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28 npcdensbwUsage 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.Caution: multivariate data-driven bandwidth selection methods are, by their nature, computationallyintensive. Virtually all methods require dropping the ith observation from the data set, computingan object, repeating this for all observations in the sample, then averaging each of these leave-oneoutestimates for a given value of the bandwidth vector, and only then repeating this a large numberof times in order to conduct multivariate numerical minimization/maximization. Furthermore, dueto the potential for local minima/maxima, restarting this procedure a large number of times mayoften be necessary. This can be frustrating for users possessing large datasets. For exploratorypurposes, you may wish to override the default search tolerances, say, setting ftol=.01 and tol=.01and conduct multistarting (the default is to restart min(5, ncol(xdat,ydat)) times) as is done for anumber of examples. Once the procedure terminates, you can restart search with default tolerancesusing those bandwidths obtained from the less rigorous search (i.e., set bws=bw on subsequent callsto this routine where bw is the initial bandwidth object). A version of this package using the Rmpiwrapper is under development that allows one to deploy this software in a clustered computingenvironment to facilitate computation involving large datasets.Author(s)Tristen Hayfield 〈hayfield@phys.ethz.ch〉, Jeffrey S. Racine 〈racinej@mcmaster.ca〉ReferencesAitchison, J. and C.G.G. Aitken (1976), “Multivariate binary discrimination by the kernel method,”Biometrika, 63, 413-420.Hall, P. and J.S. Racine and Q. Li (2004), “Cross-validation and the estimation of conditional probabilitydensities,” Journal of the American Statistical Association, 99, 1015-1026.Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton UniversityPress.Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.Scott, D.W. (1992), Multivariate Density Estimation. Theory, Practice and Visualization, NewYork: Wiley.Silverman, B.W. (1986), Density Estimation, London: Chapman and Hall.Wang, M.C. and J. van Ryzin (1981), “A class of smooth estimators for discrete distributions,”Biometrika, 68, 301-309.See Alsobw.nrd, bw.SJ, hist, npudens, npudistExamples# EXAMPLE 1 (INTERFACE=FORMULA): For this example, we compute the# likelihood cross-validated bandwidths (default) using a second-order# Gaussian kernel (default). Note - this may take a minute or two
npconmode 29# depending on the speed of your computer. We override the default# tolerances for the search method as the objective function is# well-behaved (don't of course do this in general).data("Italy")attach(Italy)bw
Page 1 and 2: The np PackageFebruary 16, 2008Vers
Page 3 and 4: Italy 3Examplesdata("cps71")attach(
Page 5 and 6: wage1 5Examplesdata("oecdpanel")att
Page 7 and 8: gradients 7## S3 method for class '
Page 9 and 10: np 9A variety of bandwidth methods
Page 11 and 12: npcmstest 11npcmstestKernel Consist
Page 13 and 14: npcmstest 13ReferencesAitchison, J.
Page 15 and 16: npcdens 15npcdensKernel Conditional
Page 17 and 18: npcdens 17Valuenpcdens returns a co
Page 19 and 20: npcdens 19# Gaussian kernel (defaul
Page 21 and 22: npcdens 21# (1993) (see their descr
Page 23 and 24: npcdensbw 23fit
Page 25 and 26: npcdensbw 25na.actionxdatydatbwspro
Page 27: npcdensbw 27data. The approach is b
Page 31 and 32: npconmode 31tydatexdateydata one (1
Page 33 and 34: npconmode 33lwt,family=binomial(lin
Page 35 and 36: npudens 35npudensKernel Density and
Page 37 and 38: npudens 37Author(s)Tristen Hayfield
Page 39 and 40: npudens 39# EXAMPLE 1 (INTERFACE=DA
Page 41 and 42: npudensbw 41library("datasets")data
Page 43 and 44: npudensbw 43bwsa bandwidth specific
Page 45 and 46: npudensbw 45fvalobjective function
Page 47 and 48: npudensbw 47# previous examples.bw
Page 49 and 50: npudensbw 49# previous examples.bw
Page 51 and 52: npksum 51Argumentsformuladatanewdat
Page 53 and 54: npksum 53Usage IssuesIf you are usi
Page 55 and 56: npksum 55# the bandwidth object its
Page 57 and 58: npksum 57ss
Page 59 and 60: npplot 59plot.behavior = c("plot","
Page 61 and 62: npplot 61xtrim = 0.0,neval = 50,com
Page 63 and 64: npplot 63xdatydatzdatxqyqzqxtrimytr
Page 65 and 66: npplot 65DetailsValuenpplot is a ge
Page 67 and 68: npplot 67year.seq
Page 69 and 70: npplot 69# npplot(). When npplot()
Page 71 and 72: npplreg 71## S3 method for class 'c
Page 73 and 74: npplreg 73residR2MSEMAEMAPECORRSIGN
Page 75 and 76: npplreg 75# Plot the regression sur
Page 77 and 78: npplregbw 77and dependent data), an
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npplregbw 79Detailsnpplregbw implem
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npplregbw 81x2
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npqcmstest 83npqcmstestKernel Consi
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npqcmstest 85Author(s)Tristen Hayfi
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npqreg 87ftol = 1.19209e-07,tol = 1
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npqreg 89Li, Q. and J.S. Racine (20
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npreg 91Usagenpreg(bws, ...)## S3 m
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npreg 93residR2MSEMAEMAPECORRSIGNif
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npreg 95summary(model)# Use npplot(
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npreg 97# - this may take a few min
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npreg 99# then a noisy samplen
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npregbw 101## S3 method for class '
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npregbw 103ckerorderukertypeokertyp
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npregbw 105ReferencesAitchison, J.
Page 107 and 108:
npsigtest 107bw
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npsigtest 109Author(s)Tristen Hayfi
Page 111 and 112:
npindex 111Usagenpindex(bws, ...)##
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npindex 113MAEMAPECORRSIGNif method
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npindex 115x2
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npindex 117# x1 is chi-squared havi
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npindexbw 119# plotting via persp()
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npindexbw 121methodnmultithe single
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npindexbw 123allows one to deploy t
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npscoef 125x1
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npscoef 127Valueeydatezdaterrorsres
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npscoef 129# We could manually plot
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npscoefbw 131optim.abstol,optim.max
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npscoefbw 133optim.maxattemptsmaxim
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npscoefbw 135ReferencesAitchison, J
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uocquantile 137uocquantileCompute Q
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INDEX 139npconmode, 29npindex, 110n
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