View source: R/pairsRosenblatt.R
pairsRosenblatt | R Documentation |
pairsColList()
creates a list
containing
information about colors for a given matrix of (approximate aka
“pseudo”) p-values. These colors are used in
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, ...)
cu.u |
|
pvalueMat |
|
method |
Note: These methods merely just set |
g1 |
|
g2 |
|
colList |
|
main |
title. |
sub |
sub-title with a smart default containing a global (p)p-value. |
panel |
a |
do.qqline |
if |
keyOpt |
argument passed to |
... |
additional arguments passed to |
P |
|
pdiv |
numeric vector of strictly increasing p-values in
(0,1) that determine the “buckets” for the background
colors of |
signif.P |
significance level (must be an element of |
pmin0 |
a Note that |
bucketCols |
|
fgColMat |
|
bgColMat |
|
col |
foreground color (defaults to "B&W.contrast" which switches
black/white according to |
BWcutoff |
number in (0, 255) for switching
foreground color if |
bg.col |
color scheme for the background colors. |
bg.ncol.gap |
number of colors left out as "gap" for
color buckets below/above |
bg.col.bottom |
|
bg.col.top |
|
Extra arguments 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 Mächler (2013).
See also demo(gof_graph)
.
pairsRosenblatt
: invisibly, the result of .pairsCond()
.
pairsColList
: a named list
with components
fgColMat
matrix
of foreground colors.
bgColMat
matrix
of background colors
(corresponding to P
).
bucketCols
vector
containing the colors
corresponding to pvalueBuckets
as described above.
pvalueBuckets
vector
containing the
endpoints of the p-value buckets.
Hofert, M. and Mächler, M. (2014) A graphical goodness-of-fit test for dependence models in higher dimensions; Journal of Computational and Graphical Statistics, 23(3), 700–716. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2013.812518")}
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), copula = 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., copula = 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)
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