This contains further description of the output object created by a
coxme
call. Most components can be accessed with extractor
functions, which is the safer route since details of the object will
likely change over time.
The structure of each element of the random effects
coefficients (obtained with ranef
) and variances
(VarCorr
) depend on the variance functions, i.e., the functions
used in the varlist
argument.
Since users can write their own variance functions this format can
never be completely known.
coefficients 
the coefficients of the fixed effects. Use the

frail 
the coefficients of the random effects. Use the

vcoef 
the variances of the random effects. Use the

variance 
the variancecovariance matrix of the coefficient
vector, including both fixed and random terms. The random effects
are listed first. This will often be a sparse matrix.
The 
loglik 
the loglikelihood vector from the fit. The first element is the loglik at the initial values, the second is the integrated partial likelihood at the solution (IPL), the third is the penalized partial likelihood at the solution(PPL). 
df 
degrees of freedom for the IPL and the PPL solutions. 
hmat 
sparse Cholesky factorization of the information matrix. 
iter 
outer and inner iterations performed. For each trial value of the variance parameters an Cox model partial likelihood must be solved; the outer iterations is the reported number from the optim() routine which handles the variance parameters, the inner iterations is the cumulative number of partial likelihood iterations. 
control 
a copy of the 
ties 
the computational method used for ties. 
u 
the vector of first derivatives of the PPL, at the solution. 
means,scale 
means and scale for each predictor, used internally to scale the problem. 
linear.predictor 
the vector of linear predictors. 
n 
vector containing the number of events and the number of observations in the fitting data set. 
terms 
the terms object from the fixed effects of the model
formula. Access using the 
formulaList 
the fixed and random portions of the formula, separated 
na.action 
the missing value attributes of the data, if any 
x,y,model 
optional: the x matrix, response, for model frame. These depend on the corresponding arguments in the call. 
call 
a copy of the call to the routine 
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