Description Details Author(s) References Examples
A collection of function accompaining the book "Data Analysis in Vegetation Ecology". These are mainly multivariate methods explained in the book but not found elsewhere. The package also includes all the data sets used in the book.
Package: | dave |
Type: | Package |
Version: | 2.0 |
Date: | 2017-10-10 |
License: | LGPL <= 2.0 |
The use of all functions included is explained in "Data Analysis in Vegetation Ecology" (see reference below). Version 2.0 includes various new data frames, sspft and ssind, plant functional types and indicator values respectively to be used in conjunction with sveg. Also new is a somewhat longer time series, sn7veg and sn7sit and the new "Vraconnaz" time series in vrveg and vrsit.
Otto Wildi, otto.wildi@wsl.ch
Wildi, O. 2013. Data Analysis in Vegetation Ecology. 2nd ed. Wiley-Blackwell, Chichester.
Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston.
1 2 3 4 5 6 7 8 9 | # A typical and probably the most complex function is Mtab() that re-arranges
# the rwos and columns within a vegetation data frame and through plotting it
# illustrates the presumably emerging pattern:
y.r<- 0.5 ; y.s<- 0.2 # defining transformations used
k.r <- 3 ; k.s <- 4 # row- and column numbers
ndiffs <- 18 # no. of columns used to show pattern
o.Mt<-Mtabs(nveg,"mulva" ,y.r,y.s,k.r,k.s,ndiffs)
plot(o.Mt,method="normal")
# to see the original order simply replace "mulva" by "raw"
|
Loading required package: labdsv
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
Loading required package: MASS
Loading required package: cluster
Attaching package: 'labdsv'
The following object is masked from 'package:stats':
density
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-4
Call:
Mtabs.default(veg = nveg, method = "mulva", y.r = y.r, y.s = y.s,
k.r = k.r, k.s = k.s, ndiffs = ndiffs)
CA eigenvalues, %: 52.82 16.61 9.78
AOC eigenvalues, %: 87.76 12.24 NA
Mean square contingency coefficient: 0.20763
Table split into 1 by 1 plots
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