Description Usage Arguments Value Author(s) See Also Examples

View source: R/factorLoadingDiamondCIplot.R

This function uses the `diamondPlot`

to visualise the results of a factor analyses. Because the factor loadings computed in factor analysis are point estimates, they may vary from sample to sample. The factor loadings for any given sample are usually not relevant; samples are but means to study populations, and so, researchers are usually interested in population values for the factor loadings. However, tables with lots of loadings can quickly become confusing and intimidating. This function aims to facilitate working with and interpreting factor analysis based on confidence intervals by visualising the factor loadings and their confidence intervals.

1 2 3 4 5 6 7 |

`fa` |
The object produced by the |

`xlab` |
The label for the x axis. |

`colors` |
The colors used for the factors. The default uses the discrete |

`labels` |
The labels to use for the items (on the Y axis). |

`theme` |
The ggplot2 theme to use. |

`...` |
Additional arguments will be passed to |

A `ggplot`

plot with several `ggDiamondLayer`

s is returned.

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

`fa`

, `meansDiamondPlot`

, `meanSDtoDiamondPlot`

, `diamondPlot`

, `ggDiamondLayer`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Not run:
### (Not run during testing because it takes too long and
### may generate warnings because of the bootstrapping of
### the confidence intervals)
factorLoadingDiamondCIplot(fa(Bechtoldt,
nfactors=2,
n.iter=50,
n.obs=200));
### And using a lower alpha value for the diamonds to
### make them more transparent
factorLoadingDiamondCIplot(fa(Bechtoldt,
nfactors=2,
n.iter=50,
n.obs=200),
alpha=.5,
size=1);
## End(Not run)
``` |

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