Perform conditional logistic regression with output formatted for input into saws which
will give confidence intervals and p-values.
Usage
clogistCalc(n, m, x, set, initb = NA, h = 1e-04, maxitr = 15,
epsilon = 1e-08, conf.level = 0.95)
clogistLoglike(n, m, x, beta)
clogistInfo(n, m, x, beta, h)
Arguments
n
vector of number at risk
m
vector of number of events
x
matrix of covariates
set
vector of denoting clusters
initb
vector of initial parameter estimates, initb=NA uses unconditional logistic regression for initial estimate
h
small value for numeric integration
maxitr
maximum number of iterations
epsilon
convergence criteria (see details)
conf.level
confidence level for confidence intervals
beta
vector of current parameter estimate
Details
The main program is clogistCalc. It calls clogistLoglike and
clogistInfo which are not to be called explicitly. The function
clogistLoglike finds the loglikelihood using recursive methods,
and clogistInfo calculates score vector and information
matrix using numerical methods. Both methods are described in Gail, Lubin and Rubinstein (1981), and the h value is
the same as is defined in that paper.
The algorithm stops when the largest absolute relative change in either the loglikelihood or in any parameter
is less than epsilon. For parameters close to zero (i.e., less than 0.01 in absolute value) the relative change
is defined as change/0.01.
Value
A list for input into the saws function, containing
the following elements (K=number of clusters, p=number of parameters):
coefficients
p by 1 vector of parameter estimates
u
K by p matrix of scores or estimating equations
omega
K by p by p array of -1*information
Author(s)
Michael Fay, modeled after a Fortran program by Doug Midthune
References
Gail, Lubin and Rubinstein (1981) Biometrika, 703-707
See Also
See also saws
Examples
data(micefat)
cout<-clogistCalc(micefat$N,micefat$NTUM,micefat[,c("fatCal","totalCal")],micefat$cluster)
## usual model based variance
saws(cout,method="dm")
## sandwich based variance with small sample correction
s3<-saws(cout,method="d3")
s3
print.default(s3)