fitmarkov {dave} | R Documentation |
Given a vegetation data frame considerd a time series with releves as rows and species as columns transition matrices are derived vor each time step based on some simple assumptions. These are averaged and a model series is derived trough scalar products. Time steps are given in a separate vector t. Missing steps are properly processed.
fitmarkov(veg, t, adjust = FALSE, ...)
rfitmarkov(veg, t, adjust)
## Default S3 method:
fitmarkov(veg, t, adjust = FALSE, ...)
## S3 method for class 'fitmarkov'
plot(x,...)
veg |
This is a vegetation data frame, releves are rows, species columns |
t |
The time step scale of length according with rows in x |
x |
An object of class "fitmarkov" |
adjust |
A logical vector adjusting the sum of species scores to 1.0. Default is adjust=FALSE |
... |
Vector colors of any length for line colors, vector widths for line widths. See example below. |
This method yields a possible solution for fitting a Markov series. The true process may be very different.
An output list of class "fitmarkov" with at least the following intems:
fitted.data |
The fitted time series' |
raw.data |
The input time series' |
transition.matrix |
The mean transition matrix' |
t.measured |
The time steps upon input where time steps may be missing' |
t.modeled |
The time steps upon output, no missing steps' |
The aim of this method is to provide a smooth curve based on input data. Because this relies on incomplete information, it is just one out of many solutions.
Otto Wildi
Orloci, L., Anand, M. & He, X. 1993. Markov chain: a realistic model for temporal coenosere? Biom. Praxim 33: 7-26.
Lippe, E., De Smitt, J.T. & Glenn-Lewin, D.C. 1985. Markov models and succession: a test from a heathland in the Netherlands. Journal of Ecology 73: 775-791.
Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston.
# data frame ltim is Lippe's data (see references)
# ltim just contains the time scale of the same
o.fm<- fitmarkov(lveg,ltim$Year)
plot(o.fm)