While both the pediatric and adult algorithms are considered complete, we have identified several areas for additional research and potential enhancement.
An experiment in re-assessing pediatric height measurements initially
marked as Exclude-Carried-Forward based on growth velocity
is included in the current version of this package.
Because of the complexity involved in this approach, this is
implemented as an independent function with its own driver script for
further study. Our hope is to revise this strategy, and, if feasible,
incorporate it into the main cleangrowth() algorithm.
The primary function is called adjustcarryforward(), and
is implemented in the file R/adjustcarryforward.R. It is
separate from the main R/growth.R file as it is set up to
be run separately, using the output from cleangrowth(). The
adjustcarryforward() function is available in the main
package namespace.
The primary tools for running this function are the script
exec/testadjustcf.R and the function
testacf(). These will run the experimental function on an
existing dataset (specified as a CSV file or data frame) and pass it a
sweep of parameter values for testing the new strategy. This tool will
produce a combined output file or list that includes the original result
alongside the resulting re-inclusion determination for each measurement
for each parameter value run.
For example, if you have run cleangrowth() on the
syngrowth synthetic data set provided with
growthcleanr as described in the main
README.md file, save it to a CSV file for
exec/testadjustcf.R:
> fwrite(cleaned_data, "cleaned.csv", row.names = F)For testacf(), keep cleaned_data in your environment.
Note that the column names should be as described for
cleaned_data the Example under Quickstart.
To use exec/testadjustcf.R, first navigate to the
growthcleanr package directory in the command line. Then execute the
sweep script is executed from the command line on the cleaned data file
for as follows (with the assumption that cleaned.csv is in your current
directory; otherwise, make sure to specify the path relative to your
current directory):
Rscript exec/testadjustcf.R cleaned.csvTo use testacf(), run the following in the console (with
cleaned_data in the environment):
result_list <- testacf(cleaned_data)This result_list will contain 2 or 3 objects; -
testacf_res: a data frame with adjustcarryforward results
for each run - params: a data frame containing the
parameters for each run - debug_filtered_data: data frame
with original data, returned if debug is TRUE
By default, the script/function will generate a range of values with nine steps for the following parameters, where the min and max surround the default value:
| parameter | default | min | max |
|---|---|---|---|
minfactor |
0.5 | 0 | 1 |
maxfactor |
2 | 0 | 4 |
banddiff |
3 | 0 | 6 |
banddiff_plus |
5.5 | 0 | 11 |
min_ht.exp_under |
2 | 0 | 4 |
min_ht.exp_over |
0 | -1 | 1 |
max_ht.exp_under |
0.33 | 0 | 0.66 |
max_ht.exp_over |
1.5 | 0 | 3 |
The determination of these values depends on the search type
(specified with the option --searchtype (script)/argument
searchtype (function):
random (default): Values will be generated randomly,
with equal amounts of values on either side of the midpoint. The
midpoint is always included.
--gridlength/grid.length, one will be added to
include the midpoint in the run.--seed/seed (default 7).line-grid: Values will be evenly distributed along
the range for each parameter. If the
--gridlength/grid.length specified is odd,
this will include the midpoint.
full-grid: Values for each included parameter will
evenly distributed along the range for each parameter and in a full
combination between all parameters.
--gridlength/gridlength^(number of included
parameters).--param/param option, which specifies a CSV of
the following format:| parameter | include | value |
|---|---|---|
minfactor |
T | |
maxfactor |
F | 3 |
banddiff |
F | |
banddiff_plus |
F | |
min_ht.exp_under |
T | |
min_ht.exp_over |
F | |
max_ht.exp_under |
F | .5 |
max_ht.exp_over |
F |
minfactor and
min_ht.exp_under will be included, and
maxfactor and max_ht_exp_under will not be
included, but will use 3 and .5 as their values.The default number of sweep steps is 9; this can be changed with the
option --gridlength/gridlength.
For testing options of handling strings of multiple carried forward
values, several options from 0 to 3 have been incorporated. 0 (no
change) is the default option, and can be changed
--exclude_opt/exclude_opt. More information on
each option can be found in the adjustcarryforward()
documentation.
