Performes a maximum likelihood factor analysis on a data frame or matrix and plots the factor solution as ellipses or tiles.
In case a data frame is used as argument, 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.

sjp.fa(data, rotation = c("promax", "varimax"), method = c("ml", "minres",
  "wls", "gls", "pa", "minchi", "minrank"), nmbr.fctr = NULL,
  fctr.load.tlrn = 0.1, digits = 2, title = NULL, axis.labels = NULL,
  type = c("bar", "circle", "tile"), geom.size = 0.6,
  geom.colors = "RdBu", wrap.title = 50, wrap.labels = 30,
  show.values = TRUE, show.cronb = TRUE, prnt.plot = TRUE)

Arguments

data

A data frame that should be used to compute a FA, or a fa object.

rotation

Rotation of the factor loadings. May be "varimax" for orthogonal rotation or "promax" for oblique transformation (default). Requires the "GPArotation" package.

method

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.

nmbr.fctr

Number of factors used for calculating the rotation. By default, this value is NULL and the amount of factors is calculated according to a parallel analysis.

fctr.load.tlrn

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

digits

Numeric, amount of digits after decimal point when rounding estimates and values.

title

character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed. For effect-plots, may also be a character vector of length > 1, to define titles for each sub-plot or facet.

axis.labels

character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.

type

Plot type resp. geom type. May be one of following: "circle" or "tile" circular or tiled geoms, or "bar" for a bar plot. You may use initial letter only for this argument.

geom.size

size resp. width of the geoms (bar width, line thickness or point size, depending on plot type and function). Note that bar and bin widths mostly need smaller values than dot sizes.

geom.colors

user defined color for geoms. See 'Details' in sjp.grpfrq.

wrap.title

numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.

wrap.labels

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

show.values

Logical, whether values should be plotted or not.

show.cronb

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.

prnt.plot

logical, if TRUE (default), plots the results as graph. Use FALSE if you don't want to plot any graphs. In either case, the ggplot-object will be returned as value.

Value

(Invisibly) returns a structure with

  • the rotated factor loading matrix (rotate)

  • the column indices of removed variables (for more details see next list item) (removed.colindex)

  • an updated data frame containing all factors that have a clear loading on a specific scale in case data was a data frame (See argument fctr.load.tlrn for more details) (removed.df)

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

  • the ggplot-object (plot),

  • the data frame that was used for setting up the ggplot-object (df).

Note

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

See also

Examples

library(GPArotation) data(efc) # recveive first item of COPE-index scale start <- which(colnames(efc) == "c82cop1") # recveive last item of COPE-index scale end <- which(colnames(efc) == "c90cop9") # use data frame as argument, let sjp.fa() compute FA sjp.fa(efc[, start:end])
#> Parallel analysis suggests that the number of factors = 3 and the number of components = NA
#> Following items have no clear factor loading:
#> none.
sjp.fa(efc[, start:end], type = "tile")
#> Parallel analysis suggests that the number of factors = 3 and the number of components = NA
#> Following items have no clear factor loading:
#> none.