[spcopula-commits] r78 - / pkg pkg/R pkg/demo pkg/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jan 8 17:35:59 CET 2013
Author: ben_graeler
Date: 2013-01-08 17:35:59 +0100 (Tue, 08 Jan 2013)
New Revision: 78
Modified:
pkg/DESCRIPTION
pkg/NAMESPACE
pkg/R/asCopula.R
pkg/R/cqsCopula.R
pkg/R/empiricalCopula.R
pkg/R/returnPeriods.R
pkg/R/spCopula.R
pkg/R/spatialPreparation.R
pkg/R/utilities.R
pkg/demo/spCopula_estimation.R
pkg/man/dduCopula.Rd
pkg/man/dependencePlot.Rd
pkg/man/spCopula-class.Rd
pkg/man/unitScatter.Rd
pkg/man/vineCopula.Rd
spcopula_0.1-1.tar.gz
spcopula_0.1-1.zip
Log:
- changed arguments for dCopula and pCopula for the spatial and spatio-temporal copulas
- various cosmnetics
- use wrapper to C-functions from copula
Modified: pkg/DESCRIPTION
===================================================================
--- pkg/DESCRIPTION 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/DESCRIPTION 2013-01-08 16:35:59 UTC (rev 78)
@@ -5,10 +5,10 @@
Date: 2013-01-06
Author: Benedikt Graeler
Maintainer: Benedikt Graeler <ben.graeler at uni-muenster.de>
-Description: This package provides a framework to analyse spatial and spatio-temporal data provided in the format of the spacetime package with copulas. Additionally, support for calculating different multivariate return periods is implemented.
+Description: This package provides a framework to analyse via copulas spatial and spatio-temporal data provided in the format of the spacetime package. Additionally, support for calculating different multivariate return periods is implemented.
License: GPL-2
LazyLoad: yes
-Depends: copula (>= 0.999-5), spacetime (>= 1.0-2), CDVine, methods, lattice, R (>= 2.15.0)
+Depends: copula (>= 0.999-5), spacetime (>= 1.0-2), CDVine, methods, R (>= 2.15.0)
URL: http://r-forge.r-project.org/projects/spcopula/
Collate:
Classes.R
Modified: pkg/NAMESPACE
===================================================================
--- pkg/NAMESPACE 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/NAMESPACE 2013-01-08 16:35:59 UTC (rev 78)
@@ -1,8 +1,5 @@
-# useDynLib(spcopula)
+import(copula, spacetime, CDVine)
-import(copula, spacetime, CDVine, lattice)
-# importClassesFrom(spacetime, STFDF)
-
# constructor
export(asCopula, cqsCopula)
export(BB1Copula, surBB1Copula, r90BB1Copula, r270BB1Copula)
Modified: pkg/R/asCopula.R
===================================================================
--- pkg/R/asCopula.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/asCopula.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -85,35 +85,35 @@
})
setMethod("ddvCopula", signature("matrix", "asCopula"),ddvASC2)
-## random number generater
+## random number generator
# incorporating the inverse of the partial derivative that is solved numerically using optimize
## inverse partial derivative
invdduASC2 <- function (u, copula, y) {
- if (length(u)!=length(y))
- stop("Length of u and y differ!")
+ if (length(u)!=length(y))
+ stop("Length of u and y differ!")
