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Create a spatial interaction models data frame from a collection of interaction models

Source: R/sim_df.R
sim_df.Rd

This function build a data frame from a collection of spatial interaction models. The data frame has a list column sim of type sim_list which stores the collection of models and classical columns that contain characteristics of the models. The name of the list column can be set to something else than sim (but not a name used by other default columns). See details for the default columns.

Usage

sim_df(x, sim_column = "sim")

Arguments

x

a collection of spatial interaction models, an object of class sim_list

sim_column

the name of the sim_list column (default "sim")

Value

a data frame representation of the spatial interaction model collection with classes sim_df and data.frame

Details

The data frame has one row per spatial interaction model and the following columns:

  • sim (default name): the last column that contains the models

  • alpha: the return to scale parameter used to build the model

  • beta: the cost inverse scale parameter used to build the model

  • diversity: model default diversity() (Shannon's diversity)

  • iterations: the number of iterations used to produce the model (1 for a static model)

  • converged: TRUE is the iterative calculation of the model converged (for models produced by blvim() and related approaches), FALSE for no convergence and NA for static models

The resulting object behaves mostly like a data.frame and support standard extraction and replacement operators. The object tries to keep its sim_df class during modifications. In particular, names<-.sim_df() tracks name change for the sim_list column. If a modification or an extraction operation changes the type of the sim_list column or drops it, the resulting object is a standard data.frame. See [.sim_df and names<-.sim_df() for details.

Examples

distances <- french_cities_distances[1:10, 1:10] / 1000 ## convert to km
production <- rep(1, 10)
attractiveness <- log(french_cities$area[1:10])
all_flows <- grid_blvim(distances, production, seq(1.05, 1.45, by = 0.2),
  seq(1, 3, by = 0.5) / 400,
  attractiveness,
  bipartite = FALSE,
  epsilon = 0.1, iter_max = 1000,
)
all_flows_df <- sim_df(all_flows)
all_flows_df$converged
#>  [1]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
#> [13]  TRUE  TRUE FALSE
## change the name of the sim column
names(all_flows_df)[6] <- "models"
## still a sim_df
class(all_flows_df)
#> [1] "sim_df"     "data.frame"
## get the models
sim_column(all_flows_df)
#> Collection of 15 spatial interaction models with 10 origin locations and 10
#> destination locations computed on the following grid:
#> • alpha: 1.05, 1.25, and 1.45
#> • beta: 0.0025, 0.00375, 0.005, 0.00625, and 0.0075