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