super class for PLNPCAfamily and PLNnetworkfamily.
responsesthe matrix of responses common to every models
covariatesthe matrix of covariates common to every models
offsetsthe matrix of offsets common to every models
weightsthe vector of observation weights
inceptiona PLNfit object, obtained when no sparsifying penalty is applied.
modelsa list of PLNfit object, one per penalty.
criteriaa data frame with the values of some criteria (approximated log-likelihood, BIC, ICL, etc.) for the collection of models / fits BIC and ICL are defined so that they are on the same scale as the model log-likelihood, i.e. with the form, loglik - 0.5 penalty
convergencesends back a data frame with some convergence diagnostics associated with the optimization process (method, optimal value, etc)
new()Create a new PLNfamily object.
PLNfamily$new(responses, covariates, offsets, weights, control)responsesthe matrix of responses common to every models
covariatesthe matrix of covariates common to every models
offsetsthe matrix of offsets common to every models
weightsthe vector of observation weights
controla list for controlling the optimization. See details.
A new PLNfamily object
postTreatment()Update fields after optimization
PLNfamily$postTreatment()
getModel()Extract a model from a collection of models
PLNfamily$getModel(var, index = NULL)varvalue of the parameter (rank for PLNPCA, sparsity for PLNnetwork) that identifies the model to be extracted from the collection. If no exact match is found, the model with closest parameter value is returned with a warning.
indexInteger index of the model to be returned. Only the first value is taken into account.
A PLNfit object
plot()Lineplot of selected criteria for all models in the collection
PLNfamily$plot(criteria, reverse)criteriaA valid model selection criteria for the collection of models. Includes loglik, BIC (all), ICL (PLNPCA) and pen_loglik, EBIC (PLNnetwork)
reverseA logical indicating whether to plot the value of the criteria in the "natural" direction (loglik - penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on the same scale as the log-likelihood.
A ggplot2 object
show()User friendly print method
PLNfamily$show()
clone()The objects of this class are cloneable with this method.
PLNfamily$clone(deep = FALSE)deepWhether to make a deep clone.
The parameter control is a list controlling the optimization with the following entries:
"covariance" character setting the model for the covariance matrix. Either "full", "diagonal", "spherical" or "genetic". Default is "full".
"corr_matrix": a symmetric positive definite correlation matrix used for the "genetic" model of covariance. Useless in other cases
"trace" integer for verbosity.
"inception" Set up the initialization. By default, the model is initialized with a multivariate linear model applied on log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit), which sometimes speeds up the inference.
"ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise.
"ftol_abs" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0
"xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4
"xtol_abs" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0
"maxeval" stop when the number of iteration exceeds maxeval. Default is 10000
"maxtime" stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction)
"algorithm" the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ".
getModel()