margint.cl: Classic marginal integration procedures for additive models
In alemermartinez/RMI-GitHub: Robust marginal integration

Description Usage Arguments Details Value Author(s)

Standard marginal integration procedures for additive models.

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margint.cl(Xp, yp, point = NULL, windows, epsilon = 1e-06, prob = NULL,
  type = "0", degree = NULL, qderivate = FALSE, orderkernel = 2,
  Qmeasure = NULL)

`Xp`	Matrix of explanatory variables (n by p).
`yp`	Vector of responses (missing values are allowed).
`point`	Matrix of points where predictions will be computed and returned.
`windows`	Vector or a squared matrix of bandwidths for the smoothing estimation procedure.
`epsilon`	Convergence criterion.
`prob`	Probabilities of observing each response (n). Defaults to “NULL”.
`type`	Three different type of estimators can be selected: type '0' (local constant on all the covariates), type '1' (local linear smoother on all the covariates), type 'alpha' (local polynomial smoother only on the direction of interest).
`degree`	Degree of the local polynomial smoother in the direction of interest when using the estimator of type 'alpha'. Defaults to “NULL” for the case when using estimators of type '0' or '1'.
`qderivate`	If TRUE, it calculates g^(q+1)/(q+1)! for each component only for the type 'alpha' method. Defaults to “FALSE”.
`orderkernel`	Order of the kernel used in the nuisance directions when using the estimator of type 'alpha'. Defaults to “2”.
`Qmeasure`	A matrix of points where the integration procedure ocurrs. Defaults to “NULL” for calcuting the integrals over the sample.

Three types of classical marginal integration procedures for additive models, that is, considering a squared loss function.

`mu`	Estimate for the intercept.
`g.matrix`	Matrix of estimated additive components (n by p).
`prediction`	Matrix of estimated additive components for the points listed in the argument point.
`mul`	A vector of size p showing in each component the estimated intercept that considers only that direction of interest when using the type 'alpha' method.
`g.derivative`	Matrix of estimated derivatives of the additive components (only when qderivate is “TRUE”) (n by p).
`prediction.derivate`	Matrix of estimated derivatives of the additive components for the points listed in the argument point (only when qderivate is “TRUE”).
`Xp`	Matrix of explanatory variables.
`yp`	Vector of responses.