Create a complete variability plot for spatial interaction models in a data frame

This function combines spatial variability interaction model representations similar to the ones produced by autoplot.sim_list() into a single ggplot. It provides an alternative graphical representation to the one produced by autoplot.sim_df() and by grid_autoplot() for collection of spatial interaction models in a sim_df object.

Usage

grid_var_autoplot(
  sim_df,
  key,
  flows = c("full", "destination", "attractiveness"),
  with_names = FALSE,
  with_positions = FALSE,
  cut_off = 100 * .Machine$double.eps^0.5,
  adjust_limits = FALSE,
  with_labels = FALSE,
  qmin = 0.05,
  qmax = 0.95,
  normalisation = c("origin", "full", "none"),
  fw_params = NULL,
  ...
)

Arguments

sim_df

a data frame of spatial interaction models, an object of class sim_df

key

the wrapping variable which acts as group identifier for spatial interaction models

flows

"full" (default), "destination" or "attractiveness", see details.

with_names

specifies whether the graphical representation includes location names (FALSE by default)

with_positions

specifies whether the graphical representation is based on location positions (FALSE by default)

cut_off

cut off limit for inclusion of a graphical primitive when with_positions = TRUE. In the attractiveness or destination representation, circles are removed when the corresponding upper quantile value is below the cut off.

adjust_limits

if FALSE (default value), the limits of the position based graph are not adjusted after removing graphical primitives. This eases comparison between graphical representations with different cut off value. If TRUE, limits are adjusted to the data using the standard ggplot2 behaviour.

with_labels

if FALSE (default value) names are displayed using plain texts. If TRUE, names are shown using labels.

qmin

lower quantile, see details (default: 0.05)

qmax

upper quantile, see details (default: 0.95)

normalisation

when flows="full", the flows can be reported without normalisation (normalisation="none") or they can be normalised, either to sum to one for each origin location (normalisation="origin", the default value) or to sum to one globally (normalisation="full").

fw_params

parameters for the ggplot2::facet_wrap call (if non NULL)

...

additional parameters passed to autoplot.sim_list()

Value

a ggplot object

Details

The rationale of autoplot.sim_df() is to display a single value for each spatial interaction model (SIM) in the sim_df data frame. On the contrary, this function produces a graphical representation of the variability of a partition of the SIMs in the data frame, using autoplot.sim_list() as the graphical engine.

The key parameter is used to partition the collection of SIMs. It can be any expression which can be evaluated in the context of the sim_df parameter. The function uses this parameter as the wrapping variable in a call to ggplot2::facet_wrap(). It also uses it as a way to specific a partition of the SIMs: each panel of the final figure is essentially the variability graph generated by autoplot.sim_list() for the subset of the SIMs in sim_df that match the corresponding value of key.

Parameters of ggplot2::facet_wrap() can be set using the fw_params parameter (in a list).

Examples

positions <- matrix(rnorm(10 * 2), ncol = 2)
distances <- as.matrix(dist(positions))
production <- rep(1, 10)
attractiveness <- c(2, rep(1, 9))
all_flows <- grid_blvim(distances, production, seq(1.05, 1.45, by = 0.1),
  seq(1, 3, by = 0.5),
  attractiveness,
  bipartite = FALSE,
  epsilon = 0.1, iter_max = 1000,
  destination_data = list(names = LETTERS[1:10], positions = positions),
  origin_data = list(names = LETTERS[1:10], positions = positions)
)
all_flows_df <- sim_df(all_flows)
## group models by iteration number
grid_var_autoplot(all_flows_df, iterations)

## or by convergence status (showing destination)
grid_var_autoplot(all_flows_df, converged, flow = "destination")

## using positions
grid_var_autoplot(all_flows_df, iterations,
  flow = "destination",
  with_positions = TRUE
)