R/nobs.R

In tidyverse/broom: Convert Statistical Objects into Tidy Tibbles

# `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
}