Description Usage Arguments Value Author(s) See Also Examples
This function fits a Marked IPPP (MIPPP) on a marked
point pattern by modeling the (joint)
intensity surface of the locations and the marks
using an IPPP for the marks (independent
of the locations) and an IPPP with mixture intensity
for the corresponding ground process, where the
mixture parameters depend on the mark value.
NOTE: The estimation procedure for continuous
marks will be implemented
in future versions of the sppmix
package.
For examples see
http://faculty.missouri.edu/~micheasa/sppmix/sppmix_all_examples.html#est_MIPPP_cond_mark
1 2 3 4 
pp 
Marked point pattern of class 
m 
A vector representing the number
of components to fit for the ground
process corresponding to each mark. Since in
real applications we don't know these numbers
we can specify an integer so that
the routine will fit a BDMCMC
with this 
L 
Number of iterations for the MCMC; default is 50000. 
burnin 
Number of initial realizations to discard. By default, it is 1/10 of the total number of iterations. 
hyper_da 
A list of hyperparameters for

hyper 
Hyperparameter for the mark distribution. Must be a vector of positive real numbers. If omitted the vector of one's is used. 
fit_markdist 
Logical variable requesting to fit and return the parameter estimates of the mark distribution. 
truncate 
Logical variable indicating whether or not we
we only work with events within the window defined
in the point pattern 
grayscale 
Logical to request plots in grayscale. 
discrete_mark 
Logical flag indicating whether the mark is discrete or not. For continuous marks set this to FALSE. 
LL 
Length of the side of the square grid. 
open_new_window 
Open a new window for a plot. 
show_plots 
Logical variable requesting to produce the ground fits and probability field plots for each mark. If label switching is present, the MAPE surface is computed and returned, otherwise the PME. 
compute_surfaces 
Logical to request computation of the Average of Surfaces (if 
An object of class MIPPP_fit
, which is simply a list containing the following components:
gen_mark_ps 
The posterior realizations of the discrete mark distribution probabilities. 
mark_dist 
The posterior means of the discrete mark distribution probabilities. 
discrete_mark 
Same logical flag as the input argument. 
pp 
Same as the input argument. 
ground_fits 
A List of objects of type 
ground_fitsAoS 
A List of objects of type 
post_surf 
A List of 
condition_on_loc 
Logical variable indicating the type of conditioning used in order to produce this MIPPP fit. For this function it is set to FALSE. 
fit_DAMCMC 
Logical variable indicating whether or not a DAMCMC or BDMCMC fit was requested. 
m 
Same as input. 
Sakis Micheas, Jiaxun Chen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  #Create a marked point pattern; use randomization and 3 discrete marks
newMPP=rMIPPP_cond_mark( params=c(.2,.5,.3),bigwin = spatstat::owin(c(10,10),c(10,10)))
newMPP$params
#supply the true number of components for each ground process
m=c(newMPP$groundsurfs[[1]]$m, newMPP$groundsurfs[[2]]$m, newMPP$groundsurfs[[3]]$m)
MIPPPfit=est_MIPPP_cond_mark(newMPP$genMPP,m=m,compute_surfaces=FALSE)
#check out the mark distribution parameters
#posterior means
MIPPPfit$mark_dist
#credible sets
GetStats(MIPPPfit$gen_mark_ps[,1])$CredibleSet#should contain .2
GetStats(MIPPPfit$gen_mark_ps[,2])$CredibleSet#should contain .5
GetStats(MIPPPfit$gen_mark_ps[,3])$CredibleSet#should contain .3
#now pretend we do not know the truth as is usually the case. Supply an integer
#for m so that the routine will fit a BDMCMC with this as the max number of
#components and use the MAP number of components
MIPPPfit=est_MIPPP_cond_mark(newMPP$genMPP,m=7,compute_surfaces=FALSE)
#check out the mark distribution parameters
MIPPPfit$mark_dist
GetStats(MIPPPfit$gen_mark_ps[,1])$CredibleSet#should contain .2
GetStats(MIPPPfit$gen_mark_ps[,2])$CredibleSet#should contain .5
GetStats(MIPPPfit$gen_mark_ps[,3])$CredibleSet#should contain .3

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.