Given a response y, a predictor x, and covariates z, the model y=m(x) +b'z +e is considered, where e is a mean-zero random error. There are three options for the null hypothesis: h0=0 tests m(x) is constant; h0=1 tests m(x) is linear, and h0=2 tests m(x) is quadratic. The (respective) alternatives are: m(x) is increasing or decreasing, m(x) is convex or concave, and m(x) is hyper-convex or hyper-concave (referring to the third derivative of m).
● Data Source:
CranContrib
● Keywords: partial linear test, semiparametric
● Alias: partlintest
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2 images
doubconetest
(Package: DoubleCone) :
Test for a vector being in the null space of a double cone
Given an n-vector y and the model y=m+e, and an m by n "irreducible" matrix amat, test the null hypothesis that the vector m is in the null space of amat.
agconst
(Package: DoubleCone) :
Test null hypothesis of constant regression function against a general, high-dimensional alternative
Given a response and 1-3 predictors, the function will test the null hypothesis that the response and predictors are not related (i.e., regression function is constant), against the alternative that the regression function is monotone in each of the predictors. For one predictor, the alternative set is a double cone; for two predictors the alternative set is a quadruple cone, and an octuple cone alternative is used when there are three predictors.
● Data Source:
CranContrib
● Keywords: model test, multiple isotonic regression
● Alias: agconst
●
1 images
Given a response and predictors, the null hypothesis of a parametric regression function is tested versus a large-dimensional alternative in the form of a union of polyhedral convex cones.
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