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: [EMAIL PROTECTED]
>> [mailto:[EMAIL PROTECTED] On Behalf Of victor
>> Sent: Wednesday, December 06, 2006 3:34 AM
>> To: r-help@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@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.
>>
>
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