sent: Friday, March 26, 2004 11:36 AM

Subject: Re: [S] LME- log-normal distribution of parameters

log(predictor+0.0001) would be the simplest and intuitive solution. But for some reason I need to do better than that.

 

Looks like glme() in the library(correlatedData) offers such flexibility but my experience in this field is almost equal to none. So looks like some background work is needed before implementation.

 

Thanks for all the help. 

 

Thanks,

Pravin

Pravin Jadhav

-----Original Message-----
From: david.thompson@mnr.gov.on.ca [mailto:david.thompson@mnr.gov.on.ca]
Sent: Friday, March 26, 2004 11:21 AM
To: jadhavpr@vcu.edu; s-news@wubios.wustl.edu
Cc: slarsen@insightful.com
Subject: RE: LME- log-normal distribution of parameters

Does log(predictor+1) seem a reasonable alternative?

 

DaveT.

**********************************************************
David J. Thompson
Silviculture Data Analyst
Ontario Forest Research Institute
Ontario Ministry of Natural Resources
1235 Queen Street East
Sault Ste. Marie, Ontario, P6A 2E5

(705) 946-7433
(705) 946-2030 Fax
david.thompson@mnr.gov.on.ca
**********************************************************
 

-----Original Message-----
From: Pravin [mailto:jadhavpr@vcu.edu]
Sent: March 25, 2004 6:42 PM
To: s-news@wubios.wustl.edu
Cc: slarsen@insightful.com
Subject: LME- log-normal distribution of parameters

Hello,

>lme(response ~ time, data="" random = ~ 1+time|ID)
##"time" is used a predictor for the "response" in the data frame "data.g"
##Random regression intercepts and slope on "time"
##Random effects vary over  individual ID

The above model assumes that the intercept and the slope are normally distributed. How can I specify log-normal distribution for these parameters. But the residual error can be normally distributed-- that is fine. One suggestion was to use log transformation. But I cannot use log transformation in the fixed effects model (log(response)~log(time)) because there are a few 0's in the predictor column.

Thank you,

Pravin

Pravin Jadhav