onlineFDR-package: onlineFDR: A package for online error rate control
In dsrobertson/onlineFDR: Online error rate control
onlineFDR-package R Documentation
onlineFDR: A package for online error rate control
Description
The onlineFDR package provides methods to control the false discovery rate
(FDR) or familywise error rate (FWER) for online hypothesis testing, where
hypotheses arrive in a stream. A null hypothesis is rejected
based on the evidence against it and on the previous rejection decisions.
Details
Package: onlineFDR
Type: Package
Version: 2.5.1
Date: 2022-08-24
License: GPL-3
Javanmard and Montanari (2015, 2018) proposed two methods for online FDR
control. The first is LORD, which stands for (significance) Levels based On
Recent Discovery and is implemented by the function LORD. This
function also includes the extension to the LORD procedure, called LORD++
(version='++'), proposed by Ramdas et al. (2017). Setting
version='discard' implements a modified version of LORD that can
improve the power of the procedure in the presence of conservative nulls by
adaptively ‘discarding’ these p-values, as proposed by Tian and Ramdas
(2019a). All these LORD procedures provably control the FDR under
independence of the p-values. However, setting version='dep' provides
a modified version of LORD that is valid for arbitrarily dependent p-values.
The second method is LOND, which stands for (significance) Levels based On
Number of Discoveries and is implemented by the function LOND.
This procedure controls the FDR under independence of the p-values, but the
slightly modified version of LOND proposed by Zrnic et al. (2018) also
provably controls the FDR under positive dependence (PRDS conditioN). In
addition, by specifying dep = TRUE, thus function runs a modified
version of LOND which is valid for arbitrarily dependent p-values.
Another method for online FDR control proposed by Ramdas et al. (2018) is the
SAFFRON procedure, which stands for Serial estimate of the
Alpha Fraction that is Futiley Rationed On true Null hypotheses. This
provides an adaptive algorithm for online FDR control. SAFFRON is related to
the Alpha-investing procedure of Foster and Stine (2008), a monotone version
of which is implemented by the function Alpha_investing. Both
these procedure provably control the FDR under independence of the p-values.
Tian and Ramdas (2019) proposed the ADDIS
algorithm, which stands for an ADaptive algorithm that DIScards conservative
nulls. The algorithm compensates for the power loss of SAFFRON with
conservative nulls, by including both adaptivity in the fraction of null
hypotheses (like SAFFRON) and the conservativeness of nulls (unlike SAFFRON).
The ADDIS procedure provably controls the FDR for independent p-values. Tian
and Ramdas (2019) also presented a version for an asynchronous testing
process, consisting of tests that start and finish at (potentially) random
times.
For testing batches of hypotheses, Zrnic et al. (2020) proposed batched
online testing algorithms that control the FDR, where the p-values across
different batches are independent, and within a batch the p-values are either
positively dependent or independent.
Zrnic et al. (2021) generalised LOND, LORD and SAFFRON for asynchronous
online testing, where each hypothesis test can itself be a sequential process
and the tests can overlap in time. Note though that these algorithms are
designed for the control of a modified FDR (mFDR). They are implemented by
the functions LONDstar, LORDstar and
SAFFRONstar. Zrnic et al. (2021) presented three explicit
versions of these algorithms:
1) version='async' is for an asynchronous testing
process, consisting of tests that start and finish at (potentially) random
times. The discretised finish times of the test correspond to the decision
times.
2) version='dep' is for online testing under local
dependence of the p-values. More precisely, for any t>0 we allow the
p-value p_t to have arbitrary dependence on the previous L_t
p-values. The fixed sequence L_t is referred to as ‘lags’.
3) version='batch' is for controlling the mFDR in
mini-batch testing, where a mini-batch represents a grouping of tests run
asynchronously which result in dependent p-values. Once a mini-batch of tests
is fully completed, a new one can start, testing hypotheses independent of
the previous batch.
Recently, Xu and Ramdas (2021) proposed the supLORD algorithm,
which provably controls the false discovery exceedance (FDX) for p-values
that are conditionally superuniform under the null. supLORD also controls the
supFDR and hence the FDR (even at stopping times).
