lplotPV:

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

Usage

lplotPV(x, y, span = 0.75, xout = FALSE, pr = TRUE, outfun = out, nboot = 1000, SEED = TRUE, plotit = TRUE, pyhat = FALSE, expand = 0.5, low.span = 2/3, varfun = pbvar, cor.op = FALSE, cor.fun = pbcor, scale = FALSE, xlab = "X", ylab = "Y", zlab = "", theta = 50, phi = 25, family = "gaussian", duplicate = "error", pc = "*", ticktype = "simple", ...)

Arguments

x
y
span
xout
pr
outfun
nboot
SEED
plotit
pyhat
expand
low.span
varfun
cor.op
cor.fun
scale
xlab
ylab
zlab
theta
phi
family
duplicate
pc
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, span = 0.75, xout = FALSE, pr = TRUE, outfun = out, 
    nboot = 1000, SEED = TRUE, plotit = TRUE, pyhat = FALSE, 
    expand = 0.5, low.span = 2/3, varfun = pbvar, cor.op = FALSE, 
    cor.fun = pbcor, scale = FALSE, xlab = "X", ylab = "Y", zlab = "", 
    theta = 50, phi = 25, family = "gaussian", duplicate = "error", 
    pc = "*", ticktype = "simple", ...) 
{
    if (SEED) 
        set.seed(2)
    x = as.matrix(x)
    if (ncol(x) == 2 && !scale) {
        if (pr) {
            print("scale=F is specified.")
            print("If there is dependence, might use scale=T")
        }
    }
    vals = NA
    nv = ncol(x)
    m = elimna(cbind(x, y))
    x <- m[, 1:nv]
    y <- m[, nv + 1]
    if (xout) {
        flag <- outfun(x, plotit = FALSE, ...)$keep
        m <- m[flag, ]
        x <- m[, 1:nv]
        y <- m[, nv + 1]
    }
    x = as.matrix(x)
    est = lplot(x, y, span = span, plotit = plotit, pr = FALSE, 
        pyhat = pyhat, outfun = outfun, expand = expand, low.span = low.span, 
        varfun = varfun, cor.op = cor.op, cor.fun = cor.fun, 
        scale = scale, xlab = xlab, ylab = ylab, zlab = zlab, 
        theta = theta, phi = phi, family = family, duplicate = duplicate, 
        pc = pc, ticktype = ticktype, ...)
    n = nrow(x)
    data1 <- matrix(sample(n, size = n * nboot, replace = TRUE), 
        nrow = nboot)
    data2 <- matrix(sample(n, size = n * nboot, replace = TRUE), 
        nrow = nboot)
    for (i in 1:nboot) {
        vals[i] = lplot(x[data1[i, ], ], y[data2[i, ]], plotit = FALSE, 
            pr = FALSE)$Strength.Assoc
    }
    p = mean(est$Strength < vals)
    list(p.value = p, Strength.Assoc = est$Strength.Assoc, Explanatory.power = est$Explanatory.power, 
        yhat.values = est$yhat.values)
  }

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