PLNfit_fixedcov | R Documentation |
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_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)
new()
Initialize a PLNfit
model
PLNfit_fixedcov$new(responses, covariates, offsets, weights, formula, control)
responses
the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariates
design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsets
offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weights
an optional vector of observation weights to be used in the fitting process.
formula
model formula used for fitting, extracted from the formula in the upper-level call
control
a list for controlling the optimization. See details.
optimize()
Call to the NLopt or TORCH optimizer and update of the relevant fields
PLNfit_fixedcov$optimize(responses, covariates, offsets, weights, config)
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
clone()
The objects of this class are cloneable with this method.
PLNfit_fixedcov$clone(deep = FALSE)
deep
Whether to make a deep clone.
## Not run:
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
## End(Not run)
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