Although it's pragmatic, this approach leads to a solution that depends on the
magnitude of the correction. Why, after all, use log(predictor+1)? Why not
log(predictor+0.0001)? The log transform makes no sense when there are zeros
unless you're sure that the zeros are due to some measurement error.
Another, less arbitrary, avenue to explore is the so-called hurdle or two-step
models.
Andrew
On Friday 26 March 2004 08:20, david.thompson@mnr.gov.on.ca wrote:
> 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=data.g, 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
--
Andrew Robinson Ph: 208 885 7115
Department of Forest Resources Fa: 208 885 6226
University of Idaho E : andrewr@uidaho.edu
PO Box 441133 W : http://www.uidaho.edu/~andrewr
Moscow ID 83843 Or: http://www.biometrics.uidaho.edu
No statement above necessarily represents my employer's opinion.
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