Finally, Tian and Ramdas (2021) proposed a number of algorithms for online
FWER control. The only previously existing procedure for online FWER control
is Alpha-spending, which is an online analog of the Bonferroni procedure.
This is implemented by the function Alpha_spending, and
provides strong FWER control for arbitrarily dependent p-values. A uniformly
more powerful method is online_fallback, which again strongly
controls the FWER even under arbitrary dependence amongst the p-values. The
ADDIS_spending procedure compensates for the power loss of
Alpha-spending and online fallback, by including both adapativity in the
fraction of null hypotheses and the conservativeness of nulls. This procedure
controls the FWER in the strong sense for independent p-values. Tian and
Ramdas (2021) also presented a version for handling local dependence, which
can be specified by setting dep=TRUE.
Further details on all these procedures can be found in Javanmard and
Montanari (2015, 2018), Ramdas et al. (2017, 2018), Robertson and Wason
(2018), Tian and Ramdas (2019, 2021), Xu and Ramdas (2021), and Zrnic et al.
(2020, 2021).
Author(s)
David S. Robertson (david.robertson@mrc-bsu.cam.ac.uk),
Lathan Liou, Adel Javanmard, Aaditya Ramdas, Jinjin Tian, Tijana Zrnic,
Andrea Montanari and Natasha A. Karp.
References
Aharoni, E. and Rosset, S. (2014). Generalized \alpha-investing:
definitions, optimality results and applications to publci databases.
Journal of the Royal Statistical Society (Series B), 76(4):771–794.
Foster, D. and Stine R. (2008). \alpha-investing: a procedure for
sequential control of expected false discoveries. Journal of the Royal
Statistical Society (Series B), 29(4):429-444.
Javanmard, A. and Montanari, A. (2015) On Online Control of False Discovery
Rate. arXiv preprint, https://arxiv.org/abs/1502.06197.
Javanmard, A. and Montanari, A. (2018) Online Rules for Control of False
Discovery Rate and False Discovery Exceedance. Annals of Statistics,
46(2):526-554.
Ramdas, A., Yang, F., Wainwright M.J. and Jordan, M.I. (2017). Online control
of the false discovery rate with decaying memory. Advances in Neural
Information Processing Systems 30, 5650-5659.
Ramdas, A., Zrnic, T., Wainwright M.J. and Jordan, M.I. (2018). SAFFRON: an
adaptive algorithm for online control of the false discovery rate.
Proceedings of the 35th International Conference in Machine Learning,
80:4286-4294.
Robertson, D.S. and Wason, J.M.S. (2018). Online control of the false
discovery rate in biomedical research. arXiv preprint,
https://arxiv.org/abs/1809.07292.
Robertson, D.S., Wason, J.M.S. and Ramdas, A. (2022). Online multiple
hypothesis testing for reproducible research.arXiv preprint,
https://arxiv.org/abs/2208.11418.
Robertson, D.S., Wildenhain, J., Javanmard, A. and Karp, N.A. (2019).
onlineFDR: an R package to control the false discovery rate for growing data
repositories. Bioinformatics, 35:4196-4199,
https://doi.org/10.1093/bioinformatics/btz191.
Tian, J. and Ramdas, A. (2019). ADDIS: an adaptive discarding algorithm for
online FDR control with conservative nulls.
Advances in Neural Information Processing Systems, 9388-9396.
Tian, J. and Ramdas, A. (2021). Online control of the familywise error rate.
Statistical Methods for Medical Research, 30(4):976–993.
Xu, Z. and Ramdas, A. (2021). Dynamic Algorithms for Online
Multiple Testing. Annual Conference on Mathematical and Scientific
Machine Learning, PMLR, 145:955-986.
Zrnic, T., Jiang D., Ramdas A. and Jordan M. (2020). The Power of
Batching in Multiple Hypothesis Testing.
International Conference on Artificial Intelligence and Statistics,
PMLR, 108:3806-3815.
