mlogit {nspmix} | R Documentation |
Class ‘mlogit’
Description
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.
Usage
mlogit(x)
rmlogit(k, gi=2, ni=2, pt=0, pr=1, beta=1, X)
Arguments
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. |
Details
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
.
Author(s)
Yong Wang <yongwang@auckland.ac.nz>
References
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.
See Also
Examples
x = rmlogit(k=30, gi=3:5, ni=6:10, pt=c(0,4), pr=c(0.7,0.3),
beta=c(0,3))
cnmms(x)
### Real-world data
# Random intercept logistic model
data(toxo)
cnmms(mlogit(toxo))
data(betablockers)
cnmms(mlogit(betablockers))
data(lungcancer)
cnmms(mlogit(lungcancer))