View source: R/PLNfit-S3methods.R
standard_error.PLNPCAfit | R Documentation |
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")
)
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) |
A p * d positive matrix (same size as B
) with standard errors for the coefficients of B
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)
vcov.PLNfit()
for the complete variance covariance estimation of the coefficient
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)))
standard_error(myPLN)
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