initialise_agents

creates the agent initial state including model weights, stochastically imputed ground floor areas

initialise_agents(sD, yeartime = 2015, cal_run = 10)

Arguments

sD

scenario (usable_roof_fraction only)

yeartime

start year (default 2010)

cal_run

calibration run number between 1 and 100

Value

a dataframe with columns

Examples

initialise_agents(sD,2015,10) #%>% system.time()
#> Joining with `by = join_by(q6)`
#> Joining with `by = join_by(qc2)`
#> Joining with `by = join_by(q1)`
#> Joining with `by = join_by(serial)`
#> Joining with `by = join_by(qh)`
#> Joining with `by = join_by(serial)`
#> # A tibble: 792 × 20
#>    serial w_q52 w_q13 w_theta region  house_type storeys construction_year   ber
#>    <chr>  <dbl> <dbl>   <dbl> <chr>   <chr>        <int>             <dbl> <dbl>
#>  1 71     0.771 0.918  1.15   Ulster… detached         2              2001  144.
#>  2 72     1.02  1.12   0.875  Dublin  semi_deta…       2              2001  121.
#>  3 74     1.41  1.09   0.698  Dublin  terraced         2              1945  264.
#>  4 75     1.44  1.17  -0.131  Dublin  apartment        2              2006  103.
#>  5 77     1.64  1.26   0.0954 Munster detached         2              1996  155.
#>  6 79     1.41  1.28   0.773  Munster detached         2              1995  250.
#>  7 81     1.01  1.16   1.22   Munster detached         1              1994  267.
#>  8 82     1.01  0.950  1.04   Ulster… detached         1              2006  228.
#>  9 84     0.762 1.03   0.508  Rest o… semi_deta…       2              2003  327.
#> 10 87     0.430 1.15   1.85   Rest o… detached         2              1983  515.
#> # ℹ 782 more rows
#> # ℹ 11 more variables: hli <dbl>, floor_area <dbl>, tech <chr>,
#> #   heating_install_time <dbl>, income <dbl>, fuel_allowance <lgl>, kW <dbl>,
#> #   q52 <dbl>, eac <dbl>, eac_actual <dbl>, eta <dbl>