Description Usage Details Methods See Also

This class contains all the input parameters to run CLERE.

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- 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`

.

Overview : `clere-package`

Classes : `Clere`

Methods : `plot`

, `clusters`

, `predict`

, `summary`

Functions : `fitClere`

Datasets : `numExpRealData`

, `numExpSimData`

, `algoComp`

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