margint.cl {rmargint} | R Documentation |
This function computes the standard marginal integration procedures for additive models.
margint.cl(Xp, yp, point = NULL, windows, epsilon = 1e-06, prob = NULL, type = "0", degree = NULL, qderivate = FALSE, orderkernel = 2, Qmeasure = NULL)
Xp |
Matrix (n by p) of explanatory variables. |
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 |
Vector of robabilities of observing each response (n). Defaults to |
type |
Three different type of estimators can be selected: type |
degree |
Degree of the local polynomial smoother in the direction of interest when using the estimator of type |
qderivate |
If TRUE, it calculates |
orderkernel |
Order of the kernel used in the nuisance directions when using the estimator of type |
Qmeasure |
A matrix of points where the integration procedure ocurrs. Defaults to |
This function computes three types of classical marginal integration procedures for additive models, that is, considering a squared loss function.
A list with the following components:
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 |
g.derivative |
Matrix of estimated derivatives of the additive components (only when qderivate is |
prediction.derivate |
Matrix of estimated derivatives of the additive components for the points listed in the argument point (only when qderivate is |
Xp |
Matrix of explanatory variables. |
yp |
Vector of responses. |
Alejandra Martinez, ale_m_martinez@hotmail.com, Matias Salibian-Barrera
Chen R., Hardle W., Linton O.B. and Severance-Lossin E. (1996). Nonparametric estimation of additive separable regression models. Physica-Verlag HD, Switzerland. Linton O. and Nielsen J. (1995). A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika, 82(1), 93-101. Severance-Lossin E. and Sperlich S. (1999). Estimation of derivatives for additive separable models. Statistics, 33(3), 241-265. Tjostheim D. and Auestad B. (1994). Nonparametric identification of nonlinear time series: Selecting significant lags. Journal of the American Statistical Association, 89(428), 1410-1430.
function.g1 <- function(x1) 24*(x1-1/2)^2-2 function.g2 <- function(x2) 2*pi*sin(pi*x2)-4 n <- 150 x1 <- runif(n) x2 <- runif(n) X <- cbind(x1, x2) eps <- rnorm(n,0,sd=0.15) regresion <- function.g1(x1) + function.g2(x2) y <- regresion + eps bandw <- matrix(0.25,2,2) set.seed(8090) nQ <- 80 Qmeasure <- matrix(runif(nQ*2), nQ, 2) fit.cl <- margint.cl(Xp=X, yp=y, windows=bandw, type='alpha', degree=1, Qmeasure=Qmeasure)