The np Package - NexTag Supports Open Source Initiatives

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50 npksum# Finally, if you wish to use initial values for numerical search, you# can either provide a vector of bandwidths as in bws=c(...) or a# bandwidth object from a previous run, as inbw
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
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 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: npudensbw 49# previous examples.bw
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
Page 81 and 82: npplregbw 81x2
Page 83 and 84: npqcmstest 83npqcmstestKernel Consi
Page 85 and 86: 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
Page 93 and 94: npreg 93residR2MSEMAEMAPECORRSIGNif
Page 95 and 96: npreg 95summary(model)# Use npplot(
Page 97 and 98: npreg 97# - this may take a few min
Page 99 and 100: npreg 99# then a noisy samplen
Page 101 and 102:
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, ...)##
Page 113 and 114:
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
Page 125 and 126:
npscoef 125x1
Page 127 and 128:
npscoef 127Valueeydatezdaterrorsres
Page 129 and 130:
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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