View source: R/plot.transiogram.R
plot.transiogram | R Documentation |
The function makes a graphical representation of transition probabilities by the use of transiogram.
## S3 method for class 'transiogram'
plot(x, ..., main, legend = FALSE, ci = NULL)
x |
an object of the class |
... |
other arguments to pass to the function |
main |
the main title (on top) whose font and size are fixed. |
legend |
a logical value; if |
ci |
a numerical value in the interval (0, 1) denoting the confidence of the interval around transition probabilities. If |
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.
An image is produced on the current graphics device. No values are returned.
Luca Sartore drwolf85@gmail.com
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.
tpfit
, predict.tpfit
, mixplot
, image.multi_tpfit
, plot
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")
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