Significance of fixed effects. Kinship package (Marc Moragues)
Witold Proskura <witoldproskura at ...> writes:
Dear R Support Team,
(Sadly there is no "R Support Team" -- just the people who read the mailing list.)
I have tried to compare the log-likelihoods of the two models, with and without the fixed effect which I want to test significance for.
[snip]
Moreover please indicate some method for multiple comparisons of groups within particular fixed effect. When I tried to obtain df value for the model, the result was always NULL. The last question is how to extract variation components and to calculate heritability?
We really need a reproducible example before we can help.
What package is this from? kinship (which is now obsolete),
or coxme?
My only thought is that you have 'loglik' component that you
can probably extract via m1$loglik, and you can do something like
pchisq(-2*(diff. in log-likelihoods),df=(diff. in number of coefficients),
lower.tail=FALSE)
to implement your own likelihood ratio test.
My model: m1=lmekin(kgm~s1+rs+w+d+(1|id), data=milk,varlist=list(kmat)) ,where: s1 and rs are categorical factors w and d are continuous variables
summary(m1)
Length Class Mode coefficients 2 -none- list var 576 -none- numeric vcoef 1 -none- list residuals 424 -none- numeric method 1 -none- character loglik 1 -none- numeric
[snip]
Linear mixed-effects kinship model fit by maximum likelihood
Data: milk
Log-likelihood = -3604.361
n= 424
Model: kgm ~ s1 + rs + w + d + (1 | id)
Fixed coefficients
Value Std Error z p
(Intercept) -7920.701534 3023.8600619 -2.62 8.8e-03
s1CT 76.794638 170.4547258 0.45 6.5e-01
d 41.339062 9.9779674 4.14 3.4e-05
[SNIP]
Random effects Group Variable Std Dev Variance id Vmat.1 1210.785 1465999.783 Residual error= 864.8264