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
mvrobject- ...
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.
Examples
data(yarn)
nir.mvr <- mvr(density ~ NIR, ncomp = 8, validation = "LOO", data = yarn)
nir.mvr
#> Partial least squares regression, fitted with the kernel algorithm.
#> Cross-validated using 28 leave-one-out segments.
#> Call:
#> mvr(formula = density ~ NIR, ncomp = 8, data = yarn, validation = "LOO")
summary(nir.mvr)
#> Data: X dimension: 28 268
#> Y dimension: 28 1
#> Fit method: kernelpls
#> Number of components considered: 8
#>
#> VALIDATION: RMSEP
#> Cross-validated using 28 leave-one-out segments.
#> (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
#> CV 27.46 4.600 3.900 2.090 0.7686 0.5004 0.4425
#> adjCV 27.46 4.454 3.973 2.084 0.7570 0.4967 0.4398
#> 7 comps 8 comps
#> CV 0.2966 0.2643
#> adjCV 0.2926 0.2610
#>
#> TRAINING: % variance explained
#> 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps 7 comps 8 comps
#> X 46.83 98.38 99.46 99.67 99.85 99.97 99.98 99.99
#> density 98.12 98.25 99.64 99.97 99.99 99.99 100.00 100.00
RMSEP(nir.mvr)
#> (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
#> CV 27.46 4.600 3.900 2.090 0.7686 0.5004 0.4425
#> adjCV 27.46 4.454 3.973 2.084 0.7570 0.4967 0.4398
#> 7 comps 8 comps
#> CV 0.2966 0.2643
#> adjCV 0.2926 0.2610
# Extract MVR validation statistics as data.frame:
as.data.frame(RMSEP(nir.mvr, estimate = "CV"))
#> estimate response comps validation method algorithm value
#> 1 CV density 0 LOO mvr kernel 27.4569014
#> 2 CV density 1 LOO mvr kernel 4.6000678
#> 3 CV density 2 LOO mvr kernel 3.8997929
#> 4 CV density 3 LOO mvr kernel 2.0898916
#> 5 CV density 4 LOO mvr kernel 0.7686120
#> 6 CV density 5 LOO mvr kernel 0.5003516
#> 7 CV density 6 LOO mvr kernel 0.4424918
#> 8 CV density 7 LOO mvr kernel 0.2965848
#> 9 CV density 8 LOO mvr kernel 0.2642607
as.data.frame(R2(nir.mvr))
#> estimate response comps validation method algorithm value
#> 1 CV density 0 LOO mvr kernel -0.07544582
#> 2 CV density 1 LOO mvr kernel 0.96981342
#> 3 CV density 2 LOO mvr kernel 0.97830455
#> 4 CV density 3 LOO mvr kernel 0.99376936
#> 5 CV density 4 LOO mvr kernel 0.99915725
#> 6 CV density 5 LOO mvr kernel 0.99964286
#> 7 CV density 6 LOO mvr kernel 0.99972068
#> 8 CV density 7 LOO mvr kernel 0.99987452
#> 9 CV density 8 LOO mvr kernel 0.99990038