Re: LME- log-normal distribution of parameters

[david.thompson@mnr.gov.on.ca]

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

 

DaveT.

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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
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-----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