The np Package - NexTag Supports Open Source Initiatives

npscoefbw 133optim.maxattemptsmaximum number of attempts taken trying to achieve successful convergence inoptim. Defaults to 100.optim.abstol the absolute convergence tolerance used by optim. Only useful for non-negativefunctions, as a tolerance for reaching zero. Defaults to .Machine$double.eps.optim.reltol relative convergence tolerance used by optim. The algorithm stops if it is unableto reduce the value by a factor of ’reltol * (abs(val) + reltol)’ at a step.Defaults to sqrt(.Machine$double.eps), typically about 1e-8.optim.maxit maximum number of iterations used by optim. Defaults to 500.cv.iterate boolean value specifying whether or not to perform iterative, cross-validatedbackfitting on the data. See details for limitations of the backfitting procedure.Defaults to FALSE.cv.num.iterationsinteger specifying the number of times to iterate the backfitting process over allcovariates. Defaults to 1.backfit.iterateboolean value specifying whether or not to iterate evaluations of the smoothcoefficient estimator, for extra accuracy, during the cross-validated backfittingprocedure. Defaults to FALSE.backfit.maxiterinteger specifying the maximum number of times to iterate the evaluation ofthe smooth coefficient estimator in the attempt to obtain the desired accuracy.Defaults to 100.Detailsbackfit.toltolerance to determine convergence of iterated evaluations of the smooth coefficientestimator. Defaults to .Machine$double.eps.npscoefbw implements a variety of methods for semiparametric regression on multivariate (p+qvariate)explanatory data defined over a set of possibly continuous data. The approach is based onLi and Racine (2003) who employ ‘generalized product kernels’ that admit a mix of continuous anddiscrete datatypes.Three classes of kernel estimators for the continuous datatypes are available: fixed, adaptive nearestneighbor,and generalized nearest-neighbor. Adaptive nearest-neighbor bandwidths change witheach sample realization in the set, x i , when estimating the density at the point x. Generalizednearest-neighbor bandwidths change with the point at which the density is estimated, x. Fixedbandwidths are constant over the support of x.npscoefbw may be invoked either with a formula-like symbolic description of variables on whichbandwidth selection is to be performed or through a simpler interface whereby data is passed directlyto the function via the xdat, ydat, and zdat parameters. Use of these two interfaces ismutually exclusive.Data contained in the data frame xdat may be continuous and in zdat may be of mixed type. Datacan be entered in an arbitrary order and data types will be detected automatically by the routine (seenp for details).Data for which bandwidths are to be estimated may be specified symbolically. A typical descriptionhas the form dependent data ~ parametric explanatory data | nonparametric