Extracts univariate standard errors for the estimated coefficient of B. Standard errors are computed from the (approximate) Fisher information matrix.

# S3 method for PLNPCAfit
standard_error(
  object,
  type = c("variational", "jackknife", "sandwich"),
  parameter = c("B", "Omega")
)

standard_error(
  object,
  type = c("variational", "jackknife", "sandwich"),
  parameter = c("B", "Omega")
)

# S3 method for PLNfit
standard_error(
  object,
  type = c("variational", "jackknife", "bootstrap", "sandwich"),
  parameter = c("B", "Omega")
)

# S3 method for PLNfit_fixedcov
standard_error(
  object,
  type = c("variational", "jackknife", "bootstrap", "sandwich"),
  parameter = c("B", "Omega")
)

# S3 method for PLNmixturefit
standard_error(
  object,
  type = c("variational", "jackknife", "sandwich"),
  parameter = c("B", "Omega")
)

# S3 method for PLNnetworkfit
standard_error(
  object,
  type = c("variational", "jackknife", "sandwich"),
  parameter = c("B", "Omega")
)

Arguments

object

an R6 object with class PLNfit

type

string describing the type of variance approximation: "variational", "jackknife", "sandwich" (only for fixed covariance). Default is "variational".

parameter

string describing the target parameter: either B (regression coefficients) or Omega (inverse residual covariance)

Value

A p * d positive matrix (same size as \(B\)) with standard errors for the coefficients of \(B\)

Methods (by class)

  • standard_error(PLNPCAfit): Component-wise standard errors of B in PLNPCAfit (not implemented yet)

  • standard_error(PLNfit): Component-wise standard errors of B in PLNfit

  • standard_error(PLNfit_fixedcov): Component-wise standard errors of B in PLNfit_fixedcov

  • standard_error(PLNmixturefit): Component-wise standard errors of B in PLNmixturefit (not implemented yet)

  • standard_error(PLNnetworkfit): Component-wise standard errors of B in PLNnetworkfit (not implemented yet)

See also

vcov.PLNfit() for the complete variance covariance estimation of the coefficient

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera,
              control = PLN_param(config_post = list(variational_var = TRUE)))
#> 
#>  Initialization...
#>  Adjusting a full covariance PLN model with nlopt optimizer
#>  Post-treatments...
#>  DONE!
standard_error(myPLN)
#>                   Che       Hyc       Hym       Hys        Psy        Aga
#> (Intercept) 0.5773734 0.5772773 0.0739217 0.4082411 0.01292334 0.09578536
#>                  Glo       Ath       Cea        Ced        Set       All
#> (Intercept) 0.267228 0.2672575 0.3781441 0.09283852 0.07272435 0.1386773
#>                   Han        Hfo        Hsp       Hve        Sta
#> (Intercept) 0.0723545 0.08665937 0.04610723 0.3333454 0.05861934