tidy.factanal {broom}

Tidy a(n) factanal object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

## S3 method for class 'factanal'
tidy(x, ...)

Arguments

x

A factanal object created by stats::factanal().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

Value

A tibble::tibble() with columns:

variable

Variable under consideration.

uniqueness

Proportion of residual, or unexplained variance

flX

Factor loading for level X.

See Also

tidy(), stats::factanal()

Other factanal tidiers: augment.factanal(), glance.factanal()

Examples

set.seed(123)

# generate data
library(dplyr)
library(purrr)

m1 <- tibble(
  v1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 5, 6),
  v2 = c(1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 3, 4, 3, 3, 3, 4, 6, 5),
  v3 = c(3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 4, 6),
  v4 = c(3, 3, 4, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 5, 6, 4),
  v5 = c(1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 6, 4, 5),
  v6 = c(1, 1, 1, 2, 1, 3, 3, 3, 4, 3, 1, 1, 1, 2, 1, 6, 5, 4)
)

# new data
m2 <- map_dfr(m1, rev)

# factor analysis objects
fit1 <- factanal(m1, factors = 3, scores = "Bartlett")
fit2 <- factanal(m1, factors = 3, scores = "regression")

# tidying the object
tidy(fit1)
tidy(fit2)

# augmented dataframe
augment(fit1)
augment(fit2)

# augmented dataframe (with new data)
augment(fit1, data = m2)
augment(fit2, data = m2)