Description Usage Arguments Details Value Methods Author(s) References See Also Examples
Compute a series of clusterings (strides) by different methods and quantify their quality using a Fisher test.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | OptimStride(x, k, ft.threshold = 1e-3, alternative = "greater",
method = c("ward", "flexible", "pam", "kmeans", "wards",
"fanny", "FCM", "KM", "optpart"), fast = FALSE, ...)
## S4 method for signature 'VegsoupOptimstride'
method(x, ...)
## S4 method for signature 'VegsoupOptimstride'
stride(x, method, ...)
## S4 method for signature 'VegsoupOptimstride'
threshold(x, ...)
## S4 method for signature 'VegsoupOptimstride'
optimclass1(x, ...)
## S4 method for signature 'VegsoupOptimstride'
optimclass2(x, threshold = 2, ...)
## S4 method for signature 'VegsoupOptimstride'
getK(x)
## S4 method for signature 'VegsoupOptimstride'
peaks(x, ...)
## S4 method for signature 'VegsoupOptimstride'
which.max(x)
|
x |
for |
k |
maximum number of cluster to compute ( |
ft.threshold |
threshold value of Fisher test, defaults to a not very strict value of
|
alternative |
alternative indicates the alternative hypothesis of the Fisher exact test
and must be one of "two.sided", "greater" or "less" (see
|
threshold |
threshold value for OptimClass2. See ‘Details’ |
method |
any method supported by |
fast |
accelerate computations using package |
... |
additional arguments passed to |
The implementation follows the method of Tichy et al. (2010) and uses a
Fisher test (FisherTest
) to identify the number of
‘faithful’ species. This sensitivity of this statistic can be
controlled by setting a threshold value. Per default this value (argument
ft.threshold
) is set to it's lowest meaningful bound 1e-3
.
Partitioning/clustering methods, type of distance matrix and standardization
are taken from the input object. Generic methods for
'VegsoupOptimstride'
are detailed in the ‘Methods’ section.
Optimstride |
|
method |
character. The computed methods. |
stride |
list. A list with length equal to |
optimclass1, optimclass2 |
matrix with dimnames. Rows are methods and columns are the respective values
of |
peaks |
peaks of the curve. |
signature(obj = "VegsoupOptimstride")
:
retrieves the names of the computed method(s). These are the method arguments as accepted
by VegsoupPartition
.
signature(obj = "VegsoupOptimstride")
:
returns the number of faithful species for each partition and number of k.
signature(obj = "VegsoupOptimstride")
:
retrieves the threshold values set for the Fisher test.
signature(obj = "VegsoupOptimstride")
:
returns a matrix with the counts of faithful species, those achieving
ft.trshold
.
signature(obj = "VegsoupOptimstride")
:
the computed maximum value of k
, the length of each stride.
signature(obj = "VegsoupOptimstride")
:
the peaks along the stride.
signature(obj = "VegsoupOptimstride")
:
returns the number of k that coincides with the highest number of faithful
species, per method.
Roland Kaiser
Tichy, L., Chytry, M., Hajek, M., Talbot, S.S., and Botta-Dukat, Z. (2010). Optimclass: Using species-to-cluster fidelity to determine the optimal partition in classification of ecological communities. Journal of Vegetation Science, 21(2):287-299.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | require(vegsoup)
# a dummy example using simulated data
x <- coenoflex(50, 100)
x <- OptimStride(x, k = 10, method = c("ward", "flexible", "pam"))
summary(x)
# get the computed clustering methods
method(x)
# the threshold of the Fisher test
threshold(x)
# matrix of results for OptimClass1
optimclass1(x)
boxplot(t(optimclass1(x)))
# the number of faithful species for each partition and method
# warning, method dispatch will break if package optpart is loaded
stride(x)
stride(x, method = "flexible") # for method flexible
# plot method for class VegsoupOptimstride
plot(x)
plot(x, method = "flexible")
# get k where the curve peaks
peaks(x)
# get k with the maximum number of faithful species
which.max(x)
|
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