intercept value in lme

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