hzTransitionProbabilities: Horizon Transition Probabilities
In aqp: Algorithms for Quantitative Pedology

Description Usage Arguments Details Value Note Author(s) See Also Examples

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

hzTransitionProbabilities(x, name, loopTerminalStates = FALSE)

genhzTableToAdjMat(tab)

mostLikelyHzSequence(mc, t0, maxIterations=10)

`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.
`tab`	A cross-tabulation relating original horizon designations to new, generalized horizon designations.
`mc`	A `markovchain` object, initialized from a horizon sequence transition probability matrix with looped terminal states.
`t0`	Time-zero: a label describing an initial state within a `markovchain` object.
`maxIterations`	the maximum number of iterations when search for the most-likely horizon sequence

Details and related tutorial pending...

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.

These functions are still experimental and subject to change.

D.E. Beaudette

generalize.hz

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

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


## Not run: 
## 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)

## End(Not run)