Clere-class: 'Clere' class
In clere: Simultaneous Variables Clustering and Regression

Description Usage Details Methods See Also

This class contains all the input parameters to run CLERE.

## S4 method for signature 'Clere'
show(object)

## S4 method for signature 'Clere,ANY,ANY,ANY'
x[i, j, drop]

## S4 method for signature 'sClere'
show(object)

## S4 method for signature 'sClere,ANY,ANY,ANY'
x[i, j, drop]

## S4 replacement method for signature 'sClere,ANY,ANY,ANY'
x[i, j] <- value

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 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.
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).
sigma2: [numeric]: Parameter sigma^2.
gamma2: [numeric]: Parameter gamma^2.

itemintercept[numeric]: Parameter beta_0 (intercept).

likelihood: [numeric]: Approximated log-likelihood.
entropy: [numeric]: Approximated entropy.
P: [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 2g+3 length vector 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).

object["slotName"]:: Get the value of the field slotName.
object["slotName"]<-value:: Set value to the field slotName.
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.
summary(object, ...):: summarizes the output of function fitClere.