Treat.coin {COINr}R Documentation

Treat a data set in a coin for outliers


Operates a two-stage data treatment process on the data set specified by dset, based on two data treatment functions, and a pass/fail function which detects outliers. The method of data treatment can be either specified by the global_specs argument (which applies the same specifications to all indicators in the specified data set), or else (additionally) by the indiv_specs argument which allows different methods to be applied for each indicator. See details. For a simpler function for data treatment, see the wrapper function qTreat().


## S3 method for class 'coin'
  global_specs = NULL,
  indiv_specs = NULL,
  combine_treat = FALSE,
  out2 = "coin",
  write_to = NULL,
  write2log = TRUE,
  disable = FALSE,



A coin


A named data set available in .$Data


A list specifying the treatment to apply to all columns. This will be applied to all columns, except any that are specified in the indiv_specs argument. Alternatively, set to "none" to apply no treatment. See details.


A list specifying any individual treatment to apply to specific columns, overriding global_specs for those columns. See details.


By default, if f1 fails to pass f_pass, then f2 is applied to the original x, rather than the treated output of f1. If combine_treat = TRUE, f2 will instead be applied to the output of f1, so the two treatments will be combined.


The type of function output: either "coin" to return an updated coin, or "list" to return a list with treated data and treatment details.


If specified, writes the aggregated data to .$Data[[write_to]]. Default write_to = "Treated".


Logical: if FALSE, the arguments of this function are not written to the coin log, so this function will not be invoked when regenerating. Recommend to keep TRUE unless you have a good reason to do otherwise.


Logical: if TRUE will disable data treatment completely and write the unaltered data set. This option is mainly useful in sensitivity and uncertainty analysis (to test the effect of turning imputation on/off).


arguments passed to or from other methods.


An updated coin with a new data set .Data$Treated added, plus analysis information in .$Analysis$Treated.

Global specifications

If the same method of data treatment should be applied to all indicators, use the global_specs argument. This argument takes a structured list which looks like this:

global_specs = list(f1 = .,
                    f1_para = list(.),
                    f2 = .,
                    f2_para = list(.),
                    f_pass = .,
                    f_pass_para = list()

The entries in this list correspond to arguments in Treat.numeric(), and the meanings of each are also described in more detail here below. In brief, f1 is the name of a function to apply at the first round of data treatment, f1_para is a list of any additional parameters to pass to f1, f2 and f2_para are equivalently the function name and parameters of the second round of data treatment, and f_pass and f_pass_para are the function and additional arguments to check for the existence of outliers.

The default values for global_specs are as follows:

global_specs = list(f1 = "winsorise",
                     f1_para = list(na.rm = TRUE,
                                    winmax = 5,
                                    skew_thresh = 2,
                                    kurt_thresh = 3.5,
                                    force_win = FALSE),
                     f2 = "log_CT",
                     f2_para = list(na.rm = TRUE),
                     f_pass = "check_SkewKurt",
                     f_pass_para = list(na.rm = TRUE,
                                        skew_thresh = 2,
                                        kurt_thresh = 3.5))

This shows that by default (i.e. if global_specs is not specified), each indicator is checked for outliers by the check_SkewKurt() function, which uses skew and kurtosis thresholds as its parameters. Then, if outliers exist, the first function winsorise() is applied, which also uses skew and kurtosis parameters, as well as a maximum number of winsorised points. If the Winsorisation function does not satisfy f_pass, the log_CT() function is invoked.

To change the global specifications, you don't have to supply the whole list. If, for example, you are happy with all the defaults but want to simply change the maximum number of Winsorised points, you could specify e.g. global_specs = list(f1_para = list(winmax = 3)). In other words, a subset of the list can be specified, as long as the structure of the list is correct.

Individual specifications

The indiv_specs argument allows different specifications for each indicator. This is done by wrapping multiple lists of the format of the list described in global_specs into one single list, named according to the column names of x. For example, if the date set has indicators with codes "x1", "x2" and "x3", we could specify individual treatment as follows:

indiv_specs = list(x1 = list(.),
                   x2 = list(.)
                   x3 = list(.))

where each list(.) is a specifications list of the same format as global_specs. Any indicators that are not named in indiv_specs are treated using the specifications from global_specs (which will be the defaults if it is not specified). As with global_specs, a subset of the global_specs list may be specified for each entry. Additionally, as a special case, specifying a list entry as e.g. x1 = "none" will apply no data treatment to the indicator "x1". See vignette("treat") for examples of individual treatment.

Function methodology

This function is set up to allow any functions to be passed as the data treatment functions (f1 and f2), as well as any function to be passed as the outlier detection function f_pass, as specified in the global_specs and indiv_specs arguments.

The arrangement of this function is inspired by a fairly standard data treatment process applied to indicators, which consists of checking skew and kurtosis, then if the criteria are not met, applying Winsorisation up to a specified limit. Then if Winsorisation still does not bring skew and kurtosis within limits, applying a nonlinear transformation such as log or Box-Cox.

This function generalises this process by using the following general steps:

  1. Check if variable passes or fails using f_pass

  2. If f_pass returns FALSE, apply f1, else return x unmodified

  3. Check again using *f_pass

  4. If f_pass still returns FALSE, apply f2

  5. Return the modified x as well as other information.

For the "typical" case described above f1 is a Winsorisation function, f2 is a nonlinear transformation and f_pass is a skew and kurtosis check. Parameters can be passed to each of these three functions in a named list, for example to specify a maximum number of points to Winsorise, or Box-Cox parameters, or anything else. The constraints are that:

See also vignette("treat").


# build example coin
coin <- build_example_coin(up_to = "new_coin")

# treat raw data set
coin <- Treat(coin, dset = "Raw")

# summary of treatment for each indicator

[Package COINr version 1.1.7 Index]