aqp (version 1.42)

hzTransitionProbabilities: Horizon Transition Probabilities

Description

Functions for creating and working with horizon (sequence) transition probability matrices.

Usage

hzTransitionProbabilities(x, name, loopTerminalStates = FALSE)

Value

The function hzTransitionProbabilities returns a square matrix of transition probabilities. See examples.

The function genhzTableToAdjMat returns a square adjacency matrix. See examples.

The function mostLikelyHzSequence returns the most likely sequence of horizons, given a markovchain object initialized from horizon transition probabilities and an initial state, t0. See examples.

Arguments

x

A SoilProfileCollection object.

name

A horizon level attribute in x that names horizons.

loopTerminalStates

should terminal states loop back to themselves? This is useful when the transition probability matrix will be used to initialize a markovchain object. See examples below.

Author

D.E. Beaudette

Details

See the following tutorials for some ideas:

horizon designation TP

http://ncss-tech.github.io/AQP/aqp/hz-transition-probabilities.html

soil color TP

http://ncss-tech.github.io/AQP/aqp/series-color-TP-graph.html

See Also

generalize.hz

Examples

data(sp4)
depths(sp4) <- id ~ top + bottom

# horizon transition probabilities: row -> col transitions
(tp <- hzTransitionProbabilities(sp4, 'name'))


if (FALSE) {
## plot TP matrix with functions from sharpshootR package
library(sharpshootR)
par(mar=c(0,0,0,0), mfcol=c(1,2))
plot(sp4)
plotSoilRelationGraph(tp, graph.mode = 'directed', edge.arrow.size=0.5)

## demonstrate genhzTableToAdjMat usage
data(loafercreek, package='soilDB')

# convert contingency table -> adj matrix / TP matrix
tab <- table(loafercreek$hzname, loafercreek$genhz)
m <- genhzTableToAdjMat(tab)

# plot
par(mar=c(0,0,0,0), mfcol=c(1,1))
plotSoilRelationGraph(m, graph.mode = 'directed', edge.arrow.size=0.5)


## demonstrate markovchain integration
library(markovchain)
tp.loops <- hzTransitionProbabilities(sp4, 'name', loopTerminalStates = TRUE)

# init new markovchain from TP matrix
mc <- new("markovchain", states=dimnames(tp.loops)[[1]], transitionMatrix = tp.loops)

# simple plot
plot(mc, edge.arrow.size=0.5)

# check absorbing states
absorbingStates(mc)

# steady-state:
steadyStates(mc)
}