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24 npcdensbwydat = stop("data 'ydat' missing"),bws,bandwidth.compute = TRUE,fast = FALSE,auto = TRUE,nmulti,remin = TRUE,itmax = 10000,ftol = 1.19209e-07,tol = 1.49012e-08,small = 2.22045e-16,...)## Default S3 method:npcdensbw(xdat = stop("data 'xdat' missing"),ydat = stop("data 'ydat' missing"),bws,bandwidth.compute = TRUE,fast,auto,nmulti,remin,itmax,ftol,tol,small,bwmethod,bwscaling,bwtype,cxkertype,cxkerorder,cykertype,cykerorder,uxkertype,uykertype,oxkertype,oykertype,...)Argumentsformuladatasubseta symbolic description of variables on which bandwidth selection is to be performed.The details 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 vector specifying a subset of observations to be used in the fitting
npcdensbw 25na.actionxdatydatbwsprocess.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.a p-variate data frame of explanatory data on which bandwidth selection will beperformed. The datatypes may be continuous, discrete (unordered and orderedfactors), or some combination thereof.a q-variate data frame of dependent data which bandwidth selection will be performed.The datatypes may be continuous, discrete (unordered and ordered factors),or some combination thereof.a bandwidth specification. This can be set as a conbandwidth object returnedfrom a previous invocation, or as a p+q-vector of bandwidths, with each elementi up to i = p corresponding to the bandwidth for column i in xdat, and eachelement i from i = p+1 to i = p+q corresponding to the bandwidth for columni − p in ydat. In either case, the bandwidth supplied will serve as a startingpoint in the numerical search for optimal bandwidths. If specified as a vector,then additional arguments will need to be supplied as necessary to specify thebandwidth type, kernel types, selection methods, and so on. This can be leftunset.... additional arguments supplied to specify the bandwidth type, kernel types, selectionmethods, and so on, detailed below.fastautobwmethodbwscalinga logical value specifying whether a significantly faster, memory intensive procedureshould be used when doing least-squares cross-validation. Recommendedfor datasets of moderate size. Use of this on datasets of size larger than 1000observations will result in decreased performance. For more information see thethe auto argument. Defaults to FALSE.a logical value specifying whether to allow the code to attempt to automaticallyselect the fastest routine, using a heuristic, to compute the least-squares crossvalidationfunction. Defaults to TRUE.which method to use to select bandwidths. cv.ml specifies likelihood crossvalidation,cv.ls specifies least-squares cross-validation, and normal-referencejust computes the ‘rule-of-thumb’ bandwidth h j using the standard formulah j = 1.06σ j n −1/(2P +l) , where σ j is the standard deviation of the jth continuousvariable, n the number of observations, P the order of the kernel, and lthe number of continuous variables. Note that when there exist factors and thenormal-reference rule is used, there is zero smoothing of the factors. Defaults tocv.ml.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.
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: npcdensbw 23fit
Page 27 and 28: npcdensbw 27data. The approach is b
Page 29 and 30: npconmode 29# depending on the spee
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
Page 79 and 80:
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
Page 87 and 88:
npqreg 87ftol = 1.19209e-07,tol = 1
Page 89 and 90:
npqreg 89Li, Q. and J.S. Racine (20
Page 91 and 92:
npreg 91Usagenpreg(bws, ...)## S3 m
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npreg 93residR2MSEMAEMAPECORRSIGNif
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npreg 95summary(model)# Use npplot(
Page 97 and 98:
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 '
Page 103 and 104:
npregbw 103ckerorderukertypeokertyp
Page 105 and 106:
npregbw 105ReferencesAitchison, J.
Page 107 and 108:
npsigtest 107bw
Page 109 and 110:
npsigtest 109Author(s)Tristen Hayfi
Page 111 and 112:
npindex 111Usagenpindex(bws, ...)##
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npindex 113MAEMAPECORRSIGNif method
Page 115 and 116:
npindex 115x2
Page 117 and 118:
npindex 117# x1 is chi-squared havi
Page 119 and 120:
npindexbw 119# plotting via persp()
Page 121 and 122:
npindexbw 121methodnmultithe single
Page 123 and 124:
npindexbw 123allows one to deploy t
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npscoef 125x1
Page 127 and 128:
npscoef 127Valueeydatezdaterrorsres
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npscoef 129# We could manually plot
Page 131 and 132:
npscoefbw 131optim.abstol,optim.max
Page 133 and 134:
npscoefbw 133optim.maxattemptsmaxim
Page 135 and 136:
npscoefbw 135ReferencesAitchison, J
Page 137 and 138:
uocquantile 137uocquantileCompute Q
Page 139:
INDEX 139npconmode, 29npindex, 110n
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