Get selected Lorenz curve for distributional stats

Usage

pipgd_select_lorenz(
  params = NULL,
  welfare = NULL,
  weight = NULL,
  mean = 1,
  times_mean = 1,
  popshare = NULL,
  povline = ifelse(is.null(popshare), mean * times_mean, NA_real_),
  complete = getOption("pipster.return_complete")
)

Arguments

params: list of parameters from pipgd_validate_lorenz()
welfare: numeric vector of cumulative share of welfare (income/consumption)
weight: numeric vector of cumulative share of the population
mean: numeric scalar of distribution mean. Default is 1
times_mean: numeric factor that multiplies the mean to create a relative poverty line. Default is 1
popshare: numeric: range (0,1). Share of population. Provide share of population instead of poverty line
povline: numeric: value of poverty line. Default is the mean value
complete: logical: If TRUE, returns a list a cumulative returns from previously used get_gd functions. Default is FALSE

Value

Returns a list of values with the best lorenz fit given the distributional Stats.

Examples

# Example 1: Directly using welfare and weight vectors.
pipgd_select_lorenz(welfare = pip_gd$L,
                    weight = pip_gd$P)
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lb"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] FALSE
#> 
#> 

# Example 2: Specifying mean and poverty line.
custom_mean <- sum(pip_gd$W * pip_gd$X) / sum(pip_gd$W)
pipgd_select_lorenz(welfare = pip_gd$L,
                    weight = pip_gd$P,
                    mean = custom_mean,
                    povline = 1.25)
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lq"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] TRUE
#> 
#> 


# Example 3.1: Using parameters from pipgd_validate_lorenz()
validated_parameters <- pipgd_validate_lorenz(welfare = pip_gd$L,
                                              weight = pip_gd$P,
                                              complete = TRUE)
pipgd_select_lorenz(params = validated_parameters)
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lb"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] FALSE
#> 
#> 


# Example 3.2: Piping from from pipgd_params |> pipgd_validate_lorenz()
pipgd_params(welfare = pip_gd$L,
             weight = pip_gd$P) |>
pipgd_validate_lorenz(complete = TRUE)|>
pipgd_select_lorenz()
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lb"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] FALSE
#> 
#> 

# Example 4: Detailed output with complete = TRUE
pipgd_select_lorenz(welfare = pip_gd$L,
                    weight = pip_gd$P,
                    complete = TRUE)
#> $gd_params
#> $gd_params$lq
#> $gd_params$lq$reg_results
#> $gd_params$lq$reg_results$ymean
#> [1] 0.1219752
#> 
#> $gd_params$lq$reg_results$sst
#> [1] 0.08456216
#> 
#> $gd_params$lq$reg_results$coef
#>          A          B          C 
#>  0.8877478 -1.4514459  0.2026400 
#> 
#> $gd_params$lq$reg_results$sse
#> [1] 3.418058e-06
#> 
#> $gd_params$lq$reg_results$r2
#> [1] 0.9999596
#> 
#> $gd_params$lq$reg_results$mse
#> [1] 3.797842e-07
#> 
#> $gd_params$lq$reg_results$se
#> [1] 0.006673127 0.019034521 0.012827923
#> 
#> 
#> $gd_params$lq$key_values
#> $gd_params$lq$key_values$e
#> [1] -0.638942
#> 
#> $gd_params$lq$key_values$m
#> [1] -1.444296
#> 
#> $gd_params$lq$key_values$n
#> [1] 1.044219
#> 
#> $gd_params$lq$key_values$r
#> [1] 1.857124
#> 
#> $gd_params$lq$key_values$s1
#> [1] -0.2814192
#> 
#> $gd_params$lq$key_values$s2
#> [1] 1.004414
#> 
#> 
#> $gd_params$lq$validity
#> $gd_params$lq$validity$is_normal
#> [1] TRUE
#> 
#> $gd_params$lq$validity$is_valid
#> [1] TRUE
#> 
#> $gd_params$lq$validity$headcount
#> [1] 0.6284604
#> 
#> 
#> 
#> $gd_params$lb
#> $gd_params$lb$reg_results
#> $gd_params$lb$reg_results$ymean
#> [1] -2.496791
#> 
#> $gd_params$lb$reg_results$sst
#> [1] 10.98072
#> 
#> $gd_params$lb$reg_results$coef
#>         A         B         C 
#> 0.5613532 0.9309501 0.5800259 
#> 
#> $gd_params$lb$reg_results$sse
#> [1] 0.003204989
#> 
#> $gd_params$lb$reg_results$r2
#> [1] 0.9997081
#> 
#> $gd_params$lb$reg_results$mse
#> [1] 0.0003561098
#> 
#> $gd_params$lb$reg_results$se
#> [1] 0.014871578 0.005505620 0.006407669
#> 
#> 
#> $gd_params$lb$key_values
#> [1] NA
#> 
#> $gd_params$lb$validity
#> $gd_params$lb$validity$is_valid
#> [1] TRUE
#> 
#> $gd_params$lb$validity$is_normal
#> [1] TRUE
#> attr(,"label")
#> [1] "Normality with a mean of 1 and a poverty line of 1;1 times the mean."
#> 
#> $gd_params$lb$validity$headcount
#> [1] 0.6161877
#> 
#> 
#> 
#> 
#> $data
#> $data$welfare
#>  [1] 0.00208 0.01013 0.03122 0.07083 0.12808 0.23498 0.34887 0.51994 0.64270
#> [10] 0.79201 0.86966 0.91277 1.00000
#> attr(,"label")
#> [1] "Cumulative share of welfare"
#> 
#> $data$weight
#>  [1] 0.0092 0.0339 0.0850 0.1640 0.2609 0.4133 0.5497 0.7196 0.8196 0.9174
#> [11] 0.9570 0.9751 1.0000
#> attr(,"label")
#> [1] "Cumulative share of population"
#> 
#> 
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lb"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] FALSE
#> 
#> 
#> attr(,"class")
#> [1] "pipgd_params"