A function to compute the conditional type-probabilities from a multivariate LGCP. See the vignette "Bayesian_lgcp" for a full explanation of this.
condProbs(obj)an lgcpPredictMultitypeSpatialPlusParameters object
an lgcpgrid object containing the consitional type-probabilities for each type
We suppose there are K point types of interest. The model for point-type k is as follows:
X_k(s) ~ Poisson[R_k(s)]
R_k(s) = C_A lambda_k(s) exp[Z_k(s)beta_k+Y_k(s)]
Here X_k(s) is the number of events of type k in the computational grid cell containing the point s, R_k(s) is the Poisson rate, C_A is the cell area, lambda_k(s) is a known offset, Z_k(s) is a vector of measured covariates and Y_i(s) where i = 1,...,K+1 are latent Gaussian processes on the computational grid. The other parameters in the model are beta_k , the covariate effects for the kth type; and eta_i = [log(sigma_i),log(phi_i)], the parameters of the process Y_i for i = 1,...,K+1 on an appropriately transformed (again, in this case log) scale.
The term 'conditional probability of type k' means the probability that at a particular location there will be an event of type k, which denoted p_k.
segProbs, postcov.lgcpPredictSpatialOnlyPlusParameters, postcov.lgcpPredictAggregateSpatialPlusParameters, postcov.lgcpPredictSpatioTemporalPlusParameters, postcov.lgcpPredictMultitypeSpatialPlusParameters, ltar, autocorr, parautocorr, traceplots, parsummary, textsummary, priorpost, postcov, exceedProbs, betavals, etavals