makeFATask: Creates a Factor Analysis Task Object for numeric data
In ptl93/AEDA: Automated Exploratory Data Analysis

Description Usage Arguments Value Examples

A Task encapsulates the Data with some additional information.

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makeFATask(id, data, nfactors = NULL, rotate = "oblimin",
  par.vals = list(), par.vals.parallel = list(plot = FALSE),
  show.NA.msg = FALSE)

`id`	[`character(1)`] ID of the Task Object
`data`	[`data.frame`] A Dataframe with different variables
`nfactors`	[`integer(1)`] Number of factors to extract. If nothing is inserted the optimal number of factors will be calculated according to parallel analysis. For more information @seealso `fa.parallel`. Default is `nfactors = NULL`
`rotate`	[`character(1)`] Defines the rotation method. Possible values are: "none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor", "Promax", "promax", "oblimin", "simplimax", "bentlerQ, "geominQ","biquartimin" and "cluster". Default is `rotate = "oblimin"`. For more information @seealso `fa`
`par.vals`	[`list`] Additional arguments handled over to @seealso `fa`. Default is empty list `par.vals = list()`
`par.vals.parallel`	[`list`] Additional arguments handed over to @seealso `fa.parallel`. Default is `par.vals.parallel = list(plot = FALSE)`
`show.NA.msg`	[`logical(1)`] Logical whether to show missing values message Default is `show.NA.msg = FALSE)`

FATask Object

 library(psych)
 data(bfi)
 #take small sample of size 200L:
 bfi_small = bfi[sample(seq_len(nrow(bfi)), size = 200L), ]
 FA.task = makeFATask(id = "bfi", data = bfi_small)