Description Usage Arguments Details Value References Examples

Set control parameters for graphical model estimation, graph structure search via stepwise or genetic algorithm, mixture model fitting via structural-EM algorithm, and Bayesian regularization.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
ctrlICF(tol = 1e-04, maxiter = 1e03)
ctrlSTEP(occamAdd = Inf, occamRem = Inf, start = NULL)
ctrlGA(popSize = 50, pcrossover = 0.8, pmutation = 0.1,
maxiter = 100, run = maxiter/2,
elitism = base::max(1, round(popSize*0.05)))
ctrlEM(tol = 1e-05, maxiter = 1e02, subset = NULL, printMsg = FALSE)
ctrlREG(data, K, scaleType = c("full", "fixed", "one", "diag"),
scale = NULL, psi = NULL)
``` |

`tol` |
Tolerance value for judging when convergence has been reached. Used in estimation of a Gaussian graphical model and in the structural-EM algorithm. |

`maxiter` |
Maximum number of iterations in the Gaussian graphical model estimation algorithm, the genetic algorithm for structure search, and the structural-EM algorithm. |

`occamAdd, occamRem` |
Set the bounds of the Occam's window for stepwise search. See "Details". Default is |

`start` |
Provide in input a user-defined starting adjacency matrix for stepwise structure search. |

`popSize` |
Population size. This number corresponds to the number of different graph structures to be considered at each iteration of the genetic algorithm. |

`run` |
Number of consecutive generations without any improvement in the best fitness value of the structure search procedure before the genetic algorithm is stopped. |

`pcrossover` |
Probability of crossover between pairs of binary adjacency matrices. |

`pmutation` |
Probability of mutation in a parent adjacency matrix. |

`elitism` |
Number of best fitness graph structures to survive at each iteration of the genetic algorithm in the graph structure search procedure. |

`subset` |
A logical or numeric vector specifying a subset of the data to be used in the initial hierarchical clustering phase employed in the initialization of the structural-EM algorithm. By default no subset is used. |

`printMsg` |
A logical value indicating whether or not certain warnings (usually related to singularity) should be issued. |

`data` |
A matrix or data frame of observations. Categorical variables are not allowed. Rows correspond to observations and columns correspond to variables. |

`K` |
The number of mixture components. |

`scaleType` |
The type of scale hyperparameter for the prior on the covariance matrix in the case of Bayesian regularization for Gaussian covariance graph model. See "Details". Default is |

`scale` |
The scale hyperparameter for the prior on the covariance matrix in the case of Bayesian regularization for Gaussian covariance graph model. |

`psi` |
The degrees of freedom hyperparameter for the prior on the covariance matrix in the case of Bayesian regularization for Gaussian covariance graph model. |

Function `ctrlICF`

is used to set control parameters of the algorithms employed to estimate a Gaussian covariance or concentration graph model.

Function `ctrlSTEP`

mainly controls the Occam's window used in the stepwise graph structure search. Default is `Inf`

, corresponding to no Occam's window reduction is implemented. The rationale of the Occam's window is too reduce the space of candidate adjacency matrices during the search by discarding those with a value of the penalized objective function that is too distant from the current optimal value. Graph candidate structures whose penalized objective function value is within `occamAdd`

from the current optimal value are considered in the next edge-add step of the search. Likewise, graph candidate structures whose penalized objective function value is within `occamRem`

from the current optimal value are considered in the next edge-remove step of the search. Small values for `occamRem`

and `occamAdd`

significantly reduce the space of candidate solutions and the computational cost of the greedy search.

Function `ctrlGA`

sets parameters of the genetic algorithm used for graph structure search. Arguments correspond to those of function `ga`

in the `GA`

package.

Function `ctrlEM`

controls standard parameters of the structural-EM algorithm.

Function `ctrlREG`

is used to set hyperparameters of the conjugate prior on the covariance matrix for Bayesian regularization in Gaussian covariance graph models. The function creates a list of hyperparameters to be given in input in argument `regHyperPar`

of functions `fitGGM`

, `searchGGM`

and `mixGGM`

.
If not provided in input, the scale hyperparameter is computed on the data via the sample covariance matrix according to the argument `scaleType`

. If `scaleType = "full"`

, the scale matrix is proportional to the data covariance matrix; if `scaleType = "fixed"`

, the scale matrix has determinant equal to *(0.001/K)^(1/V)* (Baudry, Celeux, 2015; Fop et al. 2018); if `scaleType = "one"`

the scale matrix has determinant 1; if `scaleType = "diag"`

the scale matrix is diagonal. Note that in the case `V > N`

, if not provided in input the scale matrix is forced to be diagonal.
The hyperparameter `psi`

controlling the degrees of freedom is set to `V + 2`

by default.

A list of parameters values.

Baudry, J.P. and Celeux, G. (2015). EM for mixtures: Initialization requires special care. *Statistics and Computing*, 25(4):713-726.

Fop, M., Murphy, T.B., and Scrucca, L. (2018). Model-based clustering with sparse covariance matrices. *Statistics and Computing*. To appear.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ```
## Not run:
# ga search with increased mutation probability
data(banknote, package = "mclust")
mod1 <- searchGGM(banknote[,-1], model = "concentration", search = "ga",
ctrlGa = ctrlGA(pmutation = 0.3))
# regularization
library(MASS)
V <- 10
N <- 20
mu <- rep(0, V)
sigma <- matrix(0.9, V,V)
diag(sigma) <- 1
x <- cbind( MASS::mvrnorm(N, mu, sigma),
MASS::mvrnorm(N, mu, sigma),
MASS::mvrnorm(N, mu, sigma)) # high-dimensional data V = 30, N = 20
#
hyperPar <- ctrlREG(x, K = 1, scaleType = "diag")
mod2 <- searchGGM(x, model = "covariance", penalty = "ebic") # throws an error
mod2 <- searchGGM(x, model = "covariance", penalty = "ebic", # regularization
regularize = TRUE, regHyperPar = hyperPar)
plot(mod2, "adjacency")
# occam's window
library(MASS)
V <- 20
N <- 500
mu <- rep(0, V)
sigma <- matrix(0.9, V,V)
diag(sigma) <- 1
edges <- rbinom(choose(V,2), 1, 0.3)
A <- matrix(0, V,V)
A[lower.tri(A)] <- edges
A <- A + t(A)
fit <- fitGGM(S = sigma, N = N, graph = A, model = "concentration",
ctrlIcf = ctrlICF(tol = 1e-06))
sigma <- fit$sigma
#
x <- MASS:::mvrnorm(N, mu, sigma)
#
mod3 <- searchGGM(x, model = "concentration", search = "step-back",
ctrlStep = ctrlSTEP(occamAdd = 5, occamRem = 5))
par(mfrow = c(1,2))
plot(fit, what = "adjacency")
plot(mod3, what = "adjacency")
## End(Not run)
``` |

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