tests/acceptance/test_acceptance__case_paris10km.R

In spflow: Spatial Econometric Interaction Models

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# Project: spflow - acceptance test
# Author: Lukas Dargel
# = = = = = = = = = = = = = = = = = = =
# Description:
#
# The script tests the user experience of the packages using the scenario of
# commuting flows in pars.
# = = = = = = = = = = = = = = = = = = =
# Date: Mai 2021

library("data.table")
library("spflow")
library("sf")
library("tibble")

# ---- step 1: default estimation ---------------------------------------------
# The first step should be successful creation of the required network objects,
# followed by the default estimate with minimal user input.
# Check that data can be data.frame, data.table and tibble.
data("paris10km_commuteflows")
data("paris10km_municipalities")
data("paris10km_neighborhood")

# data.frames
paris_net <- sp_network_nodes(
  network_id = "paris10km",
  node_neighborhood = paris10km_neighborhood[["by_contiguity"]],
  node_data = st_drop_geometry(paris10km_municipalities),
  node_key_column = "ID_MUN")

paris_pairs <- sp_network_pair(
  orig_net_id = "paris10km",
  dest_net_id = "paris10km",
  orig_key_column = "ID_ORIG",
  dest_key_column = "ID_DEST",
  pair_data = paris10km_commuteflows)

pairs_multi_net <- sp_multi_network(paris_net, paris_pairs)
default_formula <- log(COMMUTE_FLOW+1) ~ . + G_(log(DISTANCE + 1))
results_default <- spflow(default_formula, pairs_multi_net)

expect_inherits(results_default,"spflow_model")

# data.tables
paris_net_DT <- sp_network_nodes(
  network_id = "paris10km",
  node_neighborhood = paris10km_neighborhood[["by_contiguity"]],
  node_data = as.data.table(st_drop_geometry(paris10km_municipalities)),
  node_key_column = "ID_MUN")

paris_pairs_DT <- sp_network_pair(
  orig_net_id = "paris10km",
  dest_net_id = "paris10km",
  orig_key_column = "ID_ORIG",
  dest_key_column = "ID_DEST",
  pair_data = as.data.table(paris10km_commuteflows))

pairs_multi_net_DT <- sp_multi_network(paris_net_DT, paris_pairs_DT)
default_formula <- log(COMMUTE_FLOW+1) ~ . + G_(log(DISTANCE + 1))
results_default_DT <- spflow(default_formula, pairs_multi_net_DT)

expect_inherits(results_default_DT,"spflow_model")
rm(paris_net_DT, paris_pairs_DT, pairs_multi_net_DT, results_default_DT)

# tibbles
paris_net_tib <- sp_network_nodes(
  network_id = "paris10km",
  node_neighborhood = paris10km_neighborhood[["by_contiguity"]],
  node_data = as.data.table(st_drop_geometry(paris10km_municipalities)),
  node_key_column = "ID_MUN")

paris_pairs_tib <- sp_network_pair(
  orig_net_id = "paris10km",
  dest_net_id = "paris10km",
  orig_key_column = "ID_ORIG",
  dest_key_column = "ID_DEST",
  pair_data = as.data.table(paris10km_commuteflows))

pairs_multi_net_tib <- sp_multi_network(paris_net_tib, paris_pairs_tib)
default_formula <- log(COMMUTE_FLOW+1) ~ . + G_(log(DISTANCE + 1))
results_default_tib <- spflow(default_formula, pairs_multi_net_tib)

expect_inherits(results_default_tib,"spflow_model")
rm(paris_net_tib, paris_pairs_tib, pairs_multi_net_tib, results_default_tib)

# ---- step 2: high level estimation options ----------------------------------
# high level options are "model" and "use_intra" and "estimation_method"
model_options <- paste0("model_",1:9)
for (i in seq_along(model_options)){
  this_control <- spflow_control(model = model_options[i])
  this_model <- spflow(default_formula, pairs_multi_net,
                       flow_control = this_control)

  expect_inherits(this_model,"spflow_model",
                  info = paste0("default estimation for model_",
                                model_options[i]))
}

use_intra <- FALSE
estim_options <- c("mle","mcmc","s2sls","ols")
model_types <- c(rep("SDM",3),"SLO")
for (i in seq_along(estim_options)){
  this_control <- spflow_control(estimation_method = estim_options[i],
                                 use_intra = use_intra)
  this_model <- spflow(default_formula, pairs_multi_net,
                       flow_control = this_control)

  expect_inherits(this_model,"spflow_model",
                  info = paste0("default estimation for estimator",
                                estim_options[i]))
  expect_equal(this_model@estimation_control$model_type, model_types[i])
  expect_true(FALSE)
}

# ---- step 3: fine tuning the via the formula interface ----------------------
# use the formulas to adjust the variables to include in the model
stop("not implemented")

# ---- step 4: model comparisons ----------------------------------------------
# decide for the best model based on some criterion
stop("not implemented")

# ---- step 5: analyse the residuals ------------------------------------------
# compute the marginal effects and visualize them
stop("not implemented")

# ---- step 6: impact calculations --------------------------------------------
# compute the marginal effects and visualize them
stop("not implemented")

# ---- step 7: predict for new observations -----------------------------------
# compute the marginal effects and visualize them
stop("not implemented")