Performs a factor analysis on a data frame or matrix and displays the factors as HTML table, or saves them as file.

In case a data frame is used as parameter, the Cronbach's Alpha value for each factor scale will be calculated, i.e. all variables with the highest loading for a factor are taken for the reliability test. The result is an alpha value for each factor dimension.

  rotation = "promax",
  method = c("ml", "minres", "wls", "gls", "pa", "minchi", "minrank"),
  nmbr.fctr = NULL,
  fctr.load.tlrn = 0.1,
  sort = FALSE,
  title = "Factor Analysis",
  var.labels = NULL,
  wrap.labels = 40,
  show.cronb = TRUE,
  show.comm = FALSE,
  alternate.rows = FALSE,
  digits = 2,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE



A data frame that should be used to compute a PCA, or a prcomp object.


Rotation of the factor loadings. May be one of "varimax", "quartimax", "promax", "oblimin", "simplimax", "cluster" or "none".


the factoring method to be used. "ml" will do a maximum likelihood factor analysis (default). "minres" will do a minimum residual (OLS), "wls" will do a weighted least squares (WLS) solution, "gls" does a generalized weighted least squares (GLS), "pa" will do the principal factor solution, "minchi" will minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair. "minrank" will do a minimum rank factor analysis.


Number of factors used for calculating the rotation. By default, this value is NULL and the amount of factors is calculated according to the Kaiser-criteria.


Specifies the minimum difference a variable needs to have between factor loadings (components) in order to indicate a clear loading on just one factor and not diffusing over all factors. For instance, a variable with 0.8, 0.82 and 0.84 factor loading on 3 possible factors can not be clearly assigned to just one factor and thus would be removed from the principal component analysis. By default, the minimum difference of loading values between the highest and 2nd highest factor should be 0.1


logical, if TRUE, sort the loadings for each factors (items will be sorted in terms of their greatest loading, in descending order)


String, will be used as table caption.


Character vector with variable names, which will be used to label variables in the output.


Numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.


Logical, if TRUE (default), the cronbach's alpha value for each factor scale will be calculated, i.e. all variables with the highest loading for a factor are taken for the reliability test. The result is an alpha value for each factor dimension. Only applies when data is a data frame.


Logical, if TRUE, show the communality column in the table.


Logical, if TRUE, rows are printed in alternatig colors (white and light grey by default).


Amount of decimals for estimates


A list with user-defined style-sheet-definitions, according to the official CSS syntax. See 'Details' or this package-vignette.


Character vector, indicating the charset encoding used for variable and value labels. Default is "UTF-8". For Windows Systems, encoding = "Windows-1252" might be necessary for proper display of special characters.


Destination file, if the output should be saved as file. If NULL (default), the output will be saved as temporary file and opened either in the IDE's viewer pane or the default web browser.


Logical, if TRUE, the HTML table is shown in the IDE's viewer pane. If FALSE or no viewer available, the HTML table is opened in a web browser.


Logical, if TRUE, leading spaces are removed from all lines in the final string that contains the html-data. Use this, if you want to remove parantheses for html-tags. The html-source may look less pretty, but it may help when exporting html-tables to office tools.


Invisibly returns

  • the web page style sheet (,

  • the web page content (page.content),

  • the complete html-output (page.complete),

  • the html-table with inline-css for use with knitr (knitr),

  • the factor.index, i.e. the column index of each variable with the highest factor loading for each factor and

  • the removed.items, i.e. which variables have been removed because they were outside of the fctr.load.tlrn's range.

for further use.


This method for factor analysis relies on the functions fa and fa.parallel from the psych package.


if (FALSE) {
# Data from the EUROFAMCARE sample dataset

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
# auto-detection of labels
if (interactive()) {
  tab_fa(efc[, start:end])