Nothing
npreg <-
function(bws, ...){
args <- list(...)
if (!missing(bws)){
if (is.recursive(bws)){
if (!is.null(bws$formula) && is.null(args$txdat))
UseMethod("npreg",bws$formula)
else if (!is.null(bws$call) && is.null(args$txdat))
UseMethod("npreg",bws$call)
else if (!is.call(bws))
UseMethod("npreg",bws)
else
UseMethod("npreg",NULL)
} else {
UseMethod("npreg", NULL)
}
} else {
UseMethod("npreg", NULL)
}
}
npreg.formula <-
function(bws, data = NULL, newdata = NULL, y.eval = FALSE, ...){
tt <- terms(bws)
m <- match(c("formula", "data", "subset", "na.action"),
names(bws$call), nomatch = 0)
tmf <- bws$call[c(1,m)]
tmf[[1]] <- as.name("model.frame")
tmf[["formula"]] <- tt
umf <- tmf <- eval(tmf, envir = environment(tt))
tydat <- model.response(tmf)
txdat <- tmf[, attr(attr(tmf, "terms"),"term.labels"), drop = FALSE]
if ((has.eval <- !is.null(newdata))) {
if (!y.eval){
tt <- delete.response(tt)
orig.class <- sapply(eval(attr(tt, "variables"), newdata, environment(tt)),class)
## delete.response clobbers predvars, which is used for timeseries objects
## so we need to reconstruct it
if(all(orig.class == "ts")){
args <- (as.list(attr(tt, "variables"))[-1])
attr(tt, "predvars") <- as.call(c(quote(as.data.frame),as.call(c(quote(ts.intersect), args))))
}else if(any(orig.class == "ts")){
arguments <- (as.list(attr(tt, "variables"))[-1])
arguments.normal <- arguments[which(orig.class != "ts")]
arguments.timeseries <- arguments[which(orig.class == "ts")]
ix <- sort(c(which(orig.class == "ts"),which(orig.class != "ts")),index.return = TRUE)$ix
attr(tt, "predvars") <- bquote(.(as.call(c(quote(cbind),as.call(c(quote(as.data.frame),as.call(c(quote(ts.intersect), arguments.timeseries)))),arguments.normal,check.rows = TRUE)))[,.(ix)])
}else{
attr(tt, "predvars") <- attr(tt, "variables")
}
}
umf <- emf <- model.frame(tt, data = newdata)
if (y.eval)
eydat <- model.response(emf)
exdat <- emf[, attr(attr(emf, "terms"),"term.labels"), drop = FALSE]
}
ev <- eval(parse(text=paste("npreg(txdat = txdat, tydat = tydat,",
ifelse(has.eval,paste("exdat = exdat,",ifelse(y.eval,"eydat = eydat,","")),""),
"bws = bws, ...)")))
ev$call <- match.call(expand.dots = FALSE)
environment(ev$call) <- parent.frame()
ev$omit <- attr(umf,"na.action")
ev$rows.omit <- as.vector(ev$omit)
ev$nobs.omit <- length(ev$rows.omit)
ev$mean <- napredict(ev$omit, ev$mean)
ev$merr <- napredict(ev$omit, ev$merr)
if(ev$gradients){
ev$grad <- napredict(ev$omit, ev$grad)
ev$gerr <- napredict(ev$omit, ev$gerr)
}
if(ev$residuals){
ev$resid <- naresid(ev$omit, ev$resid)
}
return(ev)
}
npreg.call <-
function(bws, ...) {
ev <- npreg(txdat = eval(bws$call[["xdat"]], environment(bws$call)),
tydat = eval(bws$call[["ydat"]], environment(bws$call)),
bws = bws, ...)
ev$call <- match.call(expand.dots = FALSE)
environment(ev$call) <- parent.frame()
return(ev)
}
npreg.rbandwidth <-
function(bws,
txdat = stop("training data 'txdat' missing"),
tydat = stop("training data 'tydat' missing"),
exdat, eydat, gradients = FALSE, residuals = FALSE,
...){
no.ex = missing(exdat)
no.ey = missing(eydat)
txdat = toFrame(txdat)
if (!(is.vector(tydat) | is.factor(tydat)))
stop("'tydat' must be a vector or a factor")
## if no.ex then if !no.ey then ey and tx must match, to get oos errors
## alternatively if no.ey you get is errors
## if !no.ex then if !no.ey then ey and ex must match, to get oos errors
## alternatively if no.ey you get NO errors since we don't evaluate on the training
## data
if (!no.ex){
exdat = toFrame(exdat)
if (! txdat %~% exdat )
stop("'txdat' and 'exdat' are not similar data frames!")