In addition to multiple options for carried-forward strings,
“answers” for a given dataset have been incorporated. When the
--add_answers flag/add_answers argument is set
to TRUE (TRUE by default), a column called
acf_answers will have, for each height value, “Definitely
Exclude”, “Definitely Include”, or “Unknown” (if it does not fall in
either category). Weight values are set as NA.
For example, for a 9-step sweep with the default search type,
random, the parameters passed to the function in each pass
will be:
run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
1 0.494454649 0.331710969 1.681997601 5.438065292 0.371428523 -0.200524185 0.296497153 0.244186167
2 0.198872727 0.918207332 0.0261138 0.361051567 0.370286443 -0.618056939 0.318943811 0.280842425
3 0.057848889 0.343496154 2.957211272 3.448713172 0.758593493 -0.240298769 0.189112731 0.586874211
4 0.034874339 0.462954204 0.949754412 2.697612725 1.694048784 -0.563224398 0.237626234 0.410851815
5 0.5 2 3 5.5 2 0 0.33 1.5
6 0.621874695 3.545623892 4.918346826 10.84063427 2.996152267 0.904217721 0.585439346 1.787876622
7 0.896005213 2.192603083 3.885669706 7.492214661 3.581171147 0.319534914 0.537161064 2.25658771
8 0.670031176 2.90689554 5.990111082 9.239964036 3.676927744 0.082569093 0.568586483 2.645760535
9 0.98603125 2.169401426 5.718063961 6.950459617 2.91380773 0.816289079 0.457654322 2.540503306In a 9-step sweep with a line-grid search type, the
parameters passed to the function in each pass will be:
run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
1 0 0 0 0 0 -1 0 0
2 0.125 0.5 0.75 1.375 0.5 -0.75 0.0825 0.375
3 0.25 1 1.5 2.75 1 -0.5 0.165 0.75
4 0.375 1.5 2.25 4.125 1.5 -0.25 0.2475 1.125
5 0.5 2 3 5.5 2 0 0.33 1.5
6 0.625 2.5 3.75 6.875 2.5 0.25 0.4125 1.875
7 0.75 3 4.5 8.25 3 0.5 0.495 2.25
8 0.875 3.5 5.25 9.625 3.5 0.75 0.5775 2.625
9 1 4 6 11 4 1 0.66 3In a 3-step sweep with a full-grid search type, with the
--param CSV/param data frame specified as in
the above example, the parameters passed to the function in each pass
will be:
run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
1 0.0 3 3 5.5 0 0 0.5 1.5
2 0.5 3 3 5.5 0 0 0.5 1.5
3 1.0 3 3 5.5 0 0 0.5 1.5
4 0.0 3 3 5.5 2 0 0.5 1.5
5 0.5 3 3 5.5 2 0 0.5 1.5
6 1.0 3 3 5.5 2 0 0.5 1.5
7 0.0 3 3 5.5 4 0 0.5 1.5
8 0.5 3 3 5.5 4 0 0.5 1.5
9 1.0 3 3 5.5 4 0 0.5 1.5For the script, the output in the working directory will contain the
sweep parameters, like the above, in a file called
test_adjustcarrforward_DATE_TIME_parameters.csv, and the
output with adjustment results in a file called
test_adjustcarrforward_DATE_TIME.csv, where DATE and TIME
are the system date and time. For the function, a list will be returned
with the parameters as a data.frame in the params entry and
output adjustment results as a data.frame in the
testacf_res entry.
For example, a 5-step sweep with the line-grid search
would be run with this command:
Rscript exec/textadjustcf.R --gridlength 5 --searchtype line-grid cleaned.csvor with this function execution:
result_list <- testacf(
cleaned_data,
grid.length = 5,
searchtype = "line-grid"
)The parameter set for the sweep in file
test_adjustcarrforward_DATE_TIME_parameters.csv
(script)/params data frame of result_list
(function) would be:
run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
1 0 0 0 0 0 -1 0 0
2 0.25 1 1.5 2.75 1 -0.5 0.165 0.75
3 0.5 2 3 5.5 2 0 0.33 1.5
4 0.75 3 4.5 8.25 3 0.5 0.495 2.25
5 1 4 6 11 4 1 0.66 3Note that an odd-numbered length will include the default values in the middle run of the sweep (hence the examples with 5 and 9 step sweeps).
