angleplotmap {GeoXp}R Documentation

Detection of an eventual directional trend

Description

The function `angleplotmap' is used to detect an eventual directional trend associated to variable $var$. It represents the absolute difference between the value of $var$ at two sites as a function of the angle between vector $overrightarrow{s_is_j}$ and the x-axis.

Usage

angleplotmap(long, lat, var, quantiles = NULL, listvar = NULL,
listnomvar = NULL, criteria = NULL, carte = NULL, label = "",
symbol = 0, labvar = "", lablong = "", lablat = "", axis = FALSE)

Arguments

long a vector $x$ of size $n$
lat a vector $y$ of size $n$
var a vector of numeric values of size $n$
quantiles list of values of quantile orders (the regression quantile is obtained by spline smoothing)
listvar matrix of variables
listnomvar names of variables $listvar$
criteria a vector of size n of boolean with TRUE on specific sites (these for non interactive selection)
carte matrix with 2 columns for drawing spatial polygonal contours : $x$ and $y$ coordinates of the vertices of the polygon
label vector of character of size $n$ with names of sites
symbol 0 (by default) or 1, choice of representation of selected points. If 0, selected points are circles, if 1, selected points are stars
labvar name of $var$
lablong name of the x-axis that will be printed on the map
lablat name of the y-axis that will be printed on the map
axis a boolean with TRUE for drawing axes on the map

Details

For each couple of sites $(s_i,s_j)$, the graphic represents on the y-axis the absolute difference between $var_i$ and $var_j$ :

D_{ij}=|var_i-var_j|


and on the x-axis the angle $theta_{ij}$ between the vector $overrightarrow{s_is_j}$ and the x-axis. Possibility to represent a smoothing spline regression quantile $g_α$. For $0<α<1$,

Pr[D_{ij}<g_α(theta{ij})]=α

If that case, only the pair of sites $(s_i,s_j)$ verifying :

D_{ij}>g_{max(α)}(theta{ij})

are represented.

Value

A matrix of boolean of size $n times n$. TRUE if pair of sites was in the last selection

Author(s)

Thomas-Agnan Christine, Aragon Yves, Ruiz-Gazen Anne, Laurent Thibault, Robidou Laurianne

References

Aragon Yves, Perrin Olivier, Ruiz-Gazen Anne, Thomas-Agnan Christine (2008), ``Statistique et Econométrie pour données géoréférencées : modèles et études de cas''

See Also

variocloudmap,driftmap

Examples

# Data  afcon
data(afcon)
africa <- readOGR(system.file("vectors/Africa.MIF", package = "GeoXp")[1],
"Africa")

africa.contour<-spdf2list(africa)$poly

obs<-angleplotmap(afcon$x,afcon$y,afcon$totcon,listvar=afcon,
listnomvar=names(afcon),label=afcon$name,
criteria=(afcon$totcon>mean(afcon$totcon)),carte=africa.contour)

# Data Meuse
data(meuse.all)
data(meuse.riv)
obs<-angleplotmap(meuse.all$x,meuse.all$y,meuse.all$copper,
lablong="X",lablat="Y",quantiles=c(0.1,0.5,0.95),
listvar=meuse.all,listnomvar=names(meuse.all),
labvar="Concentration en plomb (en ppm)")
#points(meuse.riv, type = "l", asp = 1)

[Package GeoXp version 1.2 Index]