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

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

Details

Details and related tutorial pending...

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.

Note

These functions are still experimental and subject to change.

See also

generalize.hz

Examples

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)
## ---------------------------------------------