Principal Component Analysis with respect to orthogonal instrumental variables
performs a Principal Component Analysis with respect to orthogonal instrumental variables.
pcaivortho(dudi, df, scannf = TRUE, nf = 2) ## S3 method for class 'pcaivortho' summary(object, ...)
dudi |
a duality diagram, object of class |
df |
a data frame with the same rows |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
object |
an object of class |
... |
further arguments passed to or from other methods |
an object of class 'pcaivortho' sub-class of class dudi
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
eig |
a vector with the all eigenvalues |
lw |
a numeric vector with the row weigths (from |
cw |
a numeric vector with the column weigths (from |
Y |
a data frame with the dependant variables |
X |
a data frame with the explanatory variables |
tab |
a data frame with the modified array (projected variables) |
c1 |
a data frame with the Pseudo Principal Axes (PPA) |
as |
a data frame with the Principal axis of |
ls |
a data frame with the projection of lines of |
li |
a data frame |
l1 |
a data frame with the Constraint Principal Components (CPC) |
co |
a data frame with the inner product between the CPC and Y |
param |
a data frame containing a summary |
Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr
Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya, A 26, 329–359.
Sabatier, R., Lebreton J. D. and Chessel D. (1989) Principal component analysis with instrumental variables as a tool for modelling composition data. In R. Coppi and S. Bolasco, editors. Multiway data analysis, Elsevier Science Publishers B.V., North-Holland, 341–352
## Not run:
data(avimedi)
cla <- avimedi$plan$reg:avimedi$plan$str
# simple ordination
coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3)
# within region
w1 <- wca(coa1, avimedi$plan$reg, scan = FALSE)
# no region the same result
pcaivnonA <- pcaivortho(coa1, avimedi$plan$reg, scan = FALSE)
summary(pcaivnonA)
# region + strate
interAplusB <- pcaiv(coa1, avimedi$plan, scan = FALSE)
if(adegraphicsLoaded()) {
g1 <- s.class(coa1$li, cla, psub.text = "Sans contrainte", plot = FALSE)
g21 <- s.match(w1$li, w1$ls, plab.cex = 0, psub.text = "Intra Région", plot = FALSE)
g22 <- s.class(w1$li, cla, plot = FALSE)
g2 <- superpose(g21, g22)
g31 <- s.match(pcaivnonA$li, pcaivnonA$ls, plab.cex = 0, psub.tex = "Contrainte Non A",
plot = FALSE)
g32 <- s.class(pcaivnonA$li, cla, plot = FALSE)
g3 <- superpose(g31, g32)
g41 <- s.match(interAplusB$li, interAplusB$ls, plab.cex = 0, psub.text = "Contrainte A + B",
plot = FALSE)
g42 <- s.class(interAplusB$li, cla, plot = FALSE)
g4 <- superpose(g41, g42)
G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
} else {
par(mfrow = c(2, 2))
s.class(coa1$li, cla, sub = "Sans contrainte")
s.match(w1$li, w1$ls, clab = 0, sub = "Intra Région")
s.class(w1$li, cla, add.plot = TRUE)
s.match(pcaivnonA$li, pcaivnonA$ls, clab = 0, sub = "Contrainte Non A")
s.class(pcaivnonA$li, cla, add.plot = TRUE)
s.match(interAplusB$li, interAplusB$ls, clab = 0, sub = "Contrainte A + B")
s.class(interAplusB$li, cla, add.plot = TRUE)
par(mfrow = c(1,1))
}
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