insight (version 0.17.0)

n_obs: Get number of observations from a model

Description

This method returns the number of observation that were used to fit the model, as numeric value.

Usage

n_obs(x, ...)
# S3 method for glm
n_obs(x, disaggregate = FALSE, ...)
# S3 method for svyolr
n_obs(x, weighted = FALSE, ...)
# S3 method for afex_aov
n_obs(x, shape = c("long", "wide"), ...)
# S3 method for stanmvreg
n_obs(x, select = NULL, ...)

Arguments

x

A fitted model.

...

Currently not used.

disaggregate

For binomial models with aggregated data, n_obs() returns the number of data rows by default. If disaggregate = TRUE, the total number of trials is returned instead (determined by summing the results of weights() for aggregated data, which will be either the weights input for proportion success response or the row sums of the response matrix if matrix response, see 'Examples').

weighted

For survey designs, returns the weighted sample size.

shape

Return long or wide data? Only applicable in repeated measures designs.

select

Optional name(s) of response variables for which to extract values. Can be used in case of regression models with multiple response variables.

Value

The number of observations used to fit the model, or NULL if this information is not available.

Examples

# NOT RUN {
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
n_obs(m)

if (require("lme4")) {
  data(cbpp, package = "lme4")
  m <- glm(
    cbind(incidence, size - incidence) ~ period,
    data = cbpp,
    family = binomial(link = "logit")
  )
  n_obs(m)
  n_obs(m, disaggregate = TRUE)
}
# }