R/nd_nhpp.R
nd_nhpp.Rd
Estimate the nonhomgogenous poisson process intensity function from grouped data using a normal kernel
nd_nhpp(distances_col, id_col, data = NULL, mu_0 = 0, kappa_0 = 1, nu_0 = 1, sigma_0 = 1, L = 4, K = 4, a_alpha = 1, b_alpha = 1, a_rho = 1, b_rho = 1, iter_max, warm_up, thin = 1, seed = NULL)
distances_col | name of column in data that contains distances |
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id_col | name of column in data that contains id grouping variable |
data | data.frame object that contains grouped distances |
mu_0 | mean hyperparameter for mu normal base measure. Default is 0; Normal(0,1). |
kappa_0 | variance hyperparameter for mu normal base measure. Default is 1; Normal(0,1). |
nu_0 | df hyperparameter for sigma inv chisq base measure. Default is 1; InvChisq(1,1); |
sigma_0 | scale hyperparameter for sigma inv chisq base measure. Default is 1; InvChisq(1,1); |
L | component truncation number |
K | intensity cluster truncation number |
a_alpha | shape hyperparameter for alpha gamma prior |
b_alpha | scale hyperparameter for alpha gamma prior |
a_rho | shape hyperparameter for rho gamma prior |
b_rho | scale hyperparameter for rho gamma prior |
iter_max | total number of iterations for which to run sampler |
warm_up | number of iterations for which to burn-in or "warm-up" sampler |
thin | number of iterations to thin by |
seed | integer with which to initialize random number generator |