fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
summary(fm1)
summary(fm2)
intervals(fm1)
intervals(fm2)
anova(fm1, fm2)
VarCorr(fm1)
VarCorr(fm2)hope this helps. spencer graves
[EMAIL PROTECTED] wrote:
Dear R users!
I used lme to fit a mixed model with random intercept and spatial Gaussian correlation i.e. I fitted a model of the following form:
Y = X*beta + error
and
error = U + W(t) + Z
where U is the random intercept (normally distributed), W(t) the stationary Gaussian process and Z also a normally distributed (the residual) rv. Each of these three random variables have a variance which I am not sure to which output in lme they belong to. VarCorr gives the intercept and residual variance which I assume belong to U and Z respectively. The output of lme gives another estimate called "range" which I assume belongs to the parameter estimate needed for the Gaussian correlation.
Are my assumptions correct? And where can I get the variance for the W(t) from?
Thanks for any answers...and happy new year...
Hadassa
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