[R] intercept value in lme

[Chuck Cleland]

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

Victor:
  What happens if you center Y, Z1, and Z2 so that 0 corresponds to the
mean for each?  As it is, zero is a very unusual value for each of these
variables.  Do you really want to estimate the value of X when income =
0, unemployment = 0, and life expectancy = 0?  If I understand
correctly, I think that's why the intercept value looks unusual to you.

> 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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> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
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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.
> 

-- 
Chuck Cleland, Ph.D.
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