The np Package - NexTag Supports Open Source Initiatives

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94 npregattach(Italy)# First, compute the least-squares cross-validated bandwidths for the# local constant estimator (default). We override the default tolerances# for the search method as the objective function is well-behaved (don't# of course do this in general).bw
npreg 95summary(model)# Use npplot() to visualize the regression function, add bootstrap# error bars, and overlay the data on the same plot.# Note - this may take a minute or two depending on the speed of your# computer due to bootstrapping being conducted (-C will# interrupt). Note - nothing will appear in the graphics window until# all computations are completed (if you use# plot.errors.method="asymptotic" the figure will instantly appear).npplot(bws=bw, plot.errors.method="bootstrap")points(ordered(year), gdp, cex=.2, col="red")detach(Italy)# Sleep for 5 seconds so that we can examine the output...Sys.sleep(5)# EXAMPLE 2 (INTERFACE=FORMULA): For this example, we compute a local# linear fit using the AIC_c bandwidth selection criterion. We then plot# the estimator and its gradient using asymptotic standard errors.data("cps71")attach(cps71)bw
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The np PackageFebruary 16, 2008Vers
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Italy 3Examplesdata("cps71")attach(
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wage1 5Examplesdata("oecdpanel")att
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gradients 7## S3 method for class '
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np 9A variety of bandwidth methods
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npcmstest 11npcmstestKernel Consist
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npcmstest 13ReferencesAitchison, J.
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npcdens 15npcdensKernel Conditional
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npcdens 17Valuenpcdens returns a co
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npcdens 19# Gaussian kernel (defaul
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npcdens 21# (1993) (see their descr
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npcdensbw 23fit
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npcdensbw 25na.actionxdatydatbwspro
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npcdensbw 27data. The approach is b
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npconmode 29# depending on the spee
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npconmode 31tydatexdateydata one (1
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npconmode 33lwt,family=binomial(lin
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npudens 35npudensKernel Density and
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npudens 37Author(s)Tristen Hayfield
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npudens 39# EXAMPLE 1 (INTERFACE=DA
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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
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: npreg 93residR2MSEMAEMAPECORRSIGNif
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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