View source: R/lgcpStructures.R
condProbs | R Documentation |
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)
obj |
an lgcpPredictMultitypeSpatialPlusParameters object |
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.
an lgcpgrid object containing the consitional type-probabilities for each type
segProbs, postcov.lgcpPredictSpatialOnlyPlusParameters, postcov.lgcpPredictAggregateSpatialPlusParameters, postcov.lgcpPredictSpatioTemporalPlusParameters, postcov.lgcpPredictMultitypeSpatialPlusParameters, ltar, autocorr, parautocorr, traceplots, parsummary, textsummary, priorpost, postcov, exceedProbs, betavals, etavals
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.