Zrnic, T., Ramdas, A. and Jordan, M.I. (2021). Asynchronous Online Testing of
Multiple Hypotheses. Journal of Machine Learning Research, 22:1-33.
dsrobertson/onlineFDR documentation built on April 21, 2023, 8:17 p.m.
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| onlineFDR-package | R Documentation |
onlineFDR: A package for online error rate control
Description
The onlineFDR package provides methods to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive in a stream. A null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.
Details
| Package: | onlineFDR |
| Type: | Package |
| Version: | 2.5.1 |
| Date: | 2022-08-24 |
| License: | GPL-3 |
Javanmard and Montanari (2015, 2018) proposed two methods for online FDR
control. The first is LORD, which stands for (significance) Levels based On
Recent Discovery and is implemented by the function LORD. This
function also includes the extension to the LORD procedure, called LORD++
(version='++'), proposed by Ramdas et al. (2017). Setting
version='discard' implements a modified version of LORD that can
improve the power of the procedure in the presence of conservative nulls by
adaptively ‘discarding’ these p-values, as proposed by Tian and Ramdas
(2019a). All these LORD procedures provably control the FDR under
independence of the p-values. However, setting version='dep' provides
a modified version of LORD that is valid for arbitrarily dependent p-values.
The second method is LOND, which stands for (significance) Levels based On
Number of Discoveries and is implemented by the function LOND.
This procedure controls the FDR under independence of the p-values, but the
slightly modified version of LOND proposed by Zrnic et al. (2018) also
provably controls the FDR under positive dependence (PRDS conditioN). In
addition, by specifying dep = TRUE, thus function runs a modified
version of LOND which is valid for arbitrarily dependent p-values.
Another method for online FDR control proposed by Ramdas et al. (2018) is the
SAFFRON procedure, which stands for Serial estimate of the
Alpha Fraction that is Futiley Rationed On true Null hypotheses. This
provides an adaptive algorithm for online FDR control. SAFFRON is related to
the Alpha-investing procedure of Foster and Stine (2008), a monotone version
of which is implemented by the function Alpha_investing. Both
these procedure provably control the FDR under independence of the p-values.
Tian and Ramdas (2019) proposed the ADDIS
algorithm, which stands for an ADaptive algorithm that DIScards conservative
nulls. The algorithm compensates for the power loss of SAFFRON with
conservative nulls, by including both adaptivity in the fraction of null
hypotheses (like SAFFRON) and the conservativeness of nulls (unlike SAFFRON).
The ADDIS procedure provably controls the FDR for independent p-values. Tian
and Ramdas (2019) also presented a version for an asynchronous testing
process, consisting of tests that start and finish at (potentially) random
times.
For testing batches of hypotheses, Zrnic et al. (2020) proposed batched online testing algorithms that control the FDR, where the p-values across different batches are independent, and within a batch the p-values are either positively dependent or independent.
Zrnic et al. (2021) generalised LOND, LORD and SAFFRON for asynchronous
online testing, where each hypothesis test can itself be a sequential process
and the tests can overlap in time. Note though that these algorithms are
designed for the control of a modified FDR (mFDR). They are implemented by
the functions LONDstar, LORDstar and
SAFFRONstar. Zrnic et al. (2021) presented three explicit
versions of these algorithms:
1) version='async' is for an asynchronous testing
process, consisting of tests that start and finish at (potentially) random
times. The discretised finish times of the test correspond to the decision
times.
2) version='dep' is for online testing under local
dependence of the p-values. More precisely, for any t>0 we allow the
p-value p_t to have arbitrary dependence on the previous L_t
p-values. The fixed sequence L_t is referred to as ‘lags’.
3) version='batch' is for controlling the mFDR in
mini-batch testing, where a mini-batch represents a grouping of tests run
asynchronously which result in dependent p-values. Once a mini-batch of tests
is fully completed, a new one can start, testing hypotheses independent of
the previous batch.
