Description Usage Arguments Value Examples
Provides estimates from a rotated factor analysis.
Calculates eigenvalues for p number of dimensions
Simulates data to use as a baseline for making factor/component retention decisions
1 2 3 4 5 6 | factor_analysis(data, ..., nfactors = "parallel", rotation = "oblique",
method = NULL)
eigenvalues(data, ...)
parallel_analysis(data, ...)
|
data |
Data frame of interest |
... |
Variables (unquoted) of interest. If not provided, all variables in data will be used |
nfactors |
Method used to estimate the number of factors. The default, uses a "parallel" analysis to estimate the number of retained factors. Also available is the "kaiser" method, which retains all eigenvalues greater than 1.0. Users may also manually specify the number of factors by entering a numeric [or integer] value here. |
rotation |
Method used to estimate rotated solution. The default "oblique" rotation method (geominQ from GPArotation pkg) assumes the factors are related (a fairly typical or desirable assumption of social science constructs). Also available is the "orthogonal" rotation method (geominT from the GPArotation pkg), which assumes the factors are independent. Users may override these options, providing their own rotation method by specifying [presumably a method offered in the psych or GPArotation packages] here as well. |
Output of factor analysis.
A tibble of p factors, their [eigen]values, and whether the values exceed 1.0
1 2 3 4 5 6 7 8 9 10 | ## conduct parallel analysis of polcom news use items
factor_analysis(polcom, news_1:news_6)
## conduct parallel analysis of polcom news use items
eigenvalues(polcom, news_1:news_6)
## conduct parallel analysis of polcom news use items
parallel_analysis(polcom, news_1:news_6)
|
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