ppCplot {usefr}  R Documentation 
The function build the PP plot of Twodimensional Copulas upon
the knowledge of the margin distribution provided by the user. The
empirical probabilities are computed using function
empCopula
from package
[copulapackage]{copula}
.
ppCplot(X, Y, copula = NULL, margins = NULL, paramMargins = NULL, npoints = 100, method = "ml", smoothing = c("none", "beta", "checkerboard"), ties.method = "max", xlab = "Empirical probabilities", ylab = "Theoretical probabilities", glwd = 1.2, bgcol = "grey94", gcol = "white", dcol = "red", dlwd = 0.8, tck = NA, tcl = 0.3, xlwd = 0.8, ylwd = 0.8, xcol = "black", ycol = "black", cex.xtitle = 1.3, cex.ytitle = 1.3, padj = 1, hadj = 0.7, xcex = 1.3, ycex = 1.3, xline = 1.6, yline = 2.1, xfont = 3, yfont = 3, family = "serif", lty = 1, bty = "n", col = "black", xlim = c(0, 1), ylim = c(0, 1), pch = 20, las = 1, mar = c(4, 4, 2, 1), font = 3, cex = 1, seed = 132, ...)
X 
Numerical vector with the observations from the first margin distribution. 
Y 
Numerical vector with the observations from the second margin distribution. 
copula 
A copula object from class 
margins 
A character vector specifying all the parametric marginal distributions. See details below. 
paramMargins 
A list whose each component is a list (or numeric vectors) of named components, giving the parameter values of the marginal distributions. See details below. 
npoints 
Number of points used to build the PP plot. The 
method 
A character string specifying the estimation method to be used
to estimate the dependence parameter(s); see

smoothing 
character string specifying whether the empirical
distribution function (for F.n()) or copula (for C.n()) is computed (if
smoothing = "none"), or whether the empirical beta copula (smoothing =
"beta") or the empirical checkerboard copula (smoothing = "checkerboard")
is computed (see 
ties.method 
character string specifying how ranks should be computed
if there are ties in any of the coordinate samples of x; passed to

xlab 
A label for the x axis, defaults to a description of x. 
ylab 
A label for the y axis, defaults to a description of y. 
glwd 
Grid line width. 
bgcol 
Grid background color. 
gcol 
Grid line color 
dcol 
Diagonal line color. 
dlwd 
Diagonal line color. 
tck 
The length of tick marks as a fraction of the smaller of the width or height of the plotting region. If tck >= 0.5 it is interpreted as a fraction of the relevant side, so if tck = 1 grid lines are drawn. The default setting (tck = NA) is to use tcl = 0.5. 
tcl 
The length of tick marks as a fraction of the height of a line of text. The default value is 0.5; setting tcl = NA sets tck = 0.01 which is S' default. 
xlwd 
Xaxis line width. 
ylwd 
Yaxis line width. 
xcol 
Xaxis line color. 
ycol 
Yaxis line color. 
cex.xtitle 
Cex for xaxis title. 
cex.ytitle 
Cex for yaxis title. 
padj 
adjustment for each tick label perpendicular to the reading direction. For labels parallel to the axes, padj = 0 means right or top alignment, and padj = 1 means left or bottom alignment. This can be a vector given a value for each string, and will be recycled as necessary. 
hadj 
adjustment (see par("adj")) for all labels parallel (â€˜horizontalâ€™) to the reading direction. If this is not a finite value, the default is used (centring for strings parallel to the axis, justification of the end nearest the axis otherwise). 
xcex, ycex 
A numerical value giving the amount by which axis labels should be magnified relative to the default. 
xline, yline 
On which margin line of the plot the x & y labels must be
placed, starting at 0 counting outwards
(see 
xfont, yfont 
An integer which specifies which font to use for x & y
axes titles (see 
family, lty, bty, col, xlim, ylim, pch, las, mar, font 
Graphical parameters
(see 
cex 
A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. This starts as 1 when a device is opened, and is reset when the layout is changed, e.g. by setting mfrow. 
seed 
An integer used to set a 'seed' for random number generation. 
... 
Other graphical parameters to pass to functions:

Empirical and theoretical probabilities are estimated using the
quantiles generated with the margin quantile functions. Nonlinear fit of
margin distributions can be previously accomplished using any of the
functions fitCDF
, fitdistr
, or function
fitMixDist
for the case where the margins are mixture of
distributions. npoints random uniform and iid numbers from the
interval [0, 1] are generated and used to evaluate the quantile margin
distribution functions. Next, the quantiles are used to compute the
empirical and theoretical copulas, which will be used to estimate the
corresponding probabilities.
The PP plot and invisible temporary object with the information to build the graphic which can be assigned to a variable to use in further plots or analyses.
Robersy Sanchez (https://genomaths.com).
fitCDF
, fitdistr
,
fitMixDist
, and bicopulaGOF
.
set.seed(12) margins = c("norm", "norm") ## Random variates from normal distributions X < rlnorm(200, meanlog = 0.5, sdlog = 3.1) Y < rnorm(200, mean = 0, sd = 6) cor(X,Y) ## Correlation between X and Y parMargins = list( list(meanlog = 0.5, sdlog = 3.1), list(mean = 0, sd = 10)) copula = "normalCopula" npoints = 100 ## The information to build the graphic is stored in object 'g'. g < ppCplot(X = X, Y = Y, copula = "normalCopula", margins = margins, paramMargins = parMargins, npoints = 20)