R/PLNPCAfit-S3methods.R
, R/PLNfit-S3methods.R
, R/PLNmixturefit-S3methods.R
, and 1 more
standard_error.Rd
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")
)
an R6 object with class PLNfit
string describing the type of variance approximation: "variational", "jackknife", "sandwich" (only for fixed covariance). Default is "variational".
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)))
#>
#> Initialization...
#> Adjusting a full covariance PLN model with nlopt optimizer
#> Post-treatments...
#> DONE!
standard_error(myPLN)
#> Che Hyc Hym Hys Psy Aga
#> (Intercept) 0.5773674 0.5772194 0.07392211 0.4082373 0.01292323 0.09578399
#> Glo Ath Cea Ced Set All
#> (Intercept) 0.267255 0.2672593 0.3779843 0.09284677 0.07272687 0.1386797
#> Han Hfo Hsp Hve Sta
#> (Intercept) 0.07234487 0.08667832 0.0460969 0.3334024 0.0586191