condGEE {condGEE} | R Documentation |
Solves for the mean parameters (\theta
), the variance parameter (\sigma^2
), and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement, D. Y. and Strawderman, R. L. (2009) Biostatistics 10, 451–467. Makes a parametric assumption for the length of the censored gap time, and assumes gap times within subject are conditionally uncorrelated.
condGEE(data, start, mu.fn=MU, mu.d=MU.d, var.fn=V,
k1=K1.norm, k2=K2.norm, robust=TRUE, asymp.var=TRUE,
maxiter=100, rtol=1e-6, atol=1e-8, ctol=1e-8, useFortran=TRUE)
data |
matrix of data with one row for each gap time; the first column should be a subject ID, the second column the gap time, the third column a completeness indicator equal to 1 if the gap time is complete and 0 if the gap time is censored, and the remaining columns the covariates for use in the mean and variance functions |
start |
vector containing initial guesses for the unknown parameter vector |
mu.fn |
the specification for the mean of the gap time; the default is a linear combination of the covariates; the function should take two arguments ( |
mu.d |
the derivative of |
var.fn |
the specification for |
k1 |
the function to solve for the conditional mean length of the censored gap times; its sole argument should be the vector of standardized (i.e.\ |
k2 |
the function to solve for the conditional mean length of the square of the censored gap times; its sole argument should be the vector of standardized (i.e.\ |
robust |
logical, if |
asymp.var |
logical, if |
maxiter |
see |
rtol |
see |
atol |
see |
ctol |
see |
useFortran |
see |
Uses the function multiroot
in the rootSolve
package to solve the conditional GEE. As in multiroot
, there is no guarantee of finding the root.
A monotone increasing transformation can be applied to the observed gap times before calling condGEE
.
When robust=TRUE
, \theta
and \sigma^2
are solved for in an alternating fashion until convergence. Note that the estimating equation for the mean parameters depends on \sigma^2
through the censored gap time.
a list containing:
eta |
the parameter estimate |
a.var |
an estimate of the asymptotic variance matrix of the eta estimator |
David Clement <dyc24@cornell.edu>
Clement, D. Y. and Strawderman, R. L. 2009 Biostatistics 10, 451–467.
data(asthma)
demo(asthmaExample)