Summary and Print Methods for PLSR and PCR objects

Summary and print methods for mvr and mvrVal objects.

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

Arguments

x, object	an mvr object
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.
...	Other arguments sent to underlying methods.

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.

See also

mvr, pcr, plsr, RMSEP, MSEP

Examples

data(yarn)
nir.mvr <- mvr(density ~ NIR, ncomp = 8, validation = "LOO", data = yarn)
nir.mvr
#> Partial least squares regression , fitted with the simpls 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: simpls
#> 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