ccost: Cost function of 2 alternative classifications of rows in...
In dave: Functions for "Data Analysis in Vegetation Ecology"

Description Usage Arguments Details Value Author(s) References Examples

Given 2 alternative classifications (g groups) of rows in a data frame of vegetation data, confusion matrix, C, is derived first. Using the first classification a matrix of row centroids is derived (using function centroid) of wich a g by g distance matrix, W, is computed (correlation transformed to distance). Cost factor, cf, is the sum of element by element multiplication of C and W respectively, cf=sum(CW).

ccost(veg, oldgr, newgr, y,...)
ccost2(veg,oldgr, newgr, y)

## Default S3 method:
ccost(veg, oldgr, newgr, y,...)
## S3 method for class 'ccost'
print(x,...)

`veg`	A data frame of vegetation releves (rows) by species (columns)
`oldgr`	Initial classification, e.g., derived by hclust()
`newgr`	Final classification, e.g., result of a model
`y`	Transformation of species scores: x'= x exp(y)
`x`	An object of class "ccost"
`...`	Further variables used for printing

Cost factor cf has range 0 (both classifications identical) to n (number of rows), where n is the worst case of misclassification.

An output list of class "ccost" with at least the following intems:

`dimension`	Dimension of confusion matrix (n by n)
`ccost`	Cost factor, cf
`old.groups`	Initial classification
`new.groups`	Final classification
`conf.matrix`	Confusion matrix
`weight.matrix`	Weigth matrix
`transf`	Transformation applied to scores, y-value

Otto Wildi

Ripley, B. D. 1996. Pattern recognition and neural networks. Cambridge: Cambridge University Press.

Venables, W. N. & Ripley, B. D. 2010. Modern applied statistics with S. Fourth Edition. Springer, NY.

Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston.

# First, groups of releves are formed by cluster analysis
require(vegan)
dr<- vegdist(nveg^0.5,method="bray")      # dr is distance matrix of rows
o.clr<- hclust(dr,method="ward")          # this is clustering
oldgr<- cutree(o.clr,k=3)                 # 3 row groups formed
oldgr                                     # this displays initial classification:
# 2  4  6  9 10 18 25 27 39 49 50
# 1  2  1  3  2  3  1  2  3  1  3

# For simplicity we assume that row "2" and "50" change memebership:
newgr<- c(2,2,1,3,2,3,1,2,3,1,1)
o.ccost<- ccost(nveg,oldgr,newgr,y=0.5)           # does square root transformation
# Default method releasing cf
o.ccost                                           # displays C and W (see above)