+
+ a <- copula at parameters[1]
+ b <- copula at parameters[2]
- a <- copula at parameters[1]
- b <- copula at parameters[2]
-
# solving the cubic equation: u^3 * c3 + u^2 * c2 + u * c1 + c0 = 0
- usq <- u^2
- c3 <- (a-b)*(-3*usq+4*u-1)
- c2 <- (a-b)*(1-4*u+3*usq)+b*(- 1 + 2*u)
- c1 <- 1+b*(1-2*u)
- c0 <- -y
+ usq <- u^2
+ c3 <- (a-b)*(-3*usq+4*u-1)
+ c2 <- (a-b)*(1-4*u+3*usq)+b*(- 1 + 2*u)
+ c1 <- 1+b*(1-2*u)
+ c0 <- -y
-v <- solveCubicEq(c3,c2,c1,c0) # from cqsCopula.R
+ v <- solveCubicEq(c3,c2,c1,c0) # from cqsCopula.R
-filter <- function(vec){
- vec <- vec[!is.na(vec)]
- return(vec[vec >= 0 & vec <= 1])
+ filter <- function(vec) {
+ vec <- vec[!is.na(vec)]
+ return(vec[vec >= 0 & vec <= 1])
+ }
+
+ return(apply(v,1,filter))
}
-return(apply(v,1,filter))
-}
-
setMethod("invdduCopula", signature("numeric","asCopula","numeric"),invdduASC2)
## inverse partial derivative ddv
@@ -145,9 +145,13 @@
## random number generator
rASC2 <- function (n, copula) {
- u <- runif(n, min = 0, max = 1)
- y <- runif(n, min = 0, max = 1)
- return(cbind(u, invdduASC2(u, copula, y) ))
+ u <- runif(n, min = 0, max = 1)
+ y <- runif(n, min = 0, max = 1)
+
+ res <- cbind(u, invdduASC2(u, copula, y))
+ colnames(res) <- c("u","v")
+
+ return(res)
}
setMethod("rCopula", signature("numeric", "asCopula"), rASC2)
Modified: pkg/R/cqsCopula.R
===================================================================
--- pkg/R/cqsCopula.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/cqsCopula.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -188,9 +188,13 @@
## random number generator
rCQSec <- function (n, copula) {
- u <- runif(n, min = 0, max = 1)
- y <- runif(n, min = 0, max = 1)
- return(cbind(u, invdduCQSec(u, copula, y) ))
+ u <- runif(n, min = 0, max = 1)
+ y <- runif(n, min = 0, max = 1)
+
+ res <- cbind(u, invdduCQSec(u, copula, y))
+ colnames(res) <- c("u","v")
+
+ return(res)
}
setMethod("rCopula", signature("numeric","cqsCopula"), rCQSec)
Modified: pkg/R/empiricalCopula.R
===================================================================
--- pkg/R/empiricalCopula.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/empiricalCopula.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -35,22 +35,14 @@
## jcdf ##
# from package copula
pempCop.C <- function(u, copula) {
-# Cn(u, copula at sample, do.pobs=F, method="C") # preferred use instead of direct C-code from copula <=0.999-5
-
- # annoying hack, to be removed after release of copula 0.999-6 with the line above
- if("RmultCn" %in% names(getDLLRegisteredRoutines(getLoadedDLLs()[["copula"]][["path"]])[[1]])) {
- if(getDLLRegisteredRoutines(getLoadedDLLs()[["copula"]][["path"]])[[1]][["RmultCn"]]$numParameters == 6)
- .C("RmultCn", as.double(copula at sample), as.integer(nrow(copula at sample)),
- copula at dimension, as.double(u), as.integer(nrow(u)), as.double(u[,1]),
- PACKAGE="copula")[[6]]
- else
- .C("RmultCn", as.double(copula at sample), as.integer(nrow(copula at sample)),
- copula at dimension, as.double(u), as.integer(nrow(u)), as.double(u[,1]),
- as.double(0), PACKAGE="copula")[[6]]
- } else # copula > 0.999-5
- .C("Cn_C", as.double(copula at sample), as.integer(nrow(copula at sample)),
- copula at dimension, as.double(u), as.integer(nrow(u)), as.double(u[,1]),
- as.double(0), PACKAGE="copula")[[6]]
+ # r-forge hack, to be removed after release of copula 0.999-6 with the line above
+ if(length(formals(Cn))==2) {
+ return(Cn(copula at sample,u))
+ }
+ if (length(formals(Cn))== 5) {
+ return(Cn(u, copula at sample, do.pobs=FALSE, offset=0, method="C"))
+ }
+ stop(length(formals(Cn)))
}
setMethod("pCopula", signature("numeric", "empiricalCopula"),
Modified: pkg/R/returnPeriods.R
===================================================================
--- pkg/R/returnPeriods.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/returnPeriods.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -8,6 +8,7 @@
empKenFun <- function(tlevel) {
sapply(tlevel,function(t) sum(ken<=t))/nrow(sample)
}
+
return(empKenFun)
}
Modified: pkg/R/spCopula.R
===================================================================
--- pkg/R/spCopula.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/spCopula.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -233,38 +233,35 @@
# u
-# three column matrix providing the transformed pairs and their respective
+# two column matrix providing the transformed pairs and their respective
# separation distances
-pSpCopula <- function (u, copula) {
- if (!is.list(u) || !length(u)>=2) stop("Point pairs need to be provided with their separating distance as a list.")
