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# `stats::nobs` is a standard function to retrieve the number of
# observations used to fit a model. Unfortunately, Some packages do
# not define a `stats::nobs.MODEL` method. This file fills-in those missing
# methods. Ideally, we should offload these methods by submitting them for
# adoption in the upstream packages.
# These packages still need to be checked:
# caret
# joineRML
# ergm
# nlme
# rstanarm
# lavaan: conflict between stats::nobs and lavaan::nobs
# mass-ridgelm
# survival-*
# quantreg-rq
# quantreg-rqs
# nnet-multinom
nobs.multinom <- function(object, ...) {
nrow(object$residuals)
}
# orcutt
nobs.orcutt <- function(object, ...) {
nrow(object$residuals)
}
# mass-fitdistr
nobs.fitdistr <- function(object, ...) {
object$n
}
# biglm
nobs.biglm <- function(object, ...) {
object$n
}
# glmnet-cv-glmnet
nobs.cv.glmnet <- function(object, ...) {
stats::nobs(object$glmnet.fit)
}
# gmm
nobs.gmm <- function(object, ...) {
object$n
}
# lfe - felm
nobs.felm <- function(object, ...) {
object$N
}
# lmodel2
nobs.lmodel2 <- function(object, ...) {
object$n
}
# mclust
nobs.Mclust <- function(object, ...) {
object$n
}
# muhaz
nobs.muhaz <- function(object, ...) {
length(object$pin$times)
}
# polca
nobs.poLCA <- function(object, ...) {
object$N
}
# robust-glmrob
nobs.lmRob <- function(object, ...) {
length(object$residuals)
}
nobs.glmRob <- function(object, ...) {
length(object$residuals)
}
# stats-loess
nobs.loess <- function(object, ...) {
object$n
}
# stats-prcomp
nobs.prcomp <- function(object, ...) {
NROW(object$x)
}
# stats-smooth.spline
nobs.smooth.spline <- function(object, ...) {
length(object$x)
}
# bbmle
nobs.bbmle <- function(object, ...) {
length(object@data[[1]])
}
# survival-aareg
nobs.aareg <- function(object, ...) {
object$n[1] # obs / event times / event times in computation
}
# survival-survreg
nobs.survreg <- function(object, ...) {
length(object$linear.predictors)
}
# survival-survfit
nobs.survfit <- function(object, ...) {
object$n
}
# survival-survfit.cox
nobs.survfit.cox <- function(object, ...) {
object$n
}
# survival-coxph
nobs.coxph <- function(object, ...) {
length(object$linear.predictors)
}
# survival-pyears
nobs.pyears <- function(object, ...) {
object$observations
}
# survival-survdiff
nobs.survdiff <- function(object, ...) {
s <- summary(object)
s$nobs
}
# tseries
nobs.garch <- function(object, ...) {
object$n.used
}
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