The np Package - NexTag Supports Open Source Initiatives

npksum 51Argumentsformuladatanewdatasubsetna.actiona symbolic description of variables on which the sum is to be performed. Thedetails of constructing a formula are described below.an optional data frame, list or environment (or object coercible to a data frameby as.data.frame) containing the variables in the model. If not found indata, the variables are taken from environment(formula), typically theenvironment from which the function is called.An optional data frame in which to look for evaluation data. If omitted, datais used.an optional vector specifying a subset of observations to be used.a function which indicates what should happen when the data contain NAs. Thedefault is set by the na.action setting of options, and is na.fail if that isunset. The (recommended) default is na.omit.... additional arguments supplied to specify the parameters to the default S3method, which is called during estimation.txdattydatexdatbwsa p-variate data frame of sample realizations (training data) used to compute thesum.a numeric vector of data to be weighted. The ith kernel weight is applied to theith element. Defaults to 1.a p-variate data frame of sum evaluation points (if omitted, defaults to the trainingdata itself).a bandwidth specification. This can be set as any suitable bandwidth objectreturned from a bandwidth-generating function, or a numeric vector.weights a n by q matrix of weights which can optionally be applied to tydat in thesum. See details.leave.one.outa logical value to specify whether or not to compute the leave one out sums.Will not work if exdat is specified. Defaults to FALSE.kernel.pow an integer specifying the power to which the kernels will be raised in the sum.Defaults to 1.bandwidth.dividea logical specifying whether or not to divide continuous kernel weights by theirbandwidths. Use this with nearest-neighbor methods. Defaults to FALSE.convolution.kernela logical specifying whether or not convolution kernels are to be used. If TRUEall other options will be ignored. Defaults to FALSE.smooth.coefficienta logical specifying whether or not to use certain optimisations if the smoothcoefficient estimator is being computed. Currently does nothing. Defaults toFALSE.Detailsnpksum exists so that you can create your own kernel objects with or without a variable to beweighted (default Y = 1). With the options available, you could create new nonparametric tests or