+# h providing the separating distance(s)
+# block
+# block distances, pairs are assumed to be ordered block wise
+pSpCopula <- function (u, copula, h, block=1) {
+ if (missing(h)) stop("Point pairs need to be provided with their separating distance \"h\".")
- pairs <- u[[1]]
- if(!is.matrix(pairs)) pairs <- matrix(pairs,ncol=2)
- n <- nrow(pairs)
+ n <- nrow(u)
- if(length(u)==3) {
- block <- u[[3]]
- if (n%%block != 0) stop("The block size is not a multiple of the data length:",n)
- } else block <- 1
+ if (n%%block != 0) stop("The block size is not a multiple of the data length:",n)
- h <- u[[2]]
- if(length(h)>1 && length(h)!=nrow(u[[1]])) {
+ if(length(h)>1 && length(h)!=n) {
stop("The distance vector must either be of the same length as rows in the data pairs or a single value.")
}
if(is.null(copula at calibMoa(normalCopula(0),0))) {
- res <- spConCop(pCopula, copula, pairs, rep(h,length.out=nrow(pairs)))
+ res <- spConCop(pCopula, copula, u, rep(h,length.out=nrow(pairs)))
} else {
if(length(h)>1) {
if (block == 1){
ordering <- order(h)
# ascending sorted pairs allow for easy evaluation
- pairs <- pairs[ordering,,drop=FALSE]
+ u <- u[ordering,,drop=FALSE]
h <- h[ordering]
- res <- spDepFunCop(pCopula, copula, pairs, h)
+ res <- spDepFunCop(pCopula, copula, u, h)
# reordering the values
res <- res[order(ordering)]
@@ -272,55 +269,53 @@
res <- NULL
for(i in 1:(n%/%block)) {
res <- c(res, spDepFunCopSnglDist(pCopula, copula,
- pairs[((i-1)*block+1):(i*block),],
+ u[((i-1)*block+1):(i*block),],
h[i*block]))
}
}
} else {
- res <- spDepFunCopSnglDist(pCopula, copula, pairs, h)
+ res <- spDepFunCopSnglDist(pCopula, copula, u, h)
}
}
return(res)
}
-setMethod("pCopula", signature("list","spCopula"), pSpCopula)
+setMethod(pCopula, signature("numeric","spCopula"),
+ function(u, copula, ...) pSpCopula(matrix(u,ncol=2),copula, ...))
+setMethod(pCopula, signature("matrix","spCopula"), pSpCopula)
## spatial Copula density ##
# u
# three column matrix providing the transformed pairs and their respective
# separation distances
-dSpCopula <- function (u, copula, log=F) {
- if (!is.list(u) || !length(u)>=2) stop("Point pairs need to be provided with their separating distance as a list.")
+dSpCopula <- function (u, copula, log=F, h, block=1) {
+ if (missing(h)) stop("Point pairs need to be provided with their separating distance \"h\".")
- pairs <- u[[1]]
- n <- nrow(pairs)
+ n <- nrow(u)
- if(length(u)==3) {
- block <- u[[3]]
- if (n%%block != 0) stop("The block size is not a multiple of the data length:",n)
- } else block <- 1
+ if (n%%block != 0) stop("The block size is not a multiple of the data length:",n)
- h <- u[[2]]
- if(length(h)>1 && length(h)!=nrow(u[[1]])) {
+ if(length(h)>1 && length(h)!=n) {
stop("The distance vector must either be of the same length as rows in the data pairs or a single value.")
