jack.test  R Documentation 
Performes approximate t tests of regression coefficients based on jackknife variance estimates.
jack.test(object, ncomp = object$ncomp, use.mean = TRUE)
## S3 method for class 'jacktest'
print(x, P.values = TRUE, ...)
object 
an 
ncomp 
the number of components to use for estimating the variances 
use.mean 
logical. If 
x 
an 
P.values 
logical. Whether to print 
... 
Further arguments sent to the underlying print function

jack.test
uses the variance estimates from var.jack
to perform
t
tests of the regression coefficients. The resulting object has a
print method, print.jacktest
, which uses printCoefmat
for the actual printing.
jack.test
returns an object of class "jacktest"
, with
components
coefficients 
The estimated regression coefficients 
sd 
The square root of the jackknife variance estimates 
tvalues 
The 
df 
The ‘degrees of freedom’
used for calculating 
pvalues 
The calculated 
print.jacktest
returns the "jacktest"
object (invisibly).
The jackknife variance estimates are known to be biased
(see var.jack
). Also, the distribution of the regression
coefficient estimates and the jackknife variance estimates are unknown (at
least in PLSR/PCR). Consequently, the distribution (and in particular, the
degrees of freedom) of the resulting t
statistics is unknown. The
present code simply assumes a t
distribution with m  1
degrees
of freedom, where m
is the number of crossvalidation segments.
Therefore, the resulting p
values should not be used uncritically, and
should perhaps be regarded as mere indicator of (non)significance.
Finally, also keep in mind that as the number of predictor variables increase, the problem of multiple tests increases correspondingly.
BjørnHelge Mevik
Martens H. and Martens M. (2000) Modified Jackknife Estimation of Parameter Uncertainty in Bilinear Modelling by Partial Least Squares Regression (PLSR). Food Quality and Preference, 11, 5–16.
var.jack
, mvrCv
data(oliveoil)
mod < pcr(sensory ~ chemical, data = oliveoil, validation = "LOO", jackknife = TRUE)
jack.test(mod, ncomp = 2)
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