# Data to plot linear partial value function

Source: `R/mcda.R`
`lpvf_plot_data.Rd`

Generate data used to plot a linear partial value function for a particular criteria given model outcomes.

    lpvf_plot_data(x, criteria_min = NULL, criteria_max = NULL,
      optimal = c("low", "high"), length_out = 1000)

## Arguments

x | Model outcomes for a particular criteria on the original scale.
criteria_min | A vector of minimum values for each criterion. If `NULL`, then the minimum value is computed automatically.
criteria_max | A vector of maximum values for each criterion. If `NULL`, then the maximum value is computed automatically.
optimal | If `"low"`, then lower values of the outcome are better, and, if `"high"`, then higher values of the outcome are better.
length_out | Number of points between minimum and maximum values of `x`.

## Value

A `data.table` containing `x` and `y` coordinates for a line plot.

## See also

`mcda`

## Examples

    outcome <- rnorm(10, mean = 100, sd = 11)

    # Using optimal
    plot_data <- lpvf_plot_data(outcome, optimal = "high")
    print(plot_data)#>               x           y
    #>    1:  83.84492   0.0000000
    #>    2:  83.88159   0.1001001
    #>    3:  83.91825   0.2002002
    #>    4:  83.95492   0.3003003
    #>    5:  83.99158   0.4004004
    #>   ---
    #>  996: 120.32584  99.5995996
    #>  997: 120.36250  99.6996997
    #>  998: 120.39917  99.7997998
    #>  999: 120.43583  99.8998999
    #> 1000: 120.47249 100.0000000
    # Using criteria_min and criteria_max
    plot_data <- lpvf_plot_data(outcome, criteria_min = 80, criteria_max = 130)
    print(plot_data)#>               x           y
    #>    1:  80.00000   0.0000000
    #>    2:  80.05005   0.1001001
    #>    3:  80.10010   0.2002002
    #>    4:  80.15015   0.3003003
    #>    5:  80.20020   0.4004004
    #>   ---
    #>  996: 129.79980  99.5995996
    #>  997: 129.84985  99.6996997
    #>  998: 129.89990  99.7997998
    #>  999: 129.94995  99.8998999
    #> 1000: 130.00000 100.0000000