Re: LME- log-normal distribution of parameters

["Pravin" <jadhavpr@vcu.edu>]

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.

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David J. Thompson
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Ontario Forest Research Institute
Ontario Ministry of Natural Resources
1235 Queen Street East
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(705) 946-7433
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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