summary.mvr: Summary and Print Methods for PLSR and PCR objects

In pls: Partial Least Squares and Principal Component Regression

Summary and Print Methods for PLSR and PCR objects

Description

Summary and print methods for mvr and mvrVal objects.

Usage

## S3 method for class 'mvr'
print(x, ...)

## S3 method for class 'mvr'
summary(
  object,
  what = c("all", "validation", "training"),
  digits = 4,
  print.gap = 2,
  ...
)

## S3 method for class 'mvrVal'
print(x, digits = 4, print.gap = 2, ...)

## S3 method for class 'mvrVal'
as.data.frame(x, row.names = NULL, optional = FALSE, shortAlgs = TRUE, ...)

Arguments

x, object	an mvr object
...	Other arguments sent to underlying methods.
what	one of "all", "validation" or "training"
digits	integer. Minimum number of significant digits in the output. Default is 4.
print.gap	Integer. Gap between coloumns of the printed tables.
row.names	NULL or a character vector giving the row names for the data frame. Missing values are not allowed.
optional	Not used, only included to match signature of as.data.frame.
shortAlgs	Logical. Shorten algorithm names (default = TRUE).

Details

If what is "training", the explained variances are given; if it is "validation", the cross-validated RMSEPs (if available) are given; if it is "all", both are given.

Value

print.mvr and print.mvrVal return the object invisibly.

Author(s)

Ron Wehrens and Bjørn-Helge Mevik

See Also

mvr, pcr, plsr, RMSEP, MSEP

Examples

data(yarn)
nir.mvr <- mvr(density ~ NIR, ncomp = 8, validation = "LOO", data = yarn)
nir.mvr
summary(nir.mvr)
RMSEP(nir.mvr)
# Extract MVR validation statistics as data.frame:
as.data.frame(RMSEP(nir.mvr, estimate = "CV"))
as.data.frame(R2(nir.mvr))

pls documentation built on Nov. 18, 2023, 1:11 a.m.