pcamap: Generalized Principal Component Analysis and map

In tibo31/GeoXp: Interactive Exploratory Spatial Data Analysis

Generalized Principal Component Analysis and map

Description

The function pcamap() draws the plots summarizing a generalized Principal Component Analysis (PCA), made with genpca. It draws the scatterplot of the individuals projected on a chosen principal component plane (with their percentage of inertia), together with the scatterplot of the variables projected into the same plane with the quality of representation in order to interpret the principal component axes. The individuals scatterplot interacts with the map.

Usage

pcamap(sf.obj, names.var, direct = c(1, 2), weight = rep(1/nrow(sf.obj),
  length = nrow(sf.obj)), metric = diag(length(names.var)), 
  center = NULL, reduce = TRUE, qualproj = FALSE,  
  criteria = NULL, carte = NULL, identify = NULL, cex.lab = 0.8, pch = 16, 
  col = "lightblue3", xlab = paste(direct[1]), ylab = paste(direct[2]), 
  axes = FALSE, lablong = "", lablat = "")

Arguments

sf.obj	object of class sf
names.var	a vector of character; attribute names or column numbers in attribute table
direct	Two-dimension vector containing the numbers of principal axes to plot
weight	vector of size n of weight (by default : weight=t(1/n,...,1/n))
metric	matrix p \times p (by default : metric=Identity matrix)
center	A vector of size p of the gravity center which by default is equal to \bar X
reduce	if TRUE, reduced PCA
qualproj	if TRUE, print the quality of representation of individuals
criteria	a vector of boolean of size the number of Spatial unit, which permit to represent preselected sites with a cross, using the tcltk window
carte	matrix with 2 columns for drawing spatial polygonal contours : x and y coordinates of the vertices of the polygon
identify	if not NULL, the name of the variable for identifying observations on the map
cex.lab	character size of label
pch	a vector of symbol which must be equal to the number of group else all sites are printed in pch[1]
col	a vector of colors which must be equal to the number of group else all sites and all bars are printed in col[1]
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

Details

Let

D=diag(\lambda_1,...,\lambda_p)

1_p=(1,...,1)'

Let the coordinates of individuals in the principals components

CC=(C_1',...,C_n')'

with C_i=(C_i^1,...,C_i^p)
Let the coordinates of variables in the principals components

CC=(V_1',...,V_p')'

with V_i=(V_i^1,...,V_i^p)
Part of inertia :

(\frac{\lambda_1}{\sum_i\lambda_i},...,\frac{\lambda_p}{\sum_i\lambda_i})'

Quality of representation of individual k projected on plane (i,j):

Qu=\sqrt{\frac{(C_k^i)^2+(C_k^j)^2}{\sum_l(C_k^l)^2}}

Quality of representation of variable k projected on plane (i,j):

VQu=\sqrt{\frac{(V_k^i)^2+(V_k^j)^2}{\sum_l(V_k^l)^2}}

Value

In the case where user click on save results button, a list is created as a global variable in last.select object. obs, corresponds to the number of spatial units selected just before leaving the Tk window, inertia vector of size p with percent of inertia of each component, casecoord matrix n \times p of individuals, varcoord matrix n \times p of principal components.

Author(s)

Thomas-Agnan C., Aragon Y., Ruiz-Gazen A., Laurent T., Robidou L.

References

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.

Caussinus H., Fekri M., Hakam S., Ruiz-Gazen A. (2003) , A monitoring display of Multivariate Outliers, Computational Statistics and Data Analysis, vol. 44, 1-2, 237-252.

See Also

clustermap,genpca

Examples

###
# Data Colombus
require("robustbase")
# data columbus
require("sf")
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1])

# a basic PCA on 7 variables
pcamap(columbus, c(6:12), qualproj = TRUE, identify = "POLYID")

###
# data boston
boston <- st_read(system.file("shapes/boston_tracts.shp", package="spData")[1])

# a basic PCA on 7 variables
pcamap(boston, c(7:8, 10, 13:15), identify = "poltract", cex.lab = 0.5)

# generalized PCA : user have to construct a new metric and a vector
# of gravity center, by using for exampe covMcd
cov.boston <- robustbase::covMcd(boston.c[, c(7:8, 10, 13:15)], alpha = .75)
b.center <- cov.boston$center
b.cov <- cov.boston$cov

# example of use of pcamap
pcamap(boston, c(7:8, 10, 13:15), metric = b.cov, center = b.center,
   identify = "poltract", cex.lab = 0.5)

tibo31/GeoXp documentation built on April 8, 2023, 7:50 a.m.