Functions to extract loadings bootstrap information from mvdalab objects.

1 2 |

`object` |
an mvdareg or mvdapaca object. A fitted model. |

`ncomp` |
the number of components to include in the model (see below). |

`conf` |
for a bootstrapped model, the confidence level to use. |

`...` |
additional arguments. Currently ignored. |

`loadings`

is used to extract a summary of the loadings of a PLS or PCA model.
If `ncomps`

is missing (or is NULL), summaries for all loadings estimates are returned. Otherwise, if comps is given parameters for a model with only the requested component comps is returned.

Boostrap summaries are provided for `mvdareg`

objects where `validation = "oob"`

. These summaries can also be extracted using `loadings.boots`

A loadings object contains a data frame with columns:

`variable` |
variable names |

`Actual` |
Actual loading estimate using all the data |

`BCa percentiles` |
confidence intervals |

`boot.mean` |
mean of the bootstrap |

`skewness` |
skewness of the bootstrap distribution |

`bias` |
estimate of bias w.r.t. the loading estimate |

`Bootstrap Error` |
estimate of bootstrap standard error |

`t value` |
approximate 't-value' based on the |

`bias t value` |
approximate 'bias t-value' based on the |

Nelson Lee Afanador (nelson.afanador@mvdalab.com)

There are many references explaining the bootstrap. Among them are:

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Efron, B. (1992) Jackknife-after-bootstrap standard errors and influence functions (with Discussion). Journal of the Royal Statistical Society, B, 54, 83:127.

`loadingsplot`

, `loadings.boots`

, `loadingsplot2D`

1 2 3 4 5 6 7 8 9 10 | ```
data(Penta)
## Number of bootstraps set to 500 to demonstrate flexibility
## Use a minimum of 1000 (default) for results that support bootstraping
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
ncomp = 2, validation = "oob", boots = 500)
loadings(mod1, ncomp = 2, conf = .95)
data(iris)
pc1 <- pcaFit(iris)
loadings(pc1)
``` |

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