[R] intercept value in lme

[Andrew Robinson]

Hello Victor,

I'm afraid that this still isn't what we're looking for, in terms of
reproducible code, but we can guess.  What is the range of the 
Z1 and Z2 variables?  What is the range of the model predictions? 
If the Z1 and Z2 variables are large and positive then they will be
compensating.

Cheers

Andrew

On Wed, Dec 06, 2006 at 06:06:55PM +0100, victor wrote:
> It is boundend, you're right. In fact it is -25<=X<=0
> 
> These are cross-national survey data (I was investigated 7 countries in 
> each country there was 900-1700 cases).
> In fact, there was two level 2 variables, so:
> 
> m1<-lme(X~Y,~1|group,data=data,na.action=na.exclude,method="ML")
> m2<-lme(X~Y+Z1+Z2,~1|group,data=data,na.action=na.exclude,method="ML")
> 
> X is a life satisfaction factor combined from 2 other variables for each 
> case separately, of course.
> Y  - income per capita in household
> Z1 - unemployment rate in a country.
> Z2 - life expectancy in a country
> group - country
> 
> I attach a similar model where after adding Lev2 predictors intercept 
> value is even 22!
> 
> I'm sure there is my mistake somwhere but... what is wrong?
> 
> 
> 
> Linear mixed-effects model fit by maximum likelihood
>   Data: data
>         AIC      BIC    logLik
>    31140.77 31167.54 -15566.39
> 
> Random effects:
>   Formula: ~1 | country
>          (Intercept) Residual
> StdDev:   0.8698037 3.300206
> 
> Fixed effects: X ~ Y
>                  Value Std.Error   DF    t-value p-value
> (Intercept) -4.397051 0.3345368 5944 -13.143698       0
> Y           -0.000438 0.0000521 5944  -8.399448       0
>   Correlation:
>          (Intr)
> Y       -0.13
> 
> Standardized Within-Group Residuals:
>         Min         Q1        Med         Q3        Max
> -6.3855881 -0.5223116  0.2948941  0.6250717  2.6020180
> 
> Number of Observations: 5952
> Number of Groups: 7
> 
> 
> and for the second model:
> 
> Linear mixed-effects model fit by maximum likelihood
>   Data: data
>         AIC      BIC    logLik
>    31133.08 31173.23 -15560.54
> 
> Random effects:
>   Formula: ~1 | country
>          (Intercept) Residual
> StdDev:   0.3631184 3.300201
> 
> Fixed effects: X ~ Y + Z1 + Z2
>                  Value Std.Error   DF   t-value p-value
> (Intercept) 22.188828  4.912214 5944  4.517073  0.0000
> Y           -0.000440  0.000052 5944 -8.456196  0.0000
> Z1          -0.095532  0.037520    4 -2.546161  0.0636
> Z2          -0.333549  0.062031    4 -5.377127  0.0058
>   Correlation:
>          (Intr) FAMPEC UNEMP
> Y        0.168
> Z1      -0.429  0.080
> Z2      -0.997 -0.188  0.366
> 
> Standardized Within-Group Residuals:
>         Min         Q1        Med         Q3        Max
> -6.3778888 -0.5291287  0.2963226  0.6260023  2.6226880
> 
> Number of Observations: 5952
> Number of Groups: 7
> 
> Doran, Harold wrote:
> > As Andrew noted, you need to provide more information. But, what I see
> > is that your model assumes X is continuous but you say it is bounded,
> > -25 < X < 0 
> > 
> >> -----Original Message-----
> >> From: r-help-bounces at stat.math.ethz.ch 
> >> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of victor
> >> Sent: Wednesday, December 06, 2006 3:34 AM
> >> To: r-help at stat.math.ethz.ch
> >> Subject: [R] intercept value in lme
> >>
> >> Dear all,
> >>
> >> I've got a problem in fitting multilevel model in lme. I 
> >> don't know to much about that but suspect that something is 
> >> wrong with my model.
> >>
> >> I'm trying to fit:
> >>
> >> m1<-lme(X~Y,~1|group,data=data,na.action=na.exclude,method="ML")
> >> m2<-lme(X~Y+Z,~1|group,data=data,na.action=na.exclude,method="ML")
> >>
> >> where:
> >> X - dependent var. measured on a scale ranging from -25 to 0 
> >> Y - level 1 variable Z - level 1 variable
> >>
> >> In m1 the intercept value is equal -3, in m2 (that is after 
> >> adding Lev 2
> >> var.) is equal +16.
> >>
> >> What can be wrong with my variables? Is this possible that 
> >> intercept value exceeds scale?
> >>
> >> Best regards,
> >>
> >> victor
> >>
> >> ______________________________________________
> >> R-help at stat.math.ethz.ch mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide 
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >>
> >
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

-- 
Andrew Robinson  
Department of Mathematics and Statistics            Tel: +61-3-8344-9763
University of Melbourne, VIC 3010 Australia         Fax: +61-3-8344-4599
http://www.ms.unimelb.edu.au/~andrewpr
http://blogs.mbs.edu/fishing-in-the-bay/