Calculate Information Value for a selected outcome variable

Source: R/create_IV.R

Specify an outcome variable and return IV outputs. All numeric, character, and factor variables in the dataset are used as predictor variables.

create_IV(
  data,
  predictors = NULL,
  outcome,
  bins = 5,
  siglevel = 0.05,
  exc_sig = FALSE,
  return = "plot"
)

Arguments

data

A Person Query dataset in the form of a data frame.

predictors

A character vector specifying the columns to be used as predictors. Defaults to NULL, where all numeric, character, and factor vectors in the data will be used as predictors.

outcome

A string specifying a binary variable, i.e. can only contain the values 1 or 0, or a logical variable (TRUE/FALSE). Logical variables will be automatically converted to binary (TRUE to 1, FALSE to 0).

bins

Number of bins to use, defaults to 5.

siglevel

Significance level to use in comparing populations for the outcomes, defaults to 0.05

exc_sig

Logical value determining whether to exclude values where the p-value lies below what is set at siglevel. Defaults to FALSE, where p-value calculation does not happen altogether.

return

String specifying what to return. This must be one of the following strings:

  • "plot"

  • "summary"

  • "list"

  • "plot-WOE"

  • "IV"

See Value for more information.

Value

A different output is returned depending on the value passed to the return argument:

  • "plot": 'ggplot' object. A bar plot showing the IV value of the top (maximum 12) variables.

  • "summary": data frame. A summary table for the metric.

  • "list": list. A list of outputs for all the input variables.

  • "plot-WOE": A list of 'ggplot' objects that show the WOE for each predictor used in the model.

  • "IV" returns a list object which mirrors the return in Information::create_infotables().

See also

Other Variable Association: IV_by_period(), IV_report(), plot_WOE()

Other Information Value: IV_by_period(), IV_report(), plot_WOE()

Examples

# Return a summary table of IV
sq_data %>%
  dplyr::mutate(X = ifelse(Workweek_span > 40, 1, 0)) %>%
  create_IV(outcome = "X",
            predictors = c("Email_hours",
                           "Meeting_hours",
                           "Instant_Message_hours"),
            return = "plot")



# Return summary
sq_data %>%
  dplyr::mutate(X = ifelse(Collaboration_hours > 10, 1, 0)) %>%
  create_IV(outcome = "X",
            predictors = c("Email_hours", "Meeting_hours"),
            return = "summary")
#>        Variable       IV
#> 1 Meeting_hours 2.672205
#> 2   Email_hours 1.218238