This class contains all the input parameters to run CLERE.
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[numeric]: The vector of observed responses.
[matrix]: The matrix of predictors.
[integer]: The sample size or the number of rows in matrix x.
[integer]: The number of variables of the number of columns in matrix x.
[integer]: The number or the maximum number of groups considered. Maximum number of groups stands when model selection is required.
[numeric]: Number of Gibbs iterations to generate the partitions.
[numeric]: Number of SEM/MCEM iterations.
[numeric]: Number of SEM iterations discarded before calculating the MLE which is averaged over SEM draws.
[numeric]: Number of iterations between sampled partitions when calculating the likelihood at the end of the run.
[numeric]: Number of sampled partitions for calculating the likelihood at the end of the run.
[logical]: Should a
0 class be imposed to the model?
[character]: Which analysis is to be performed. Values are
[character]: The algorithmto 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.
[logical]: Is set to TRUE when an initial partition and an initial vector of parameters is given by the user.
[numeric]: An EM algorithm is used inside the SEM to maximize the complete log-likelihood
maxit stands as the maximum number of EM iterations for the internal EM.
[numeric]: Maximum increased in complete log-likelihood for the internal EM (stopping criterion).
[integer]: An integer given as a seed for random number generation. If set to
NULL, then a random seed is generated between
[numeric]: Vector of parameter b. Its size equals the number of group(s).
[numeric]: Vector of parameter pi. Its size equals the number of group(s).
[numeric]: Parameter sigma^2.
[numeric]: Parameter gamma^2.
itemintercept[numeric]: Parameter beta_0 (intercept).
[numeric]: Approximated log-likelihood.
[numeric]: Approximated entropy.
p x g matrix of posterior probability of membership to the groups.
P = E[Z|theta].
nItEM x (2g+4) matrix containing values of the model parameters and complete data likelihood at each iteration of the SEM/MCEM algorithm
p x nsamp matrix which columns are samples from the posterior distribution of Beta (regression coefficients) given the data and the maximum likelihood estimates.
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.
2g+3 length vector containing initial guess of the model parameters. See example for function
p x 1 vector of integers taking values between 1 and
p (number of variables).
Get the value of the field
value to the field
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.
summarizes the output of function
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