Treating coins is a simple extension of treating a data frame. The
coin method simply extracts the relevant data set as a data frame, and
passes it to the data frame method. So more or less, the same arguments
are present.
We begin by building the example coin, which will be used for the
examples here.
Default treatment
The Treat() function can be applied directly to a coin
with completely default options:
coin <- Treat(coin, dset = "Raw")
#> Written data set to .$Data$Treated
For each indicator, the Treat() function:
- Checks skew and kurtosis using the
check_SkewKurt()
function
- If the indicator fails the test (returns
FALSE),
applies Winsorisation
- Checks again skew and kurtosis
- If the indicator still fails, applies a log transformation.
If at any stage the indicator passes the skew and kurtosis test, it
is returned without further treatment.
When we run Treat() on a coin, it also stores
information returned from f1, f2 and
f_pass in the coin:
# summary of treatment for each indicator
head(coin$Analysis$Treated$Dets_Table)
#> iCode check_SkewKurt0.Pass check_SkewKurt0.Skew check_SkewKurt0.Kurt
#> 1 LPI TRUE -0.3042681 -0.6567514
#> 2 Flights FALSE 2.1032872 4.5088794
#> 3 Ship TRUE -0.5756680 -0.6814795
#> 4 Bord FALSE 2.1482360 5.7914905
#> 5 Elec FALSE 2.2252736 5.7910268
#> 6 Gas FALSE 2.8294486 10.3346494
#> winsorise.nwin check_SkewKurt1.Pass check_SkewKurt1.Skew check_SkewKurt1.Kurt
#> 1 NA NA NA NA
#> 2 1 TRUE 1.900658 3.336065
#> 3 NA NA NA NA
#> 4 1 TRUE 1.899211 4.346298
#> 5 1 TRUE 1.717744 2.586062
#> 6 1 TRUE 1.602518 1.525576
Notice that only one treatment function was used here, since after
Winsorisation (f1), all indicators passed the skew and
kurtosis test (f_pass).
In general, Treat() tries to collect all information
returned from the functions that it calls. Details of the treatment of
individual points are also stored in
.$Analysis$Treated$Treated_Points.
The Treat() function gives you a high degree of control
over which functions are used to treat and test indicators, and it is
also possible to specify different functions for different indicators.
Let’s begin though by seeing how we can change the specifications for
all indicators, before proceeding to individual treatment.
Unless indiv_specs is specified (see later), the same
procedure is applied to all indicators. This process is specified by the
global_specs argument. To see how to use this, it is
easiest to show the default of this argument which is built into the
treat() function:
# default treatment for all cols
specs_def <- 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))
Notice that there are six entries in the list:
f1 which is a string referring to the first treatment
functionf1_para which is a list of any other named arguments to
f1, excluding x (the data to be treated)f2 and f2_para which are analogous to
f1 and f1_para but for the second treatment
functionf_pass is a string referring to the function to check
for outliersf_pass_para a list of any other named arguments to
f_pass, other than x (the data to be
checked)
To understand what the individual parameters do, for example in
f1_para, we need to look at the function called by
f1, which is the winsorise() function:
x A numeric vector.na.rm Set TRUE to remove NA
values, otherwise returns NA.winmax Maximum number of points to Winsorise. Default
5. Set NULL to have no limit.skew_thresh A threshold for absolute skewness
(positive). Default 2.25.kurt_thresh A threshold for kurtosis. Default 3.5.force_win Logical: if TRUE, forces
winsorisation up to winmax (regardless of skew/kurt).
Here we see the same parameters as named in the list
f1_para, and we can change the maximum number of points to
be Winsorised, the skew and kurtosis thresholds, and other things.
To make adjustments, unless we want to redefine everything, we don’t
need to specify the entire list. So for example, if we want to change
the maximum Winsorisation limit winmax, we can just pass
this part of the list (notice we still have to wrap the parameter inside
a list):
# treat with max winsorisation of 3 points
coin <- Treat(coin, dset = "Raw", global_specs = list(f1_para = list(winmax = 1)))
#> Written data set to .$Data$Treated
#> (overwritten existing data set)
# see what happened
coin$Analysis$Treated$Dets_Table |>
head(10)
#> iCode check_SkewKurt0.Pass check_SkewKurt0.Skew check_SkewKurt0.Kurt
#> 1 LPI TRUE -0.3042681 -0.6567514
#> 2 Flights FALSE 2.1032872 4.5088794
#> 3 Ship TRUE -0.5756680 -0.6814795
#> 4 Bord FALSE 2.1482360 5.7914905
#> 5 Elec FALSE 2.2252736 5.7910268
#> 6 Gas FALSE 2.8294486 10.3346494
#> 7 ConSpeed TRUE 0.4622037 0.1873214
#> 8 Cov4G TRUE -1.3725191 0.5419314
#> 9 Goods FALSE 2.6499733 8.2666095
#> 10 Services TRUE 1.7010849 2.3756557
#> winsorise.nwin check_SkewKurt1.Pass check_SkewKurt1.Skew
#> 1 NA NA NA
#> 2 1 TRUE 1.900658
#> 3 NA NA NA
#> 4 1 TRUE 1.899211
#> 5 1 TRUE 1.717744
#> 6 1 TRUE 1.602518
#> 7 NA NA NA
#> 8 NA NA NA
#> 9 1 FALSE 2.469910
#> 10 NA NA NA
#> check_SkewKurt1.Kurt check_SkewKurt2.Pass check_SkewKurt2.Skew
#> 1 NA NA NA
#> 2 3.336065 NA NA
#> 3 NA NA NA
#> 4 4.346298 NA NA
#> 5 2.586062 NA NA
#> 6 1.525576 NA NA
#> 7 NA NA NA
#> 8 NA NA NA
#> 9 7.087309 TRUE 0.03104001
#> 10 NA NA NA
#> check_SkewKurt2.Kurt
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 -0.8888965
#> 10 NA
Having imposed a much stricter Winsorisation limit (only one point),
we can see that now one indicator has been passed to the second
treatment function f2, which has performed a log
transformation. After doing this, the indicator passes the skew and
kurtosis test.
