plot.transiogram {spMC}

Plot One-dimensional Transiograms

Description

The function makes a graphical representation of transition probabilities by the use of transiogram.

Usage

## S3 method for class 'transiogram'
plot(x, ..., main, legend = FALSE, ci = NULL)

Arguments

x

an object of the class transiogram, typically with the output of the function transiogram or predict.tpfit.

...

other arguments to pass to the function plot.

main

the main title (on top) whose font and size are fixed.

legend

a logical value; if TRUE, the legend is plot on the bottom.

ci

a numerical value in the interval (0, 1) denoting the confidence of the interval around transition probabilities. If NULL (by default), no confidence interval is plotted.

Details

Transiogram is a diagram which is drawn for a single pair of categories in the direction \phi. It shows the transition probabilities in the y-axis for some specific lags in the x-axis.

Confidence intervals are computed on the log odds of the transition probabilities. The approximation of the confidence bounds is based on the delta method applied on the logistic transformation.

Value

An image is produced on the current graphics device. No values are returned.

Author(s)

Luca Sartore [e-mail]

References

Carle, S. F., Fogg, G. E. (1997) Modelling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains. Mathematical Geology, 29(7), 891-918.

Li, W. (2007) Transiograms for Characterizing Spatial Variability of Soil Classes. Soil Science Society of America Journal, 71(3), 881-893.

Sartore, L. (2010) Geostatistical models for 3-D data. M.Phil. thesis, Ca' Foscari University of Venice.

See Also

tpfit, predict.tpfit, mixplot, image.multi_tpfit, plot

Examples

data(ACM)

# Estimate empirical transition 
# probabilities by points
ETr <- transiogram(ACM$MAT3, ACM[, 1:3], c(0, 0, 1), 100, 100)

# Estimate the transition rate matrix
RTm <- tpfit(ACM$MAT3, ACM[, 1:3], c(0, 0, 1))

# Compute transition probabilities 
# from the one-dimensional MC model
TPr <- predict(RTm, lags = ETr$lags)

# Plot empirical transition probabilities
plot(ETr, type = "l", ci = 0.99)

# Plot theoretical transition probabilities
plot(TPr, type = "l")