get_obs_dens {geocausal} | R Documentation |
Generate observed densities
Description
'get_obs_dens()' takes a hyperframe and returns observed densities. The output is used as propensity scores.
Usage
get_obs_dens(hfr, dep_var, indep_var, ngrid = 100, window)
Arguments
hfr |
hyperframe |
dep_var |
The name of the dependent variable. Since we need to obtain the observed density of treatment events, 'dep_var' should be the name of the treatment variable. |
indep_var |
vector of names of independent variables (covariates) |
ngrid |
the number of grid cells that is used to generate observed densities. By default = 100. Notice that as you increase 'ngrid', the process gets computationally demanding. |
window |
owin object |
Details
'get_obs_dens()' assumes the poisson point process model and calculates observed densities for each time period. It depends on 'spatstat.model::mppm()'. Users should note that the coefficients in the output are not directly interpretable, since they are the coefficients inside the exponential of the poisson model.
Value
list of the following: * 'indep_var': independent variables * 'coef': coefficients * 'intens_grid_cells': im object of observed densities for each time period * 'estimated_counts': the number of events that is estimated by the poisson point process model for each time period * 'sum_log_intens': the sum of log intensities for each time period
Examples
# Data
dat_out <- insurgencies[1:100, ]
dat_out$time <- as.numeric(dat_out$date - min(dat_out$date) + 1)
# Hyperframe
dat_hfr <- get_hfr(data = dat_out,
col = "type",
window = iraq_window,
time_col = "time",
time_range = c(1, max(dat_out$time)),
coordinates = c("longitude", "latitude"),
combine = TRUE)
# Covariates
dist_baghdad <- get_dist_focus(window = iraq_window,
lon = c(44.366), #Baghdad
lat = c(33.315),
resolution = 0.1,
mile = FALSE,
preprocess = FALSE)
dat_hfr$dist_bagh <- dist_baghdad
# Observed density
get_obs_dens(dat_hfr,
dep_var = "all_combined",
indep_var = c("dist_bagh"),
ngrid = 100,
window = iraq_window)