angleplotmap: Detection of an eventual directional trend
In GeoXp: Interactive exploratory spatial data analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

The function angleplotmap() is used to detect an eventual directional trend associated to variable name.var. It represents the absolute difference between the value of name.var at two sites as a function of the angle between vector vector(s_is_j) and the x-axis.

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angleplotmap(sp.obj, name.var, quantiles=TRUE, names.attr=names(sp.obj), 
criteria=NULL, carte=NULL, identify=FALSE, cex.lab=0.8, pch=16, col="lightblue3",
xlab="angle", ylab="absolute magnitude", axes=FALSE, lablong="", lablat="")

`sp.obj`	object of class extending Spatial-class
`name.var`	a character; attribute name or column number in attribute table
`quantiles`	a boolean to represent the Additive Quantile Regression Smoothing
`names.attr`	a vector of character: names to use in panel (if different from the names of variable used in sp.obj)
`criteria`	a vector of boolean which permit to represent preselected sites with a cross, using the tcltk window (must be equal to the number of spatial units)
`carte`	matrix with 2 columns for drawing spatial polygonal contours : x and y coordinates of the vertices of the polygon
`identify`	if not FALSE, identify plotted objects (currently only working for points plots). Labels for identification are the row.names of the attribute table row.names(as.data.frame(sp.obj)).
`cex.lab`	character size of label
`pch`	16 by default, symbol for selected points
`col`	"lightblue3" by default, color of bars on the cloud map
`xlab`	a title for the graphic x-axis
`ylab`	a title for the graphic y-axis
`axes`	a boolean with TRUE for drawing axes on the map
`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

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=abs(var_i-var_j)

and on the x-axis the angle theta_ij between vector(s_is_j) and the x-axis. Possibility to represent a smoothing spline regression quantile g_alpha. For 0<alpha<1,

Pr[D_ij<g_alpha(theta_ij)]=alpha

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

D_ij>g_max(alpha)(theta_ij)

are represented.

In the case where user click on save results button, a matrix of integer is created as a global variable in last.select object. It corresponds to the numbers of spatial unit corresponding to couple of sites selected just before leaving the Tk window.

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

Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan (2012), GeoXp: An R Package for Exploratory Spatial Data Analysis. Journal of Statistical Software, 47(2), 1-23.

Roger S.Bivand, Edzer J.Pebesma, Virgilio Gomez-Rubio (2009), Applied Spatial Data Analysis with R, Springer.

variocloudmap,driftmap

######
# data Conflicts Africa
data(afcon)

# afcon is a data.frame object. We have to create
# a Spatial object, by using first the longitude and latitude
# to create Spatial Points object ...
afcon.sp = SpatialPoints(cbind(afcon$x,afcon$y))
# ... and then by integrating other variables to create SpatialPointsDataFrame
afcon.spdf = SpatialPointsDataFrame(afcon.sp, afcon)
# For more details, see vignette('sp', package="sp")

# optional : we add some contours that don't correspond to the spatial unit
# but are nice for mapping
require("maptools")
africa <- readShapePoly(system.file("shapes/Africa.shp", package = "GeoXp")[1])
africa.contour<-spdf2list(africa)$poly

# A basic call of histomap function
angleplotmap(afcon.spdf,"totcon", carte= africa.contour,
identify=TRUE, cex.lab=0.6)


#####
# Data Meuse
data(meuse)

# meuse is a data.frame object. We have to create
# a Spatial object, by using first the longitude and latitude
# to create Spatial Points object ...
meuse.sp = SpatialPoints(cbind(meuse$x,meuse$y))
# ... and then by integrating other variables to create SpatialPointsDataFrame
meuse.spdf = SpatialPointsDataFrame(meuse.sp, meuse)

# meuse.riv is used for contour plot
data(meuse.riv)

angleplotmap(meuse.spdf,"copper",
col="green",quantiles=TRUE, cex.lab=0.7,
xlab="Concentration in plomb (in ppm)",pch=7,carte=meuse.riv[c(21:65,110:153),])