jack.test(object, ncomp = object$ncomp, use.mean = TRUE)
## S3 method for class 'jacktest':
print(x, P.values = TRUE, \dots)
jack.test
returns an object of class "jacktest"
, with componentsprint.jacktest
returns the "jacktest"
object (invisibly).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 cross-validation 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.
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.var.jack
, mvrCv
data(oliveoil)
mod <- pcr(sensory ~ chemical, data = oliveoil, validation = "LOO", jackknife = TRUE)
jack.test(mod, ncomp = 2)
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