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

An R6 Class to represent a PLNfit in a standard, general framework, with fixed (inverse) residual covariance

Super class

PLNmodels::PLNfit -> PLNfit_fixedcov

Active bindings

nb_param

number of parameters in the current PLN model

vcov_model

character: the model used for the residual covariance

vcov_coef

matrix of sandwich estimator of the variance-covariance of B (needs known covariance at the moment)

Methods

Public methods

• PLNfit_fixedcov$new()

• PLNfit_fixedcov$optimize()

• PLNfit_fixedcov$postTreatment()

• PLNfit_fixedcov$clone()

Inherited methods

• PLNmodels::PLNfit$optimize_vestep()
• PLNmodels::PLNfit$predict()
• PLNmodels::PLNfit$predict_cond()
• PLNmodels::PLNfit$print()
• PLNmodels::PLNfit$show()
• PLNmodels::PLNfit$update()

---

Method new()

Initialize a PLNfit model

Usage

PLNfit_fixedcov$new(responses, covariates, offsets, weights, formula, control)

Arguments

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class

weights

an optional vector of observation weights to be used in the fitting process.

formula

model formula used for fitting, extracted from the formula in the upper-level call

control

a list for controlling the optimization. See details.

---

Method optimize()

Call to the NLopt or TORCH optimizer and update of the relevant fields

Usage

PLNfit_fixedcov$optimize(responses, covariates, offsets, weights, config)

Arguments

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class

weights

an optional vector of observation weights to be used in the fitting process.

config

part of the control argument which configures the optimizer

---

Method postTreatment()

Update R2, fisher and std_err fields after optimization

Usage

PLNfit_fixedcov$postTreatment(
  responses,
  covariates,
  offsets,
  weights = rep(1, nrow(responses)),
  config_post,
  config_optim,
  nullModel = NULL
)

Arguments

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class

weights

an optional vector of observation weights to be used in the fitting process.

config_post

a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details

config_optim

a list for controlling the optimization parameter. See details

nullModel

null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable.

Details

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PLNfit_fixedcov$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }