An R6 Class to represent a PLNfit in a standard, general framework, with fixed (inverse) residual covariance
An R6 Class to represent a PLNfit in a standard, general framework, with fixed (inverse) residual covariance
PLNmodels::PLNfit -> PLNfit_fixedcov
nb_paramnumber of parameters in the current PLN model
vcov_modelcharacter: the model used for the residual covariance
vcov_coefmatrix of sandwich estimator of the variance-covariance of B (needs known covariance at the moment)
new()Initialize a PLNfit model
PLNfit_fixedcov$new(responses, covariates, offsets, weights, formula, control)responsesthe matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariatesdesign matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsetsoffset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weightsan optional vector of observation weights to be used in the fitting process.
formulamodel formula used for fitting, extracted from the formula in the upper-level call
controla list for controlling the optimization. See details.
optimize()Call to the NLopt or TORCH optimizer and update of the relevant fields
PLNfit_fixedcov$optimize(responses, covariates, offsets, weights, config)responsesthe matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariatesdesign matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsetsoffset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weightsan optional vector of observation weights to be used in the fitting process.
configpart of the control argument which configures the optimizer
postTreatment()Update R2, fisher and std_err fields after optimization
PLNfit_fixedcov$postTreatment(
responses,
covariates,
offsets,
weights = rep(1, nrow(responses)),
config_post,
config_optim,
nullModel = NULL
)responsesthe matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariatesdesign matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsetsoffset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weightsan optional vector of observation weights to be used in the fitting process.
config_posta list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details
config_optima list for controlling the optimization parameter. See details
nullModelnull model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable.
The list of parameters config controls the post-treatment processing, with the following entries:
trace integer for verbosity. should be > 1 to see output in post-treatments
jackknife boolean indicating whether jackknife should be performed to evaluate bias and variance of the model parameters. Default is FALSE.
bootstrap integer indicating the number of bootstrap resamples generated to evaluate the variance of the model parameters. Default is 0 (inactivated).
variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.
rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE
clone()The objects of this class are cloneable with this method.
PLNfit_fixedcov$clone(deep = FALSE)deepWhether to make a deep clone.
if (FALSE) {
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
}
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