Nothing
npregression <-
function(bws, eval, mean, merr = NA, grad = NA, gerr = NA,
resid = NA,
ntrain, trainiseval = FALSE, gradients = FALSE, residuals = FALSE,
xtra = rep(NA, 6),
rows.omit = NA){
if (missing(bws) | missing(eval) | missing(ntrain))
stop("improper invocation of npregression constructor")
if (length(rows.omit) == 0)
rows.omit <- NA
d <- list(
bw = bws$bw,
bws = bws,
pregtype = bws$pregtype,
xnames = bws$xnames,
ynames = bws$ynames,
nobs = dim(eval)[1],
ndim = bws$ndim,
nord = bws$nord,
nuno = bws$nuno,
ncon = bws$ncon,
pscaling = bws$pscaling,
ptype = bws$ptype,
pckertype = bws$pckertype,
pukertype = bws$pukertype,
pokertype = bws$pokertype,
eval = eval,
mean = mean,
merr = merr,
grad = grad,
gerr = gerr,
resid = resid,
ntrain = ntrain,
trainiseval = trainiseval,
gradients = gradients,
residuals = residuals,
R2 = xtra[1],
MSE = xtra[2],
MAE = xtra[3],
MAPE = xtra[4],
CORR = xtra[5],
SIGN = xtra[6],
rows.omit = rows.omit,
nobs.omit = ifelse(identical(rows.omit,NA), 0, length(rows.omit)))
class(d) <- "npregression"
return(d)
}
print.npregression <- function(x, digits=NULL, ...){
cat("\nRegression Data: ", x$ntrain, " training points,",
ifelse(x$trainiseval, "", paste(" and ", x$nobs," evaluation points,",
sep="")),
" in ",x$ndim," variable(s)\n",sep="")
print(matrix(x$bw,ncol=x$ndim,dimnames=list(paste(x$pscaling,":",sep=""),x$xnames)))
cat(genRegEstStr(x))
cat(genBwKerStrs(x$bws))
cat("\n\n")
if(!missing(...))
print(...,digits=digits)
invisible(x)
}
fitted.npregression <- function(object, ...){
object$mean
}
residuals.npregression <- function(object, ...) {
if(object$residuals) { return(object$resid) } else { return(npreg(bws = object$bws, residuals =TRUE)$resid) }
}
se.npregression <- function(x) { x$merr }
gradients.npregression <- function(x, errors = FALSE, ...) {
if(!errors)
return(x$grad)
else
return(x$gerr)
}
predict.npregression <- function(object, se.fit = FALSE, ...) {
tr <- eval(npreg(bws = object$bws, ...), envir = parent.frame())
if(se.fit)
return(list(fit = fitted(tr), se.fit = se(tr),
df = tr$nobs, residual.scale = tr$MSE))
else
return(fitted(tr))
}
plot.npregression <- function(x, ...) { npplot(bws = x$bws, ...) }
summary.npregression <- function(object, ...) {
cat("\nRegression Data: ", object$ntrain, " training points,",
ifelse(object$trainiseval, "", paste(" and ", object$nobs," evaluation points,",
sep="")),
" in ",object$ndim," variable(s)\n",sep="")
cat(genOmitStr(object))
print(matrix(object$bw,ncol=object$ndim,dimnames=list(paste(object$pscaling,":",sep=""),object$xnames)))
cat(genRegEstStr(object))
cat(genGofStr(object))
cat(genBwKerStrs(object$bws))
cat('\n\n')
}
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