Component-wise standard errors of B

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

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

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

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

# S3 method for class '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) 0.5773521 0.5773 0.07392279 0.4082529 0.01292372 0.09579415
#>                   Glo       Ath       Cea       Ced        Set       All
#> (Intercept) 0.2672489 0.2673105 0.3782165 0.0928489 0.07270995 0.1386912
#>                    Han        Hfo        Hsp       Hve        Sta
#> (Intercept) 0.07233891 0.08667446 0.04608661 0.3333497 0.05862089