pairsColList() creates a list containing
information about colors for a given matrix of (approximate aka
pairsRosenblatt() for visualizing a graphical
goodness-of-fit test based on pairwise Rosenblatt transformed data.pairsRosenblatt(cu.u, pvalueMat=pviTest(pairwiseIndepTest(cu.u)),
method = c("scatter", "QQchisq", "QQgamma",
"PPchisq", "PPgamma", "none"),
g1, g2, col = "B&W.contrast",
colList = pairsColList(pvalueMat, col=col),
main=NULL,
sub = gpviString(pvalueMat, name = "pp-values"),
panel = NULL, do.qqline = TRUE,
keyOpt = list(title="pp-value", rug.at=pvalueMat), ...)pairsColList(P, pdiv = c(1e-04, 0.001, 0.01, 0.05, 0.1, 0.5),
signif.P = 0.05, pmin0 = 1e-05, bucketCols = NULL,
fgColMat = NULL, bgColMat = NULL, col = "B&W.contrast",
BWcutoff = 170,
bg.col = c("ETHCL", "zurich", "zurich.by.fog", "baby",
"heat", "greenish"),
bg.ncol.gap = floor(length(pdiv)/3),
bg.col.bottom = NULL, bg.col.top = NULL, ...)
array of pairwise
Rosenblatt-transformed observations as returned by
pairwiseCcop().matrix of p-values (or pp-values).character indicating the plot method
to be used. Currently possible are:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Note: These methods merfunction from
$[0,1]^n\to[0,1]^n$
applied to "x" for plotting in one panel.function from
$[0,1]^{n\times 2}\to[0,1]^n$
applied to "y" for plotting in one panel.list of colors and information as
returned by pairsColList().method = "QQ....", specify if the plot
panels should also draw a qqline()..pairsCond() for
options for the key..pairsCond()
(for pairsRosenblatt())
and to heat_hcl() (for pairsColList;
used to generate the color palette), see Detailmatrix of p-values..pairsCond() which creates the
pairs-like goodness-of-fit plotpdiv).numeric indicating the lower endpoint of
the p-value buckets if pmin is zero. If set to 0, the lowest
value of the p-value buckets will also be 0. Note that pmin0 s
vector of length as pdiv containing the
colors for the buckets. If not specified, either
bg.col.bottom and bg.col.top are used (if provided) or
bg.comatrix with foreground colors (the
default will be black if the background color is bright
and white if it is dark; see also BWcutoff).matrix of background colors;
do not change this unless you know what you are doing.BWcutoff), passed to
.pairsCond(). If colList is not specified,
this color is ucol="B&W.contrast".signif.P (to make
significance/non-significance more visible).vector of length 3 containing a HCL
color specification. If bg.col.bottom is provided and
bucketCols is not, bg.col.bottom is used as the color
for the buckvector of length 3 containing a HCL
color specification. If bg.col.top is provided and
bucketCols is not, bg.col.top is used as the color for
the bucket of pairsRosenblatt() are passed to
.pairsCond(), these notably may include key, true
by default, which draws a color key for the colors used as panel
background encoding (pseudo) p-values. pairsColList() is basically an auxiliary function to specify
the colors used in the graphical goodness-of-fit test as
conducted by pairsRosenblatt(). The latter is described in
detail in Hofert and demo(gof_graph).
pairwiseCcop for the tools behind the scenes.
demo(gof_graph) for examples.## 2-dim example {d = 2} ===============
##
## "t" Copula with 22. degrees of freedom; and (pairwise) tau = 0.5
nu <- 2.2 # degrees of freedom
## Define the multivariate distribution
tCop <- ellipCopula("t", param=iTau(ellipCopula("t", df=nu), tau = 0.5),
dim=2, df=nu)
set.seed(19)
X <- qexp(rCopula(n = 400, tCop))
## H0 (wrongly): a Normal copula, with correct tau
copH0 <- ellipCopula("normal", param=iTau(ellipCopula("normal"), tau = 0.5))
## create array of pairwise copH0-transformed data columns
cu.u <- pairwiseCcop(pobs(X), copH0)
## compute pairwise matrix of p-values and corresponding colors
pwIT <- pairwiseIndepTest(cu.u, N=200) # (d,d)-matrix of test results
round(pmat <- pviTest(pwIT), 3) # pick out p-values
## .286 and .077
pairsRosenblatt(cu.u, pvalueMat= pmat)
### A shortened version of demo(gof_graph) -------------------------------
N <- 32 ## too small, for "testing"; realistically, use a larger one:
if(FALSE)
N <- 100
## 5d Gumbel copula ##########
n <- 250 # sample size
d <- 5 # dimension
family <- "Gumbel" # copula family
tau <- 0.5
set.seed(17)
## define and sample the copula (= H0 copula), build pseudo-observations
cop <- getAcop(family)
th <- cop@iTau(tau) # correct parameter value
copH0 <- onacopulaL(family, list(th, 1:d)) # define H0 copula
U. <- pobs(rCopula(n, cop=copH0))
## create array of pairwise copH0-transformed data columns
cu.u <- pairwiseCcop(U., copH0)
## compute pairwise matrix of p-values and corresponding colors
pwIT <- pairwiseIndepTest(cu.u, N=N, verbose=interactive()) # (d,d)-matrix of test results
round(pmat <- pviTest(pwIT), 3) # pick out p-values
## Here (with seed=1): no significant ones, smallest = 0.0603
## Plots ---------------------
## plain (too large plot symbols here)
pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".")
## with title, no subtitle
pwRoto <- "Pairwise Rosenblatt transformed observations"
pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=pwRoto, sub=NULL)
## two-line title including expressions, and centered
title <- list(paste(pwRoto, "to test"),
substitute(italic(H[0]:C~~bold("is Gumbel with"~~tau==tau.)),
list(tau.=tau)))
line.main <- c(4, 1.4)
pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".",
main=title, line.main=line.main, main.centered=TRUE)
## Q-Q plots -- can, in general, better detect outliers
pairsRosenblatt(cu.u, pvalueMat=pmat, method="QQchisq", cex=0.2)Run the code above in your browser using DataLab