adrun:

In musto101/wilcox_R: What the package does (short line)

Usage

adrun(x, y, est = tmean, iter = 10, pyhat = FALSE, plotit = TRUE, fr = 1, xlab = "X", ylab = "Y", zlab = "", theta = 50, phi = 25, expand = 0.5, scale = TRUE, zscale = TRUE, xout = FALSE, eout = xout, outfun = out, ticktype = "simple", ...)

Arguments

x
y
est
iter
pyhat
plotit
fr
xlab
ylab
zlab
theta
phi
expand
scale
zscale
xout
eout
outfun
ticktype
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, y, est = tmean, iter = 10, pyhat = FALSE, plotit = TRUE, 
    fr = 1, xlab = "X", ylab = "Y", zlab = "", theta = 50, phi = 25, 
    expand = 0.5, scale = TRUE, zscale = TRUE, xout = FALSE, 
    eout = xout, outfun = out, ticktype = "simple", ...) 
{
    m <- elimna(cbind(x, y))
    if (xout) {
        flag <- outfun(x, plotit = FALSE)$keep
        x = x[flag, ]
        y = y[flag]
    }
    x <- as.matrix(x)
    p <- ncol(x)
    if (p == 1) 
        val <- rungen(x[, 1], y, est = est, pyhat = TRUE, plotit = plotit, 
            fr = fr, xlab = xlab, ylab = ylab, ...)$output
    if (p > 1) {
        library(MASS)
        library(akima)
        np <- p + 1
        x <- m[, 1:p]
        y <- m[, np]
        fhat <- matrix(NA, ncol = p, nrow = length(y))
        fhat.old <- matrix(NA, ncol = p, nrow = length(y))
        res <- matrix(NA, ncol = np, nrow = length(y))
        dif <- 1
        for (i in 1:p) fhat.old[, i] <- rungenv2(x[, i], y, est = est, 
            pyhat = TRUE, plotit = FALSE, fr = fr, ...)
        eval <- NA
        for (it in 1:iter) {
            for (ip in 1:p) {
                res[, ip] <- y
                for (ip2 in 1:p) {
                  if (ip2 != ip) 
                    res[, ip] <- res[, ip] - fhat.old[, ip2]
                }
                fhat[, ip] <- rungenv2(x[, ip], res[, ip], est = est, 
                  pyhat = TRUE, plotit = FALSE, fr = fr, ...)
            }
            eval[it] <- sum(abs(fhat/sqrt(sum(fhat^2)) - fhat.old/sqrt(sum(fhat.old^2))))
            if (it > 1) {
                itm <- it - 1
                dif <- abs(eval[it] - eval[itm])
            }
            fhat.old <- fhat
            if (dif < 0.01) 
                break
        }
        val <- apply(fhat, 1, sum)
        aval <- est(y - val, ...)
        val <- val + aval
        if (plotit && p == 2) {
            fitr <- val
            iout <- c(1:length(fitr))
            nm1 <- length(fitr) - 1
            for (i in 1:nm1) {
                ip1 <- i + 1
                for (k in ip1:length(fitr)) if (sum(x[i, ] == 
                  x[k, ]) == 2) 
                  iout[k] <- 0
            }
            fitr <- fitr[iout >= 1]
            mkeep <- x[iout >= 1, ]
            fitr <- interp(mkeep[, 1], mkeep[, 2], fitr)
            persp(fitr, theta = theta, phi = phi, xlab = xlab, 
                ylab = ylab, zlab = zlab, expand = expand, scale = scale, 
                ticktype = ticktype)
        }
    }
    if (!pyhat) 
        val <- "Done"
    val
  }

musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.