[R] intercept value in lme

[victor]

Thanks to all of you!
Yes, you're right - I didn't take into consideration the ranges of 
predicors which are quite large. I think the matter over and realize 
that my assumption that something have to be wrong doesn't have in fact 
any reason except "strange" look of the value.
Centering helped (as suggested by Chuck) especially in interpretation 
and helped me to understand what is really going on in the model.

Thank you once again - these are my first experiences with R as like as 
with multilevel models, so... thank you for your patience!

Best regards,

victor

Chuck Cleland wrote:
> 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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>>>> 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.
>>
>