Performes a principle component analysis on a data frame or matrix (with varimax or oblimin rotation) and displays the factor solution 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.
sjt.pca(data, rotation = c("varimax", "quartimax", "promax", "oblimin",
"simplimax", "cluster", "none"), nmbr.fctr = NULL,
fctr.load.tlrn = 0.1, title = "Principal Component Analysis",
var.labels = NULL, wrap.labels = 40, show.cronb = TRUE,
show.msa = FALSE, show.var = FALSE, alternate.rows = FALSE,
digits = 2, string.pov = "Proportion of Variance",
string.cpov = "Cumulative Proportion", CSS = NULL, 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".
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
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, shows an additional column with the measure of sampling adequacy according
dor each component.
Logical, if TRUE, the proportions of variances for each component as well as cumulative
variance are shown in the table footer.
Logical, if TRUE, rows are printed in
alternatig colors (white and light grey by default).
Amount of decimals for estimates
String for the table row that contains the proportions of variances. By default, "Proportion of Variance" will be used.
String for the table row that contains the cumulative variances. By default, "Cumulative Proportion" will be used.
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
openend 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 (page.style),
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.
# NOT RUN {
# Data from the EUROFAMCARE sample dataset
library(sjmisc)
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")
# auto-detection of labels
sjt.pca(efc[, start:end])
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab