angleplotmap {GeoXp} | R Documentation |
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.
angleplotmap(long, lat, var, quantiles = NULL, listvar = NULL, listnomvar = NULL, criteria = NULL, carte = NULL, label = "", symbol = 0, labvar = "", lablong = "", lablat = "", axis = FALSE)
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 |
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.
A matrix of boolean of size $n times n$. TRUE if pair of sites was in the last selection
Thomas-Agnan Christine, Aragon Yves, Ruiz-Gazen Anne, Laurent Thibault, Robidou Laurianne
Aragon Yves, Perrin Olivier, Ruiz-Gazen Anne, Thomas-Agnan Christine (2008), ``Statistique et Econométrie pour données géoréférencées : modèles et études de cas''
# 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)