And the first few result rows in
test_adjustcarrforward_DATE_TIME.csv
(script)/testacf_res data frame of result_list
(function) would be:
id subjid sex agedays param measurement gcr_result run-1 run-2 run-3 run-4 run-5
1510 775155 0 889 HEIGHTCM 84.9 Exclude-Extraneous-Same-Day Missing Missing Missing Missing Missing
1511 775155 0 889 HEIGHTCM 89.06 Include No Change No Change No Change No Change No Change
1512 775155 0 1071 HEIGHTCM 92.5 Include No Change No Change No Change No Change No Change
1513 775155 0 1253 HEIGHTCM 96.2 Include No Change No Change No Change No Change No Change
1514 775155 0 1435 HEIGHTCM 96.2 Exclude-Carried-Forward No Change No Change Include Include Include
1515 775155 0 1435 HEIGHTCM 99.692 Include No Change No Change No Change No Change No Change
1516 775155 0 1806 HEIGHTCM 106.1 Include No Change No Change No Change No Change No Change
1517 775155 0 2177 HEIGHTCM 112.3 Include No Change No Change No Change No Change No Change
1518 775155 0 889 WEIGHTKG 13.1 Include No Change No Change No Change No Change No ChangeThe fifth row in the example above demonstrates the results of the
experimental script; for runs 1 and 2, the result is not changed, but
for runs 3-5, the measurement is adjusted for reinclusion. To
demonstrate the range, the following is an extract of measurements only
marked as carried forward exclusions by cleangrowth():
id subjid sex agedays param measurement gcr_result run-1 run-2 run-3 run-4 run-5
1514 775155 0 1435 HEIGHTCM 96.2 Exclude-Carried-Forward No Change No Change Include Include Include
1521 775155 0 1435 WEIGHTKG 15.3 Exclude-Carried-Forward No Change No Change No Change No Change No Change
7952 1340377 1 1806 HEIGHTCM 107.1 Exclude-Carried-Forward No Change Include Include Include Include
7967 1340377 1 1806 WEIGHTKG 18.4 Exclude-Carried-Forward No Change No Change No Change No Change No Change
41775 3643526 1 1253 HEIGHTCM 87.808 Exclude-Carried-Forward Include Include Include Include Include
44901 3706097 0 4032 HEIGHTCM 138.8 Exclude-Carried-Forward No Change Include Include Include Include
30011 5792371 1 3661 HEIGHTCM 145.4 Exclude-Carried-Forward No Change Include Include Include Include
30013 5792371 1 4032 HEIGHTCM 145.4 Exclude-Carried-Forward No Change No Change No Change No Change No Change
30016 5792371 1 1071 WEIGHTKG 15.9 Exclude-Carried-Forward No Change No Change No Change No Change No ChangeSome of these values are not adjusted at all; one is from run 1 on, a few are from run 2 on, and one is from run 3 on.
The adult algorithm added in release 2.0 is complete, but has some known limitations that present areas for future research and enhancement. These areas are enumerated below.
We welcome your ideas on these or any additional enhancements via email, tickets, or pull requests.
Any active work on these areas will be ticketed and managed through GitHub.
In some cases, runs of two or three similar - but not necessarily identical - deviant weights can occur, which are difficult to detect. One approach to address these could involve modifying the moderate EWMA step, but capturing these reliably may be a very challenging task.
A number of adjustments are possible to improve this step, such as tweaks to the weight change allowance, polation, and prioritization. This might require fewer changes to code but more extensive testing to ensure confidence in new threshold numbers/details.
The criteria for some of these steps may be loosened to catch more implausible values. For unit error correction, we could consider having two levels: one suitable for correction, the other not.