loadings {mvdalab} | R Documentation |
Summary Information Pertaining to the Bootstrapped Loadings
Description
Functions to extract loadings bootstrap information from mvdalab objects.
Usage
## S3 method for class 'mvdareg'
loadings(object, ncomp = object$ncomp, conf = .95, ...)
Arguments
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. |
Details
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
Value
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 |
Author(s)
Nelson Lee Afanador (nelson.afanador@mvdalab.com)
References
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.
See Also
loadingsplot
, loadings.boots
, loadingsplot2D
Examples
data(Penta)
## Number of bootstraps set to 300 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 = 300)
loadings(mod1, ncomp = 2, conf = .95)
data(iris)
pc1 <- pcaFit(iris)
loadings(pc1)