Get number of observations from a model

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

Usage

n_obs(x, ...)

# S3 method for class 'glm'
n_obs(x, disaggregate = FALSE, ...)

# S3 method for class 'svyolr'
n_obs(x, weighted = FALSE, ...)

# S3 method for class 'afex_aov'
n_obs(x, shape = c("long", "wide"), ...)

# S3 method for class '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

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
n_obs(m)
#> [1] 32

data(cbpp, package = "lme4")
m <- glm(
  cbind(incidence, size - incidence) ~ period,
  data = cbpp,
  family = binomial(link = "logit")
)
n_obs(m)
#> [1] 56
n_obs(m, disaggregate = TRUE)
#> [1] 842