test.factors {EMMAgeo}  R Documentation 
This function performs eigenspace decomposition using the weighttransformed
matrix W to determine the explained variance with increasing number of
factors. Depending on the number of provided weight transformation limits
(l
) a vector or a matrix is returned.
test.factors(X, l, c, r.min = 0.95, plot = FALSE, legend, ...)
X 

l 

c 

r.min 

plot 

legend 

... 
Additional arguments passed to the plot function. Use

The results may be used to define a minimum number of endmembers for subsequent modelling steps, e.g. by using the Kaiser criterion, which demands a minimum number of eigenvalues to reach a squared R of 0.95.
List
with objects
L 
Vector or matrix of cumulative explained variance. 
q.min 
Vector with number of factors that passed r.min. 
Michael Dietze, Elisabeth Dietze
Dietze E, Hartmann K, Diekmann B, IJmker J, Lehmkuhl F, Opitz S, Stauch G, Wuennemann B, Borchers A. 2012. An endmember algorithm for deciphering modern detrital processes from lake sediments of Lake Donggi Cona, NE Tibetan Plateau, China. Sedimentary Geology 243244: 169180.
## load example data set
data(example_X)
## create sequence of weight transformation limits
l < seq(from = 0, to = 0.2, 0.02)
## perform the test and show q.min
L < test.factors(X = X, l = l, c = 100, plot = TRUE)
L$q.min
## a visualisation with more plot parameters
L < test.factors(X = X, l = l, c = 100, plot = TRUE,
ylim = c(0.5, 1), xlim = c(1, 7),
legend = "bottomright", cex = 0.7)
## another visualisation, a closeup
plot(1:7, L$L[1,1:7], type = "l",
xlab = "q", ylab = "Explained variance")
for(i in 2:7) {lines(1:7, L$L[i,1:7], col = i)}