PLNfit_fixedcov {PLNmodels}

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

Description

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

## Not run: 
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)

## End(Not run)