This class contains all the input parameters to run CLERE.
Details
y
[numeric]: The vector of observed responses.
x
[matrix]: The matrix of predictors.
n
[integer]: The sample size or the number of rows in
matrix x.
p
[integer]: The number of variables of the number of
columns in matrix x.
g
[integer]: The number or the maximum number of groups considered. Maximum number of groups stands when model selection is required.
nItMC
[numeric]: Number of Gibbs iterations to generate the partitions.
nItEM
[numeric]: Number of SEM/MCEM iterations.
nBurn
[numeric]: Number of SEM iterations discarded before calculating the MLE which is averaged over SEM draws.
dp
[numeric]: Number of iterations between sampled partitions when calculating the likelihood at the end of the run.
nsamp
[numeric]: Number of sampled partitions for calculating the likelihood at the end of the run.
sparse
[logical]: Should a 0 class be imposed to the model?
analysis
[character]: Which analysis is to be performed. Values are "fit", "bic", "aic" and "icl".
algorithm
[character]: The algorithm to be chosen to fit
the model. Either the SEM-Gibbs algorithm or the MCEM
algorithm. The most efficient algorithm being the SEM-Gibbs
approach. MCEM is not available for binary response.
initialized
[logical]: Is set to TRUE when an initial
partition and an initial vector of parameters is given by the user.
maxit
[numeric]: An EM algorithm is used inside the SEM
to maximize the complete log-likelihood p(y,Z|theta). maxit stands as the maximum number of EM
iterations for the internal EM.
tol
[numeric]: Maximum increased in complete
log-likelihood for the internal EM (stopping criterion).
seed
[integer]: An integer given as a seed for random
number generation. If set to NULL, then a random seed
is generated between 1 and 1000.
b
[numeric]: Vector of parameter b. Its size equals the number of group(s).
pi
[numeric]: Vector of parameter pi. Its size equals the number of group(s).
[matrix]: A [p x g] matrix of posterior probability of membership
to the groups. P = E[Z|theta].
theta
[matrix]: A [nItEM x (2g+4)] matrix containing values
of the model parameters and complete data likelihood at each
iteration of the SEM/MCEM algorithm
Bw
[matrix]: A [p x nsamp] matrix which columns are samples
from the posterior distribution of Beta (regression coefficients) given the data and the maximum likelihood estimates.
Zw
[matrix]: A [p x nsamp] matrix which columns are samples from the posterior distribution of Z (groups membership indicators)
given the data and the maximum likelihood estimates.
theta0
[numeric]: A vector size [2g+3] containing initial guess of the model parameters. See example for function fitClere.
Z0
[numeric]: A [p x 1] vector of integers taking values between 1 and p (number of variables).
Methods
object["slotName"]:
Get the value of the field slotName.
object["slotName"]<-value:
Set value to the field slotName.
show(object):
Returns the formatted values of Clere object.
plot(x, ...):
Graphical summary for MCEM/SEM-Gibbs estimation.
clusters(object, threshold = NULL, ...):
Returns the estimated clustering of variables.
predict(object, newx, ...):
Returns prediction using a fitted model and a new matrix of design.