Recently, Xu and Ramdas (2021) proposed the supLORD algorithm,
which provably controls the false discovery exceedance (FDX) for p-values
that are conditionally superuniform under the null. supLORD also controls the
supFDR and hence the FDR (even at stopping times).
Finally, Tian and Ramdas (2021) proposed a number of algorithms for online
FWER control. The only previously existing procedure for online FWER control
is Alpha-spending, which is an online analog of the Bonferroni procedure.
This is implemented by the function Alpha_spending, and
provides strong FWER control for arbitrarily dependent p-values. A uniformly
more powerful method is online_fallback, which again strongly
controls the FWER even under arbitrary dependence amongst the p-values. The
ADDIS_spending procedure compensates for the power loss of
Alpha-spending and online fallback, by including both adapativity in the
fraction of null hypotheses and the conservativeness of nulls. This procedure
controls the FWER in the strong sense for independent p-values. Tian and
Ramdas (2021) also presented a version for handling local dependence, which
can be specified by setting dep=TRUE.
Further details on all these procedures can be found in Javanmard and Montanari (2015, 2018), Ramdas et al. (2017, 2018), Robertson and Wason (2018), Tian and Ramdas (2019, 2021), Xu and Ramdas (2021), and Zrnic et al. (2020, 2021).
Author(s)
David S. Robertson (david.robertson@mrc-bsu.cam.ac.uk), Lathan Liou, Adel Javanmard, Aaditya Ramdas, Jinjin Tian, Tijana Zrnic, Andrea Montanari and Natasha A. Karp.
References
Aharoni, E. and Rosset, S. (2014). Generalized \alpha-investing:
definitions, optimality results and applications to publci databases.
Journal of the Royal Statistical Society (Series B), 76(4):771–794.
Foster, D. and Stine R. (2008). \alpha-investing: a procedure for
sequential control of expected false discoveries. Journal of the Royal
Statistical Society (Series B), 29(4):429-444.
Javanmard, A. and Montanari, A. (2015) On Online Control of False Discovery Rate. arXiv preprint, https://arxiv.org/abs/1502.06197.
Javanmard, A. and Montanari, A. (2018) Online Rules for Control of False Discovery Rate and False Discovery Exceedance. Annals of Statistics, 46(2):526-554.
Ramdas, A., Yang, F., Wainwright M.J. and Jordan, M.I. (2017). Online control of the false discovery rate with decaying memory. Advances in Neural Information Processing Systems 30, 5650-5659.
Ramdas, A., Zrnic, T., Wainwright M.J. and Jordan, M.I. (2018). SAFFRON: an adaptive algorithm for online control of the false discovery rate. Proceedings of the 35th International Conference in Machine Learning, 80:4286-4294.
Robertson, D.S. and Wason, J.M.S. (2018). Online control of the false discovery rate in biomedical research. arXiv preprint, https://arxiv.org/abs/1809.07292.
Robertson, D.S., Wason, J.M.S. and Ramdas, A. (2022). Online multiple hypothesis testing for reproducible research.arXiv preprint, https://arxiv.org/abs/2208.11418.
Robertson, D.S., Wildenhain, J., Javanmard, A. and Karp, N.A. (2019). onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinformatics, 35:4196-4199, https://doi.org/10.1093/bioinformatics/btz191.
Tian, J. and Ramdas, A. (2019). ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. Advances in Neural Information Processing Systems, 9388-9396.
Tian, J. and Ramdas, A. (2021). Online control of the familywise error rate. Statistical Methods for Medical Research, 30(4):976–993.
Xu, Z. and Ramdas, A. (2021). Dynamic Algorithms for Online Multiple Testing. Annual Conference on Mathematical and Scientific Machine Learning, PMLR, 145:955-986.
Zrnic, T., Jiang D., Ramdas A. and Jordan M. (2020). The Power of Batching in Multiple Hypothesis Testing. International Conference on Artificial Intelligence and Statistics, PMLR, 108:3806-3815.
Zrnic, T., Ramdas, A. and Jordan, M.I. (2021). Asynchronous Online Testing of Multiple Hypotheses. Journal of Machine Learning Research, 22:1-33.
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