test.factors {EMMAgeo} | R Documentation |
Calculate the initial cumulative explained variance of factors.
Description
This function performs eigenspace decomposition using the weight-transformed
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.
Usage
test.factors(X, l, c, r.min = 0.95, plot = FALSE, legend, ...)
Arguments
X |
|
l |
|
c |
|
r.min |
|
plot |
|
legend |
|
... |
Additional arguments passed to the plot function. Use
|
Details
The results may be used to define a minimum number of end-members 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.
Value
List
with objects
L |
Vector or matrix of cumulative explained variance. |
q.min |
Vector with number of factors that passed r.min. |
Author(s)
Michael Dietze, Elisabeth Dietze
References
Dietze E, Hartmann K, Diekmann B, IJmker J, Lehmkuhl F, Opitz S, Stauch G, Wuennemann B, Borchers A. 2012. An end-member algorithm for deciphering modern detrital processes from lake sediments of Lake Donggi Cona, NE Tibetan Plateau, China. Sedimentary Geology 243-244: 169-180.
Examples
## 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$q.min
## a visualisation with more plot parameters
L <- test.factors(X = X, l = l, c = 100, plot = TRUE,
ylim = c(0.5, 1), xlim = c(1, 7),
legend = "bottomright", cex = 0.7)
## another visualisation, a close-up
plot(1:7, L$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)}