Description Usage Arguments Details Value Author(s) References Examples
Fit Gaussian geostatistical variogram to the obtained spatial and temporal mark variograms
1 | FMvariogram(u, gmv)
|
u |
vector of spatial or temporal distance at which |
gmv |
vector containing the values of γ estimated through |
This function fit Gaussian geostatistical variogram to the obtained mark variogram through gsp
and gte
functions using the R package geoR
Ribeiro and Diggle (2001).
u |
If u is missing, a vector of distances u at which |
gmv |
The fitted Gaussian geostatistical variogram. |
Parameters |
The nugget effects, sill and range fitted for a Gaussian geostatistical variogram to the obtained spatial and temporal mark variograms. |
Francisco J. Rodriguez Cortes <cortesf@uji.es> https://fjrodriguezcortes.wordpress.com
Baddeley, A., Rubak, E., Turner, R. (2015). Spatial Point Patterns: Methodology and Applications with R. CRC Press, Boca Raton.
Chiu, S. N., Stoyan, D., Kendall, W. S., and Mecke, J. (2013). Stochastic Geometry and its Applications. John Wiley & Sons.
Gabriel, E., Rowlingson, B., Diggle P J. (2013) stpp
: an R package for plotting, simulating and analyzing Spatio-Temporal Point Patterns. Journal of Statistical Software, 53:1–29.
Gonzalez, J. A., Rodriguez-Cortes, F. J., Cronie, O. and Mateu, J. (2016). Spatio-temporal point process statistics: a review. Spatial Statiscts. 18:505-544.
Illian, J B., Penttinen, A., Stoyan, H. and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley and Sons, London.
Stoyan, D., Rodriguez-Cortes, F. J., Mateu, J. and Wilfried, G. (2016). Mark variograms for spatio-temporal point processes. Spatial Statistics, 20:125-147.
Ribeiro, Jr., Diggle, P.J. (2001). geoR
: A package for geostatistical analysis. R New 1, 2:14–18.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ## Not run:
#################
# A realisation of spatio-temporal double-cluster (ellipsoid) point processes
Xe <- stdcpp(lambp=20, a=0.12, b=0.09, c=0.07, mu=100)
plot(Xe$xyt)
# Spatial mark variogram function
outs <- gsp(Xe$xyt)
# R plot - Spatial mark variogram function
par(mfrow=c(1,1))
xl <- c(0,0.25)
yl <- c(min(0,outs$egsp,1/12),max(0,outs$egsp,1/12))
plot(outs$ds,outs$egsp,type="l",xlab="r = distance",ylab=expression(gamma[sp](r)),
xlim=xl,ylim=yl,col=1,cex.lab=1.5,cex.axis=1.5)
lines(outs$ds,rep(1/12,length(outs$ds)),col=11)
points(0,0,col=11,cex=1.5)
# Model parameters estimated "by least squares fit of empirical variograms"
fitv <- FMvariogram(outs$ds,outs$egsp)
fitv$Parameters
lines(fitv$u,fitv$gv)
# Temporal mark variogram function
outt <- gte(Xe$xyt)
# R plot - Temporal mark variogram function
par(mfrow=c(1,1))
xl <- c(0,0.25)
yl <- c(min(0,outt$egte,1/6),max(0,outt$egte,1/6))
plot(outt$dt,outt$egte,type="l",xlab="t = time",ylab=expression(gamma[te](t)),
xlim=xl,ylim=yl,col=1,cex.lab=1.5,cex.axis=1.5)
lines(outt$dt,rep(1/6,length(outt$dt)),col=11)
points(0,0,col=11,cex=1.5)
# Model parameters estimated "by least squares fit of empirical variograms"
fitv <- FMvariogram(outt$dt,outt$egte)
fitv$Parameters
lines(fitv$u,fitv$gv)
## End(Not run)
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