binnest {repeated} | R Documentation |
Binary Random Effects Models with Two Levels of Nesting
Description
binnest
is designed to handle binary and binomial data with two
levels of nesting. The first level is the individual and the second will
consist of clusters within individuals.
Usage
binnest(
response,
totals = NULL,
nest = NULL,
ccov = NULL,
tvcov = NULL,
mu = ~1,
re1 = ~1,
re2 = ~1,
preg = NULL,
pre1 = NULL,
pre2 = NULL,
binom.mix = c(10, 10),
binom.prob = c(0.5, 0.5),
fcalls = 900,
eps = 0.01,
print.level = 0
)
Arguments
response |
A list of three column matrices with counts, corresponding
totals (not necessary if the response is binary), and (second-level)
nesting indicator for each individual, one matrix or dataframe of such
counts, or an object of class, response (created by
|
totals |
If |
nest |
If |
ccov |
If |
tvcov |
If |
mu |
If |
re1 |
If |
re2 |
If |
preg |
Initial parameter estimates for the fixed effect regression
model: either the model specified by |
pre1 |
Initial parameter estimates for the first level of nesting
variance model: either the model specified by |
pre2 |
Initial parameter estimates for the second level of nesting
variance model: either the model specified by |
binom.mix |
A vector of two values giving the totals for the binomial distributions used as the mixing distributions at the two levels of nesting. |
binom.prob |
A vector of two values giving the probabilities in the binomial distributions used as the mixing distributions at the two levels of nesting. If they are 0.5, the mixing distributions approximate normal mixing distributions; otherwise, they are skewed. |
fcalls |
Number of function calls allowed. |
eps |
Convergence criterion. |
print.level |
If 1, the iterations are printed out. |
Details
The variance components at the two levels can only depend on the covariates
if response
has class, repeated
.
Value
A list of classes binnest
is returned.
Author(s)
T.R. Ten Have and J.K. Lindsey
References
Ten Have, T.R., Kunselman, A.R., and Tran, L. (1999) Statistics in Medicine 18, 947-960.
See Also
gar
, read.list
,
restovec
, rmna
,
tcctomat
, tvctomat
.
Examples
#y <- rbind(matrix(rbinom(20,1,0.6), ncol=4),
# matrix(rbinom(20,1,0.4), ncol=4))
y <- matrix(c(1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,1,0,1,0,
1,1,1,1,1,1,1,1,0,1,1,0),nrow=10,ncol=4,byrow=TRUE)
resp <- restovec(y, nest=1:4, times=FALSE)
ccov <- tcctomat(c(rep(0,5),rep(1,5)), name="treatment")
reps <- rmna(resp, ccov=ccov)
# two random effects
binnest(reps, mu=~treatment, preg=c(1,1), pre1=2, pre2=2)
# first level random effect only
binnest(reps, mu=~treatment, preg=c(1,-1), pre1=1)
# second level random effect only
binnest(reps, mu=~treatment, preg=c(1,-1), pre2=1)