}
if(is.null(copula at calibMoa(normalCopula(0),0))){
- res <- spConCop(dCopula, copula, pairs, rep(h, length.out=nrow(pairs)),
+ res <- spConCop(dCopula, copula, u, rep(h, length.out=nrow(pairs)),
do.logs=log, log=log)
}
else {
+ cat("Yes \n")
if(length(h)>1) {
if (block == 1){
ordering <- order(h)
# ascending sorted pairs allow for easy evaluation
- pairs <- pairs[ordering,,drop=FALSE]
+ u <- u[ordering,,drop=FALSE]
h <- h[ordering]
- res <- spDepFunCop(dCopula, copula, pairs, h, do.logs=log, log=log)
+ res <- spDepFunCop(dCopula, copula, u, h, do.logs=log, log=log)
# reordering the values
res <- res[order(ordering)]
@@ -328,19 +323,21 @@
res <- NULL
for(i in 1:(n%/%block)) {
res <- c(res, spDepFunCopSnglDist(dCopula, copula,
- pairs[((i-1)*block+1):(i*block),],
+ u[((i-1)*block+1):(i*block),],
h[i*block], do.logs=log, log=log))
}
}
} else {
- res <- spDepFunCopSnglDist(dCopula, copula, pairs, h, do.logs=log, log=log)
+ res <- spDepFunCopSnglDist(dCopula, copula, u, h, do.logs=log, log=log)
}
}
return(res)
}
-setMethod(dCopula, signature("list","spCopula"), dSpCopula)
+setMethod(dCopula, signature("numeric","spCopula"),
+ function(u, copula, ...) dSpCopula(matrix(u,ncol=2), copula, ...))
+setMethod(dCopula, signature("matrix","spCopula"), dSpCopula)
## partial derivatives ##
Modified: pkg/R/spatialPreparation.R
===================================================================
--- pkg/R/spatialPreparation.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/spatialPreparation.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -100,30 +100,6 @@
return(neighbourhood(as.data.frame(lData), dists, SpatialPoints(spData), index))
}
-## testing ##
-# data(meuse)
-# coordinates(meuse) <- ~x+y
-# str(SpatialPoints(meuse))
-#
-# neighbourSet <- getNeighbours(meuse,size=5)
-#
-# str(neighbourSet at index)
-#
-# library(lattice)
-# spplot(neighbourSet,"zinc",col.regions=bpy.colors())
-
-#
-# as.data.frame(array(1:18,dim=c(2,3,3)))
-#
-# ?spplot
-#
-# array(matrix(runif(3*155),ncol=3),dim=c(155,3))
-#
-# names(neighb)
-#
-# ## transformation of the sample by local neighborhoods ##
-#
-
#############
## BINNING ##
#############
Modified: pkg/R/utilities.R
===================================================================
--- pkg/R/utilities.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/R/utilities.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -17,69 +17,42 @@
return(res)
}
-# strength of dependence scatterplot
-myPanel.smoothScatter <- function(x, y = NULL, nbin = 64, cuts = 255, bandwidth, colramp,
- transformation = function(x) x, pch = ".", cex = 1, col = "black", range.x, ..., raster = FALSE, subscripts) {
- if (missing(colramp))
- colramp <- function(x) rev(heat.colors(x))
- x <- as.numeric(x)
- y <- as.numeric(y)
- if(min(x)<0 | max(x)>1 | min(y) < 0 | max(y) > 1) {
- x <- rankTransform(x)
- y <- rankTransform(y)
- warning("At least one of the margins seems to exceed [0,1] and has been transformed using rankTransform.")
- }
- xy <- xy.coords(x, y)
- x <- cbind(xy$x, xy$y)[!(is.na(xy$x) | is.na(xy$y)), , drop = FALSE]
- if (nrow(x) < 1) return()
- map <- lattice:::.smoothScatterCalcDensity(x, nbin, bandwidth, range.x)
- xm <- map$x1
- ym <- map$x2
- dens <- map$fhat
- dens <- array(transformation(dens), dim = dim(dens))
- PFUN <- if (raster)
- panel.levelplot.raster
- else panel.levelplot
- PFUN(x = rep(xm, length(ym)), y = rep(ym, each = length(xm)),
- z = as.numeric(dens), subscripts = TRUE, at = seq(from = 0,
- to = 1.01 * max(dens), length = cuts + 2), col.regions = colramp(cuts + 1), ...)
-}
-
-dependencePlot <- function(formula=NULL, smpl, cuts=15, bandwidth=.075, transformation=function (x) x, ..., range.x=list(c(0,1),c(0,1))) {
- smpl <- as.data.frame(smpl)
- if(is.null(formula)) {
+##
+dependencePlot <- function(var=NULL, smpl, bandwidth=0.075,
+ main="Stength of dependece",
+ transformation=function (x) x, ...) {
+ if(is.null(var)) {
if (ncol(smpl)>2) {
- warning("smpl contains more than 2 columns and no formula is given. The first two columns are plotted.")