if (!no.ey){
if (!(is.vector(eydat) | is.factor(eydat)))
stop("'eydat' must be a vector or a factor")
if (dim(exdat)[1] != length(eydat))
stop("number of evaluation data 'exdat' and dependent data 'eydat' do not match")
if (!identical(coarseclass(eydat),coarseclass(tydat)))
stop("type of evaluation data 'eydat' does not match that of 'tydat'")
}
} else if(!no.ey) {
if (dim(txdat)[1] != length(eydat))
stop("number of training data 'txdat' and dependent data 'eydat' do not match")
}
if (length(bws$bw) != length(txdat))
stop("length of bandwidth vector does not match number of columns of 'txdat'")
ccon = unlist(lapply(txdat[,bws$icon, drop = FALSE],class))
if ((any(bws$icon) && !all((ccon == class(integer(0))) | (ccon == class(numeric(0))))) ||
(any(bws$iord) && !all(unlist(lapply(txdat[,bws$iord, drop = FALSE],class)) ==
class(ordered(0)))) ||
(any(bws$iuno) && !all(unlist(lapply(txdat[,bws$iuno, drop = FALSE],class)) ==
class(factor(0)))))
stop("supplied bandwidths do not match 'txdat' in type")
if (dim(txdat)[1] != length(tydat))
stop("number of explanatory data 'txdat' and dependent data 'tydat' do not match")
## catch and destroy NA's
goodrows = 1:dim(txdat)[1]
rows.omit = attr(na.omit(data.frame(txdat,tydat)), "na.action")
goodrows[rows.omit] = 0
if (all(goodrows==0))
stop("Training data has no rows without NAs")
txdat = txdat[goodrows,,drop = FALSE]
tydat = tydat[goodrows]
## no.ex = missing(exdat)
## no.ey = missing(eydat)
if (!no.ex){
goodrows = 1:dim(exdat)[1]
rows.omit = eval(parse(text=paste('attr(na.omit(data.frame(exdat',
ifelse(no.ey,"",",eydat"),')), "na.action")')))
goodrows[rows.omit] = 0
exdat = exdat[goodrows,,drop = FALSE]
if (!no.ey)
eydat = eydat[goodrows]
if (all(goodrows==0))
stop("Evaluation data has no rows without NAs")
}
## evaluate residuals before data conversion ...
if (residuals){
resid <- tydat - npreg(txdat = txdat, tydat = tydat, bws = bws)$mean
}
tnrow = dim(txdat)[1]
enrow = ifelse(no.ex,tnrow,dim(exdat)[1])
ncol = dim(txdat)[2]
## convert tydat, eydat to numeric, from a factor with levels from the y-data
## used during bandwidth selection.
if (is.factor(tydat)){
tydat <- adjustLevels(data.frame(tydat), bws$ydati)[,1]
tydat <- (bws$ydati$all.dlev[[1]])[as.integer(tydat)]
}
else
tydat <- as.double(tydat)
if (no.ey)
eydat <- double()
else {
if (is.factor(eydat)){
eydat <- adjustLevels(data.frame(eydat), bws$ydati, allowNewCells = TRUE)
eydat <- toMatrix(eydat)[,1]
}
else
eydat <- as.double(eydat)
}
## re-assign levels in training and evaluation data to ensure correct
## conversion to numeric type.
txdat <- adjustLevels(txdat, bws$xdati)
if (!no.ex)
exdat <- adjustLevels(exdat, bws$xdati, allowNewCells = TRUE)
## grab the evaluation data before it is converted to numeric
if(no.ex)
