Datasets: example algorithm outputs

These functions load example outputs from key patter functions. They are included in the package to streamline function examples and tests.

dat_path()

dat_coa()

dat_pff()

dat_pfb()

dat_tff()

Source

For full details on the algorithm parameters used to generate these datasets, see https://github.com/edwardlavender/patter/blob/main/data-raw/003-add-data-algorithms.R.

Value

The functions return a dataset or a character string that defines the path to a dataset. See the corresponding function documentation for full details.

Details

• dat_path() reads an example output from sim_path_walk();

• dat_coa() reads an example output from coa();

• dat_pff() reads an example output from a forward run of pf_filter();

• dat_pfb() reads an example output from a backward run of pf_filter();

• dat_tff() reads an example output from pf_smoother_two_filter();

See also

Particle filters and smoothers sample states (particles) that represent the possible locations of an individual through time, accounting for all data and the individual's movement.

• To simulate artificial datasets, see sim_*() functions (especially sim_path_walk(), sim_array() and sim_observations()).

• To assemble real-world datasets for the filter, see assemble_*() functions.

• pf_filter() runs the filter:

  • For state types, see State;

  • For observation models, see ModelObs;

  • For movement models, see ModelMove;

• To run particle smoothing, use pf_smoother_two_filter().

• To map emergent patterns of space use, use a map_*() function (such as map_pou(), map_dens() and map_hr()).

Author

Edward Lavender

Examples

# Load inbuilt datasets
dat_path() |> summary()
#>     path_id     timestep        timestamp                     map_value     
#>  Min.   :1   Min.   :  1.00   Min.   :2016-01-01 00:00:00   Min.   : 13.81  
#>  1st Qu.:1   1st Qu.: 90.75   1st Qu.:2016-01-01 02:59:30   1st Qu.: 70.94  
#>  Median :1   Median :180.50   Median :2016-01-01 05:59:00   Median :103.07  
#>  Mean   :1   Mean   :180.50   Mean   :2016-01-01 05:59:00   Mean   :105.72  
#>  3rd Qu.:1   3rd Qu.:270.25   3rd Qu.:2016-01-01 08:58:30   3rd Qu.:138.64  
#>  Max.   :1   Max.   :360.00   Max.   :2016-01-01 11:58:00   Max.   :183.60  
#>        x                y          
#>  Min.   :708205   Min.   :6265168  
#>  1st Qu.:709215   1st Qu.:6268402  
#>  Median :710053   Median :6269311  
#>  Mean   :709972   Mean   :6268913  
#>  3rd Qu.:710777   3rd Qu.:6270004  
#>  Max.   :711345   Max.   :6270754  
dat_coa() |> summary()
#>     timestep      timestamp                     map_value            x         
#>  Min.   :1.00   Min.   :2016-01-01 00:00:00   Min.   : 74.88   Min.   :708742  
#>  1st Qu.:2.25   1st Qu.:2016-01-01 02:30:00   1st Qu.: 85.59   1st Qu.:709202  
#>  Median :3.50   Median :2016-01-01 05:00:00   Median : 98.15   Median :710019  
#>  Mean   :3.50   Mean   :2016-01-01 05:00:00   Mean   :103.15   Mean   :709772  
#>  3rd Qu.:4.75   3rd Qu.:2016-01-01 07:30:00   3rd Qu.:111.01   3rd Qu.:710240  
#>  Max.   :6.00   Max.   :2016-01-01 10:00:00   Max.   :151.03   Max.   :710609  
#>        y          
#>  Min.   :6266140  
#>  1st Qu.:6268791  
#>  Median :6269140  
#>  Mean   :6268864  
#>  3rd Qu.:6269756  
#>  Max.   :6270129  
dat_pff() |> summary()
#>             Length Class      Mode   
#> xinit       3      data.table list   
#> states      6      data.table list   
#> diagnostics 4      data.table list   
#> convergence 1      -none-     logical
dat_pfb() |> summary()
#>             Length Class      Mode   
#> xinit       3      data.table list   
#> states      6      data.table list   
#> diagnostics 4      data.table list   
#> convergence 1      -none-     logical
dat_tff() |> summary()
#>             Length Class      Mode   
#> xinit       0      -none-     NULL   
#> states      6      data.table list   
#> diagnostics 4      data.table list   
#> convergence 1      -none-     logical