| Name : R-hornpa
| |
| Version : 1.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.9
| Date : 2024-06-14 11:09:34
|
| Group : Development/Libraries/Other
| Source RPM : R-hornpa-1.1.0-lp153.1.9.src.rpm
|
| Size : 0.02 MB
| |
| Packager : (none)
| |
| Summary : Horn\'s (1965) Test to Determine the Number of Components/Factors
|
Description :
A stand-alone function that generates a user specified number of random
datasets and computes eigenvalues using the random datasets (i.e.,
implements Horn\'s [1965, Psychometrika] parallel analysis
< https://link.springer.com/article/10.1007/BF02289447>). Users then
compare the resulting eigenvalues (the mean or the specified
percentile) from the random datasets (i.e., eigenvalues resulting from
noise) to the eigenvalues generated with the user\'s data. Can be used
for both principal components analysis (PCA) and common/exploratory
factor analysis (EFA). The output table shows how large eigenvalues can
be as a result of merely using randomly generated datasets. If the
user\'s own dataset has actual eigenvalues greater than the
corresponding eigenvalues, that lends support to retain that
factor/component. In other words, if the i(th) eigenvalue from the
actual data was larger than the percentile of the (i)th eigenvalue
generated using randomly generated data, empirical support is provided
to retain that factor/component. Horn, J. (1965). A rationale and test
for the number of factors in factor analysis. Psychometrika, 32,
179-185.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