teval <- txdat
else
teval <- exdat
## put the unordered, ordered, and continuous data in their own objects
## data that is not a factor is continuous.
txdat = toMatrix(txdat)
tuno = txdat[, bws$iuno, drop = FALSE]
tcon = txdat[, bws$icon, drop = FALSE]
tord = txdat[, bws$iord, drop = FALSE]
if (!no.ex){
exdat = toMatrix(exdat)
euno = exdat[, bws$iuno, drop = FALSE]
econ = exdat[, bws$icon, drop = FALSE]
eord = exdat[, bws$iord, drop = FALSE]
} else {
euno = data.frame()
eord = data.frame()
econ = data.frame()
}
myopti = list(
num_obs_train = tnrow,
num_obs_eval = enrow,
num_uno = bws$nuno, num_ord = bws$nord,
num_con = bws$ncon,
int_LARGE_SF = ifelse(bws$scaling, SF_NORMAL, SF_ARB),
BANDWIDTH_reg_extern = switch(bws$type,
fixed = BW_FIXED,
generalized_nn = BW_GEN_NN,
adaptive_nn = BW_ADAP_NN),
int_MINIMIZE_IO=ifelse(options('np.messages'), IO_MIN_FALSE, IO_MIN_TRUE),
kerneval = switch(bws$ckertype,
gaussian = CKER_GAUSS + bws$ckerorder/2 - 1,
epanechnikov = CKER_EPAN + bws$ckerorder/2 - 1,
uniform = CKER_UNI,
"truncated gaussian" = CKER_TGAUSS),
ukerneval = switch(bws$ukertype,
aitchisonaitken = UKER_AIT,
liracine = UKER_LR),
okerneval = switch(bws$okertype,
wangvanryzin = OKER_WANG,
liracine = OKER_LR),
ey_is_ty = no.ey,
do_grad = gradients,
regtype = switch(bws$regtype,
lc = REGTYPE_LC,
ll = REGTYPE_LL),
no.ex = no.ex,
mcv.numRow = attr(bws$xmcv, "num.row"),
int_do_tree = ifelse(options('np.tree'), DO_TREE_YES, DO_TREE_NO),
old.reg = FALSE)
myout=
.C("np_regression",
as.double(tuno), as.double(tord), as.double(tcon), as.double(tydat),
as.double(euno), as.double(eord), as.double(econ), as.double(eydat),
as.double(c(bws$bw[bws$icon],bws$bw[bws$iuno],bws$bw[bws$iord])),
as.double(bws$xmcv), as.double(attr(bws$xmcv, "pad.num")),
as.double(bws$nconfac), as.double(bws$ncatfac), as.double(bws$sdev),
as.integer(myopti),
mean = double(enrow),
merr = double(enrow),
g = double(ifelse(gradients,enrow*ncol,0)),
gerr = double(ifelse(gradients,enrow*ncol,0)),
xtra = double(6),
PACKAGE="np" )[c("mean","merr", "g", "gerr", "xtra")]
if (gradients){
myout$g = matrix(data=myout$g, nrow = enrow, ncol = ncol, byrow = FALSE)
rorder = numeric(ncol)
rorder[c((1:ncol)[bws$icon], (1:ncol)[bws$iuno], (1:ncol)[bws$iord])]=1:ncol
myout$g = as.matrix(myout$g[,rorder])
myout$gerr = matrix(data=myout$gerr, nrow = enrow, ncol = ncol, byrow = FALSE)
myout$gerr = as.matrix(myout$gerr[,rorder])
}
ev <- eval(parse(text = paste("npregression(bws = bws,",
"eval = teval,",
"mean = myout$mean, merr = myout$merr,",
ifelse(gradients,
"grad = myout$g, gerr = myout$gerr,",""),
ifelse(residuals, "resid = resid,", ""),
"ntrain = tnrow,",
"trainiseval = no.ex,",
"gradients = gradients,",
"residuals = residuals,",
"xtra = myout$xtra, rows.omit = rows.omit)")))
ev$call <- match.call(expand.dots = FALSE)
environment(ev$call) <- parent.frame()
return(ev)
}
npreg.default <- function(bws, txdat, tydat, ...){
sc <- sys.call()
sc.names <- names(sc)
## here we check to see if the function was called with tdat =
## if it was, we need to catch that and map it to dat =
## otherwise the call is passed unadulterated to npudensbw
bws.named <- any(sc.names == "bws")
txdat.named <- any(sc.names == "txdat")
tydat.named <- any(sc.names == "tydat")
no.bws <- missing(bws)
no.txdat <- missing(txdat)
no.tydat <- missing(tydat)
## if bws was passed in explicitly, do not compute bandwidths
if(txdat.named)
txdat <- toFrame(txdat)
sc.bw <- sc
sc.bw[[1]] <- quote(npregbw)
if(bws.named){
sc.bw$bandwidth.compute <- FALSE
}
ostxy <- c('txdat','tydat')
nstxy <- c('xdat','ydat')
m.txy <- match(ostxy, names(sc.bw), nomatch = 0)
if(any(m.txy > 0)) {
names(sc.bw)[m.txy] <- nstxy[m.txy > 0]
}
tbw <- eval.parent(sc.bw)
## convention: drop 'bws' and up to two unnamed arguments (including bws)
if(no.bws){
tx.str <- ",txdat = txdat"
ty.str <- ",tydat = tydat"
} else {
tx.str <- ifelse(txdat.named, ",txdat = txdat","")
ty.str <- ifelse(tydat.named, ",tydat = tydat","")
if((!bws.named) && (!txdat.named)){
ty.str <- ifelse(tydat.named, ",tydat = tydat",
ifelse(no.tydat,"",",tydat"))
}
}
ev <- eval(parse(text=paste("npreg(bws = tbw", tx.str, ty.str, ",...)")))
ev$call <- match.call(expand.dots = FALSE)
environment(ev$call) <- parent.frame()
return(ev)
}
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