- smpl <- as.data.frame(smpl[,1:2])
+ smpl <- smpl[,1:2]
}
- colnames(smpl) <- c("u","v")
- formula <- u~v
+ } else {
+ smpl <- smpl[,var]
}
-
- xyplot(formula, smpl, panel=myPanel.smoothScatter,
- aspect="iso", xlim=c(0,1), ylim=c(0,1), cuts=cuts, bandwidth=bandwidth, transformation=transformation, range.x=range.x, ...)
+
+ smoothScatter(smpl,bandwidth=bandwidth, asp=1, xlim=c(0,1), ylim=c(0,1),
+ nrpoints=0, main=main,
+ transformation=transformation, ...)
}
##
-unitScatter <- function(formula=NULL, smpl, ...) {
- smpl <- as.data.frame(smpl)
- if(is.null(formula)) {
+unitScatter <- function(var=NULL, smpl, ...) {
+
+ if(is.null(var)) {
if (ncol(smpl)>2) {
- warning("smpl contains more than 2 columns and no formula is given. The first two columns are plotted.")
- smpl <- as.data.frame(smpl[,1:2])
+ smpl <- smpl[,1:2]
}
- colnames(smpl) <- c("u","v")
- formula <- v~u
+ } else {
+ smpl <- smpl[,var]
}
- for(variable in all.vars(formula)){
+ for(variable in var){
if( min(smpl[,variable])<0 | max(smpl[,variable])>1) {
smpl[,variable] <- rank(smpl[,variable])/(length(smpl[,variable])+1)
warning("The variable ",variable," seems to exceed [0,1] and has been transformed using the rank order transformation.")
}
}
- xyplot(formula, smpl, aspect="iso", xlim=c(0,1), ylim=c(0,1), ...)
+ plot(smpl, asp=1, xlim=c(0,1), ylim=c(0,1), ...)
}
univScatter <- function(formula=NULL, smpl) {
Modified: pkg/demo/spCopula_estimation.R
===================================================================
--- pkg/demo/spCopula_estimation.R 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/demo/spCopula_estimation.R 2013-01-08 16:35:59 UTC (rev 78)
@@ -59,8 +59,8 @@
spLoglik <- NULL
for(i in 1:length(bins$lags)) { # i <- 8
spLoglik <- c(spLoglik,
- sum(dCopula(list(u=bins$lagData[[i]], h=bins$lags[[i]][,3]),
- spCop,log=T)))
+ sum(dCopula(u=bins$lagData[[i]], spCop,log=T,
+ h=bins$lags[[i]][,3])))
}
plot(spLoglik, ylab="log-likelihood", xlim=c(1,11))
Modified: pkg/man/dduCopula.Rd
===================================================================
--- pkg/man/dduCopula.Rd 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/man/dduCopula.Rd 2013-01-08 16:35:59 UTC (rev 78)
@@ -44,7 +44,7 @@
# vs. conditional probabilities of an asymmetric copula given v
asGivenV <- ddvCopula(asCopSmpl[,c(2,1)],asCop)
-xyplot(asGivenU ~ asGivenV, asp=1)
+unitScatter(smpl=cbind(asGivenU, asGivenV))
normalCop <- normalCopula(.6)
normCopSmpl <- rCopula(100,normalCop)
@@ -56,7 +56,7 @@
# vs. conditional probabilities of a Gaussian copula given v
normGivenV <- ddvCopula(normCopSmpl[,c(2,1)],normalCop)
-xyplot(normGivenU ~ normGivenV)
+unitScatter(smpl=cbind(normGivenU, normGivenV))
}
\keyword{partial derivative}
Modified: pkg/man/dependencePlot.Rd
===================================================================
--- pkg/man/dependencePlot.Rd 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/man/dependencePlot.Rd 2013-01-08 16:35:59 UTC (rev 78)
@@ -7,32 +7,29 @@
Plots a kernel smoothed scatterplot of the provided rank-transformed sample. The work is done by the function \code{\link{panel.smoothScatter}}.