By default, if an indicator does not satisfy f_pass
after applying f1, it is passed to f2 in
its original form - in other words it is not the output of
f1 that is passed to f2, and f2
is applied instead of f1, rather than in addition
to it. If you want to apply f2 on top of f1
set combine_treat = TRUE. In this case, if
f_pass is not satisfied after f1 then the
output of f1 is used as the input of f2. For
the defaults of f1 and f2 this approach is
probably not advisable because Winsorisation and the log transform are
quite different approaches. However depending on what you want to do, it
might be useful.
Individual treatment
The global_specs specifies the treatment methodology to
apply to all indicators. However, the indiv_specs argument
(if specified), can be used to override the treatment specified in
global_specs for specific indicators. It is specified in
exactly the same way as global_specs but requires a
parameter list for each indicator that is to have individual
specifications applied, wrapped inside one list.
This is probably clearer using an example. To begin with something
simple, let’s say that we keep the defaults for all indicators except
one, where we change the Winsorisation limit. We will set the
Winsorisation limit of the indicator “Flights” to zero, to force it to
be log-transformed.
# change individual specs for Flights
indiv_specs <- list(
Flights = list(
f1_para = list(winmax = 0)
)
)
# re-run data treatment
coin <- Treat(coin, dset = "Raw", indiv_specs = indiv_specs)
#> Written data set to .$Data$Treated
#> (overwritten existing data set)
The only thing to remember here is to make sure the list is created
correctly. Each indicator to assign individual treatment must have its
own list - here containing f1_para. Then
f1_para itself is a list of named parameter values for
f1. Finally, all lists for each indicator have to be
wrapped into a single list to pass to indiv_specs. This
looks a bit convoluted for changing a single parameter, but gives a high
degree of control over how data treatment is performed.
We can now see what happened to “Flights”:
coin$Analysis$Treated$Dets_Table[
coin$Analysis$Treated$Dets_Table$iCode == "Flights",
]
#> iCode check_SkewKurt0.Pass check_SkewKurt0.Skew check_SkewKurt0.Kurt
#> 2 Flights FALSE 2.103287 4.508879
#> winsorise.nwin check_SkewKurt1.Pass check_SkewKurt1.Skew check_SkewKurt1.Kurt
#> 2 0 FALSE 2.103287 4.508879
#> check_SkewKurt2.Pass check_SkewKurt2.Skew check_SkewKurt2.Kurt
#> 2 TRUE -0.09502644 -0.8305217
Now we see that “Flights” didn’t pass the first Winsorisation step
(because nothing happened to it), and was passed to the log transform.
After that, the indicator passed the skew and kurtosis check.
As another example, we may wish to exclude some indicators from data
treatment completely. To do this, we can set the corresponding entries
in indiv_specs to "none". This is the only
case where we don’t have to pass a list for each indicator.
# change individual specs for two indicators
indiv_specs <- list(
Flights = "none",
LPI = "none"
)
# re-run data treatment
coin <- Treat(coin, dset = "Raw", indiv_specs = indiv_specs)
#> Written data set to .$Data$Treated
#> (overwritten existing data set)
Now if we examine the treatment table, we will find that these
indicators have been excluded from the table, as they were not subjected
to treatment.
External functions
Any functions can be passed to Treat(), for both
treating and checking for outliers. As an example, we can pass an
outlier detection function ` from the performance
package
The following code chunk will only run if you have the ‘performance’
package installed.
library(performance)
# the check_outliers function outputs a logical vector which flags specific points as outliers.
# We need to wrap this to give a single TRUE/FALSE output, where FALSE means it doesn't pass,
# i.e. there are outliers
outlier_pass <- function(x){
# return FALSE if any outliers
!any(check_outliers(x))
}
# now call treat(), passing this function
# we set f_pass_para to NULL to avoid passing default parameters to the new function
coin <- Treat(coin, dset = "Raw",
global_specs = list(f_pass = "outlier_pass",
f_pass_para = NULL)
)
#> Written data set to .$Data$Treated
#> (overwritten existing data set)
# see what happened
coin$Analysis$Treated$Dets_Table |>
head(10)
#> iCode outlier_pass0 winsorise.nwin outlier_pass1 outlier_pass2
#> 1 LPI FALSE 0 FALSE FALSE
#> 2 Flights FALSE 1 FALSE TRUE
#> 3 Ship FALSE 0 FALSE FALSE
#> 4 Bord FALSE 1 FALSE FALSE
#> 5 Elec FALSE 1 FALSE TRUE
#> 6 Gas FALSE 1 FALSE FALSE
#> 7 ConSpeed FALSE 0 FALSE FALSE
#> 8 Cov4G FALSE 0 FALSE FALSE
#> 9 Goods FALSE 2 FALSE TRUE
#> 10 Services FALSE 0 FALSE TRUE
Here we see that the test for outliers is much stricter and very few
of the indicators pass the test, even after applying a log
transformation. Clearly, how an outlier is defined can vary and depend
on your application.
