The np Package - NexTag Supports Open Source Initiatives

132 npscoefbwoptimal bandwidths. If specified as a vector, then additional arguments willneed to be supplied as necessary to specify the bandwidth type, kernel types,selection methods, and so on. This can be left unset.... additional arguments supplied to specify the regression type, bandwidth type,kernel types, selection methods, and so on, detailed below.bandwidth.computea logical value which specifies whether to do a numerical search for bandwidthsor not. If set to FALSE, a scbandwidth object will be returned with bandwidthsset to those specified in bws. Defaults to TRUE.bwmethodbwscalingbwtypeckertypeckerordernmultiwhich method was used to select bandwidths. cv.ls specifies least-squarescross-validation, which is all that is currently supported. Defaults to cv.ls.a logical value that when set to TRUE the supplied bandwidths are interpretedas ‘scale factors’ (c j ), otherwise when the value is FALSE they are interpretedas ‘raw bandwidths’ (h j for continuous datatypes, λ j for discrete datatypes). Forcontinuous datatypes, c j and h j are related by the formula h j = c j σ j n −1/(2P +l) ,where σ j is the standard deviation of continuous variable j, n the number of observations,P the order of the kernel, and l the number of continuous variables.For discrete datatypes, c j and h j are related by the formula h j = c j n −2/(2P +l) ,where here j denotes discrete variable j. Defaults to FALSE.character string used for the continuous variable bandwidth type, specifying thetype of bandwidth provided. Defaults to fixed. Option summary:fixed: fixed bandwidths or scale factorsgeneralized_nn: generalized nearest neighborsadaptive_nn: adaptive nearest neighborscharacter string used to specify the continuous kernel type. Can be set as gaussian,epanechnikov, or uniform. Defaults to gaussian.numeric value specifying kernel order (one of (2,4,6,8)). Kernel order specifiedalong with a uniform continuous kernel type will be ignored. Defaults to2.integer number of times to restart the process of finding extrema of the crossvalidationfunction from different (random) initial points. Defaults to min(5,ncol(xdat)).optim.method method used by optim for minimization of the objective function. See ?optimfor references. Defaults to "Nelder-Mead".the default method is an implementation of that of Nelder and Mead (1965),that uses only function values and is robust but relatively slow. It will workreasonably well for non-differentiable functions.method "BFGS" is a quasi-Newton method (also known as a variable metricalgorithm), specifically that published simultaneously in 1970 by Broyden,Fletcher, Goldfarb and Shanno. This uses function values and gradients to buildup a picture of the surface to be optimized.method "CG" is a conjugate gradients method based on that by Fletcher andReeves (1964) (but with the option of Polak-Ribiere or Beale-Sorenson updates).Conjugate gradient methods will generally be more fragile than theBFGS method, but as they do not store a matrix they may be successful in muchlarger optimization problems.