aqp (version 1.18)

hzTransitionProbabilities: Horizon Transition Probabilities

Description

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

Usage

hzTransitionProbabilities(x, name, loopTerminalStates = FALSE)
genhzTableToAdjMat(tab)
mostLikelyHzSequence(mc, t0, maxIterations=10)

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.

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

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.

Details

Details and related tutorial pending...

See Also

generalize.hz

Examples

# NOT RUN {
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)
# }