The np Package - NexTag Supports Open Source Initiatives

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10 npSo, if you had two variables, x i1 and x i2 , and x i1 was continuous while x i2 was, say, binary (0/1),and you created a data frame of the form X
npcmstest 11npcmstestKernel Consistent Model Specification Test with Mixed DataDescriptionnpcmstest implements a consistent test for correct specification of parametric regression models(linear or nonlinear) as described in Hsiao, Li, and Racine (forthcoming).Usagenpcmstest(formula,data = NULL,subset,xdat,ydat,model = stop(paste(sQuote("model")," has not been provided")),distribution = c("bootstrap", "asymptotic"),boot.method=c("iid","wild","wild-rademacher"),boot.num = 399,pivot = TRUE,density.weighted = TRUE,random.seed = 42,...)Argumentsformuladatasubsetmodelxdatydata symbolic description of variables on which the test 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 vector specifying a subset of observations to be used.a model object obtained from a call to lm (or glm). Important: the call to eitherglm or lm must have the arguments x=TRUE and y=TRUE or npcmstestwill not work.a p-variate data frame of explanatory data (training data) used to calculate theregression estimators.a one (1) dimensional numeric or integer vector of dependent data, each elementi corresponding to each observation (row) i of xdat.distribution a character string used to specify the method of estimating the distribution of thestatistic to be calculated. bootstrap will conduct bootstrapping. asymptoticwill use the normal distribution. Defaults to bootstrap.
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: np 9A variety of bandwidth methods
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 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
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npplot 65DetailsValuenpplot is a ge
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npplot 67year.seq
Page 69 and 70:
npplot 69# npplot(). When npplot()
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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
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
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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
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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
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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