Description Usage Arguments Details Author(s) References See Also Examples
These functions can be used to fit a binomial logistic regression model that has a random intercept to clustered observations. Observations in each cluster are assumed to have the same intercept, while different clusters may have different intercepts. This is a mixed-effects problem.
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x |
a numeric matrix with four or more columns that stores clustered data. |
k |
the number of groups or clusters. |
gi |
a numeric vector that gives the sample size in each group. |
ni |
a numeric vector for the number of Bernoulli trials for each observation. |
pt |
a numeric vector for all the support points. |
pr |
a numeric vector for all the probabilities associated with the support points. |
beta |
a numeric vector for the fixed coefficients of the covariates of the observation. |
X |
the numeric matrix as the design matrix. If missing, a random matrix is created from a normal distribution. |
Class mlogit
is used to store data for fitting the binomial logistic
regression model with a random intercept.
Function mlogit
creates an object of class mlogit
, given a
matrix with four or more columns that stores, respectively, the
group/cluster membership (column 1), the number of ones or successes in the
Bernoulli trials (column 2), the number of the Bernoulli trials (column 3),
and the covariates (columns 4+).
Function rmlogit
generates a random sample that is saved as an object
of class mlogit
.
An object of class mlogit
contains a matrix with four or more
columns, that stores, respectively, the group/cluster membership (column 1),
the number of ones or successes in the Bernoulli trials (column 2), the
number of the Bernoulli trials (column 3), and the covariates (columns 4+).
It also has two additional attributes that facilitate the computing by
function cmmms
. The first attribute is ui
, which stores the
unique values of group memberships, and the second is gi
, the number
of observations in each unique group.
It is convenient to use function mlogit
to create an object of class
mlogit
.
Yong Wang <yongwang@auckland.ac.nz>
Kiefer, J. and Wolfowitz, J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. Ann. Math. Stat., 27, 886-906.
Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric mixture models. Statistics and Computing, 20, 75-86.
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