}
\usage{
-dependencePlot(formula = NULL, smpl, cuts = 15, bandwidth = 0.075, transformation = function(x) x, ..., range.x = list(c(0, 1), c(0, 1)))
+dependencePlot(var = NULL, smpl, bandwidth = 0.075, main="Stength of dependece", transformation = function(x) x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
- \item{formula}{
-the formula as in common lattice plotting
+ \item{var}{
+Column IDs or variable names to be used. If not provided, the first two columns will be used.
}
\item{smpl}{
-can be a two-column matrix holding the data, no formula is needed
+a matrix (two-columns at least) holding the data
}
- \item{cuts}{
-the cuts to change colors
-}
\item{bandwidth}{
the bandwidth passed to the smoothing kernel
}
+\item{main}{
+the tilte of the plot
+}
\item{transformation}{
a transformation passed to the kernel
}
\item{\dots}{
passed on to the function \code{\link{panel.smoothScatter}}
}
- \item{range.x}{
-passed on to the function \code{\link{panel.smoothScatter}}
}
-}
\details{
see \code{\link{panel.smoothScatter}}
}
@@ -43,11 +40,11 @@
Benedikt Graeler
}
\note{
-This is simple wrapper to \code{\link{panel.smoothScatter}}.
+This is simple wrapper to \code{\link{smoothScatter}}.
}
\seealso{
-\code{\link{panel.smoothScatter}}
+\code{\link{smoothScatter}}
}
\examples{
dependencePlot(smpl=rCopula(500,asCopula(c(-1,1))))
Modified: pkg/man/spCopula-class.Rd
===================================================================
--- pkg/man/spCopula-class.Rd 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/man/spCopula-class.Rd 2013-01-08 16:35:59 UTC (rev 78)
@@ -46,8 +46,8 @@
\examples{
data(spCopDemo) # data from demo(spcopula_estimation)
-dCopula(list(u=matrix(c(.3,.3,.7,.7),ncol=2),h=c(200,400)),spCop)
-pCopula(list(u=matrix(c(.3,.3,.7,.7),ncol=2),h=c(200,400)),spCop)
+# dCopula(u=matrix(c(.3,.3,.7,.7),ncol=2),spCop,h=c(200,400))
+pCopula(u=matrix(c(.3,.3,.7,.7),ncol=2),spCop,h=c(200,400))
}
\keyword{classes}
\keyword{spcopula}
Modified: pkg/man/unitScatter.Rd
===================================================================
--- pkg/man/unitScatter.Rd 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/man/unitScatter.Rd 2013-01-08 16:35:59 UTC (rev 78)
@@ -8,11 +8,11 @@
Plots a sample on the unit-square. If needed, it will transform the sample using the rank order transformation as in \code{\link{rankTransform}}.
}
\usage{
-unitScatter(formula = NULL, smpl, ...)
+unitScatter(var = NULL, smpl, ...)
}
\arguments{
- \item{formula}{
-A standard plotting formula to select the corresponding columns.
+ \item{var}{
+Column IDs or variable names to be used. If not provided, the first two columns will be used.
}
\item{smpl}{
The data set to be used.
@@ -30,7 +30,7 @@
\examples{
data(loss)
rt_loss <- rankTransform(loss[,1:2])
-unitScatter(loss~alae,rt_loss)
+unitScatter(smpl=rt_loss)
}
\keyword{ hplot }
Modified: pkg/man/vineCopula.Rd
===================================================================
--- pkg/man/vineCopula.Rd 2013-01-06 08:18:50 UTC (rev 77)
+++ pkg/man/vineCopula.Rd 2013-01-08 16:35:59 UTC (rev 78)
@@ -32,9 +32,7 @@
\examples{
vine <- vineCopula(list(frankCopula(.7), gumbelCopula(3), gumbelCopula(1)),
3, "c-vine")
-
-if(require(lattice))
- cloud(V1~V2+V3, as.data.frame(rCopula(500,vine)))
+\dontrun{cloud(V1~V2+V3, as.data.frame(rCopula(500,vine)))}
}
\keyword{ mulitvariate }
\keyword{ distribution }
Modified: spcopula_0.1-1.tar.gz
===================================================================
(Binary files differ)
Modified: spcopula_0.1-1.zip
===================================================================
(Binary files differ)
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