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, mvrCvdata(oliveoil)
mod <- pcr(sensory ~ chemical, data = oliveoil, validation = "LOO", jackknife = TRUE)
jack.test(mod, ncomp = 2)Run the code above in your browser using DataLab