[R] intercept value in lme

[Doran, Harold]

Dear Victor:

Firstly, why do you think something is wrong? Ignoring the fact that
your DV is not continuous for a moment and your distributional
assumptions assume it is, could it not be the case that conditional on
your covariates the changes in the intercept are correct?

I might be missing something, but to me it seems that you are concluding
that something is wrong because of the vast changes in the intercept. As
far as I can see in this thread so far we still do not know anything
about the covariates that could help diagnose the issue.

Syntactically, your lme model is correct (although you should switch to
lmer which is more supported), but you might consider a transformation
of you DV (e.g., log) to better coincide with your distributional
assumptions.

> -----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 12:07 PM
> To: Doran, Harold
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] intercept value in lme
> 
> 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
> >>
> >> ______________________________________________
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> >> PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >>
> >
> 
> ______________________________________________
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>