The np Package - NexTag Supports Open Source Initiatives

npplot 65DetailsValuenpplot is a general purpose plotting routine for visually exploring objects generated by the nplibrary, such as regressions, quantile regressions, partially linear regressions, single-index models,densities and distributions.Visualizing one and two dimensional datasets is a straightforward process. The default behavior ofnpplot is to generate a standard 2D plot to visualize univariate data, and a perspective plot forbivariate data. When visualizing higher dimensional data, npplot resorts to plotting a series of1D slices of the data. For a slice along dimension i, all other variables at indices j ≠ i are heldconstant at the quantiles specified in the jth element of xq. The default is the median.The slice itself is evaluated on a uniformly spaced sequence of neval points. The interval of evaluationis determined by the training data. The default behavior is to evaluate from min(txdat[,i])to max(txdat[,i]). The xtrim variable allows for control over this behavior. When xtrimis set, data is evaluated from the xtrim[i]th quantile of txdat[,i] to the 1.0-xtrim[i]thquantile of txdat[,i].Furthermore, xtrim can be set to a negative value in which case it will expand the limits of theevaluation interval beyond the support of the training data, by measuring the distance betweenmin(txdat[,i]) and the xtrim[i]th quantile of txdat[,i], and extending the support bythat distance on the lower limit of the interval. npplot uses an analogous procedure to extend theupper limit of the interval.Bootstrap resampling is conducted pairwise on (y, X, Z) (i.e., by resampling from rows of the(y, X) data or (y, X, Z) data where appropriate). inid admits general heteroskedasticity of unknownform, though it does not allow for dependence. fixed conducts Kunsch’s (1988) blockbootstrap for dependent data, while geom conducts Politis and Romano’s (1994) stationary bootstrap.For consistency of the block and stationary bootstrap, the (mean) block length b should grow withthe sample size n at an appropriate rate. If b is not given, then a default growth rate of const × n 1/3is used. This rate is “optimal” under certain conditions (see Politis and Romano (1994) for moredetails). However, in general the growth rate depends on the specific properties of the DGP. Adefault value for const (3.15) has been determined by a Monte Carlo simulation using a GaussianAR(1) process (AR(1)-parameter of 0.5, 500 observations). const has been chosen such that themean square error for the bootstrap estimate of the variance of the empirical mean is minimized.Setting plot.behavior will instruct npplot what data to return. Option summary:plot: instruct npplot to just plot the data and return NULLplot-data: instruct npplot to plot the data and return the data used to generate the plots. Thedata will be a list of objects of the appropriate type, with one object per plot. For example,invoking npplot on 3D density data will have it return a list of three npdensity objects. If biaseswere calculated, they are stored in a component named biasdata: instruct npplot to generate data only and no plotsUsage IssuesIf you are using data of mixed types, then it is advisable to use the data.frame function toconstruct your input data and not cbind, since cbind will typically not work as intended onmixed data types and will coerce the data to the same type.