Tidy a(n) factanal object
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.
## S3 method for class 'factanal' tidy(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble() with columns:
variable |
Variable under consideration. |
uniqueness |
Proportion of residual, or unexplained variance |
flX |
Factor loading for level X. |
Other factanal tidiers:
augment.factanal(),
glance.factanal()
set.seed(123) # data m1 <- dplyr::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 <- purrr::map_dfr(m1, rev) # factor analysis objects fit1 <- stats::factanal(m1, factors = 3, scores = "Bartlett") fit2 <- stats::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)
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