Remove outliers from data table

Remove outliers based on outlier measure.

Usage

outlier_remove(
  dat,
  project,
  x,
  dat.remove = "none",
  sd_val = NULL,
  over_write = FALSE
)

Arguments

dat

Primary data containing information on hauls or trips. Table in the FishSET database contains the string 'MainDataTable'.

project

Project name.

x

Variable in dat containing potential outliers.

dat.remove

Defines measure to subset the data. Users can use the predefined values (see below) or user-defined standard deviations from the mean. For user-defined values, dat.remove should be a numeric value. For example, dat.remove=6 would would result in value outside 6SD from the mean being class as outliers. User-defined standard deviations from the mean can also be applied using sd_val. Predefined choices: "none", "5_95_quant", "25_75_quant", "mean_2SD", "median_2SD", "mean_3SD", "median_3SD".

sd_val

Optional. Number of standard deviations from mean defining outliers. For example, sd_val=6 would mean values outside +/- 6 SD from the mean would be outliers.

over_write

Logical, If TRUE, saves data over previously saved data table in the FishSET database.

Value

Returns the modified primary dataset. Modified dataset will be saved to the FishSET database.

Details

The dat.remove choices are:

• numeric value: Remove data points outside +/- `x`SD of the mean

• none: No data points are removed

• 5_95_quant: Removes data points outside the 5th and 95th quantiles

• 25_75_quant: Removes data points outside the 25th and 75th quantiles

• mean_2SD: Removes data points outside +/- 2SD of the mean

• median_2SD: Removes data points outside +/- 2SD of the median

• mean_3SD: Removes data points outside +/- 3SD of the mean

• median_3SD: Removes data points outside +/- 3SD of the median

Examples

if (FALSE) {
pollockMainDataTable <- outlier_remove(pollockMainDataTable, 'pollock', 'dist', 
   dat.remove = 'mean_2SD', save.output = TRUE)
}