nptest-package: Nonparametric Bootstrap and Permutation Tests
In nptest: Nonparametric Bootstrap and Permutation Tests
nptest-package R Documentation
Nonparametric Bootstrap and Permutation Tests
Description
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Author(s)
Nathaniel E. Helwig <helwig@umn.edu>
Maintainer: Nathaniel E. Helwig <helwig@umn.edu>
References
Blair, R. C., Higgins, J. J., Karniski, W., & Kromrey, J. D. (1994). A study of multivariate permutation tests which may replace Hotelling's T2 test in prescribed circumstances. Multivariate Behavioral Research, 29(2), 141-163. doi: 10.1207/s15327906mbr2902_2
Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Statistics in Medicine, 19(9), 1141-1164. doi: 10.1002/(SICI)1097-0258(20000515)19:9%3C1141::AID-SIM479%3E3.0.CO;2-F
Chung, E., & Romano, J. P. (2016). Asymptotically valid and exact permutation tests based on two-sample U-statistics. Journal of Statistical Planning and Inference, 168, 97-105. doi: 10.1016/j.jspi.2015.07.004
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press. doi: 10.1017/CBO9780511802843
DiCiccio, C. J., & Romano, J. P. (2017). Robust permutation tests for correlation and regression coefficients. Journal of the American Statistical Association, 112(519), 1211-1220. doi: 10.1080/01621459.2016.1202117
Draper, N. R., & Stoneman, D. M. (1966). Testing for the inclusion of variables in linear regression by a randomisation technique. Technometrics, 8(4), 695-699. doi: 10.2307/1266641
Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Boostrap. Chapman & Hall/CRC. doi: 10.1201/9780429246593
Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver and Boyd.
Freedman, D., & Lane, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business and Economic Statistics, 1(4), 292-298. doi: 10.2307/1391660
Helwig, N. E. (2019a). Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging. WIREs Computational Statistics, 11(2), e1457. doi: 10.1002/wics.1457
Helwig, N. E. (2019b). Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics. NeuroImage, 201, 116030. doi: 10.1016/j.neuroimage.2019.116030
Huh, M.-H., & Jhun, M. (2001). Random permutation testing in multiple linear regression. Communications in Statistics - Theory and Methods, 30(10), 2023-2032. doi: 10.1081/STA-100106060
Janssen, A. (1997). Studentized permutation tests for non-i.i.d. hypotheses and the generalized Behrens-Fisher problem. Statistics & Probability Letters , 36 (1), 9-21. doi: 10.1016/S0167-7152(97)00043-6
Johnson, N. J. (1978). Modified t tests and confidence intervals for asymmetrical populations. Journal of the American Statistical Association, 73 (363), 536-544. doi: 10.2307/2286597
Kennedy, P. E., & Cade, B. S. (1996). Randomization tests for multiple regression. Communications in Statistics - Simulation and Computation, 25(4), 923-936. doi: 10.1080/03610919608813350
Manly, B. (1986). Randomization and regression methods for testing for associations with geographical, environmental and biological distances between populations. Researches on Population Ecology, 28(2), 201-218. doi: 10.1007/BF02515450
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals Of Mathematical Statistics, 18(1), 50-60. doi: 10.1214/aoms/1177730491
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2018). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-0. https://CRAN.R-project.org/package=e1071
Nichols, T. E., Ridgway, G. R., Webster, M. G., & Smith, S. M. (2008). GLM permutation: nonparametric inference for arbitrary general linear models. NeuroImage, 41(S1), S72.
O'Gorman, T. W. (2005). The performance of randomization tests that use permutations of independent variables. Communications in Statistics - Simulation and Computation, 34(4), 895-908. doi: 10.1080/03610910500308230
Pitman, E. J. G. (1937a). Significance tests which may be applied to samples from any populations. Supplement to the Journal of the Royal Statistical Society, 4(1), 119-130. doi: 10.2307/2984124
Pitman, E. J. G. (1937b). Significance tests which may be applied to samples from any populations. ii. the correlation coefficient test. Supplement to the Journal of the Royal Statistical Society, 4(2), 225-232. doi: 10.2307/2983647
Romano, J. P. (1990). On the behavior of randomization tests without a group invariance assumption. Journal of the American Statistical Association, 85(411), 686-692. doi: 10.1080/01621459.1990.10474928
Still, A. W., & White, A. P. (1981). The approximate randomization test as an alternative to the F test in analysis of variance. British Journal of Mathematical and Statistical Psychology, 34(2), 243-252. doi: 10.1111/j.2044-8317.1981.tb00634.x
Student. (1908). The probable error of a mean. Biometrika, 6(1), 1-25. doi: 10.2307/2331554
ter Braak, C. J. F. (1992). Permutation versus bootstrap significance tests in multiple regression and ANOVA. In K. H. J\"ockel, G. Rothe, & W. Sendler (Eds.), Bootstrapping and related techniques. lecture notes in economics and mathematical systems, vol 376 (pp. 79-86). Springer.
Welch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika, 39(3/4), 350-362. doi: 10.2307/2332010
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. doi: 10.2307/3001968
White, H. (1980). A heteroscedasticity-consistent covariance matrix and a direct test for heteroscedasticity. Econometrica, 48(4), 817-838. doi: 10.2307/1912934
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397. doi: 10.1016/j.neuroimage.2014.01.060
Examples
# See examples for...
# flipn (generate all sign flip vectors)
# mcse (Monte Carlo standard errors)
# np.boot (nonparametric bootstrap resampling)
# np.cor.test (nonparametric correlation tests)
# np.loc.test (nonparametric location tests)
# np.reg.test (nonparametric regression tests)
# permn (generate all permutation vectors)
nptest documentation built on April 15, 2023, 1:08 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| nptest-package | R Documentation |
Nonparametric Bootstrap and Permutation Tests
Description
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Author(s)
Nathaniel E. Helwig <helwig@umn.edu>
Maintainer: Nathaniel E. Helwig <helwig@umn.edu>
References
Blair, R. C., Higgins, J. J., Karniski, W., & Kromrey, J. D. (1994). A study of multivariate permutation tests which may replace Hotelling's T2 test in prescribed circumstances. Multivariate Behavioral Research, 29(2), 141-163. doi: 10.1207/s15327906mbr2902_2
Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Statistics in Medicine, 19(9), 1141-1164. doi: 10.1002/(SICI)1097-0258(20000515)19:9%3C1141::AID-SIM479%3E3.0.CO;2-F
Chung, E., & Romano, J. P. (2016). Asymptotically valid and exact permutation tests based on two-sample U-statistics. Journal of Statistical Planning and Inference, 168, 97-105. doi: 10.1016/j.jspi.2015.07.004
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press. doi: 10.1017/CBO9780511802843
DiCiccio, C. J., & Romano, J. P. (2017). Robust permutation tests for correlation and regression coefficients. Journal of the American Statistical Association, 112(519), 1211-1220. doi: 10.1080/01621459.2016.1202117
Draper, N. R., & Stoneman, D. M. (1966). Testing for the inclusion of variables in linear regression by a randomisation technique. Technometrics, 8(4), 695-699. doi: 10.2307/1266641
Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Boostrap. Chapman & Hall/CRC. doi: 10.1201/9780429246593
Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver and Boyd.
Freedman, D., & Lane, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business and Economic Statistics, 1(4), 292-298. doi: 10.2307/1391660
Helwig, N. E. (2019a). Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging. WIREs Computational Statistics, 11(2), e1457. doi: 10.1002/wics.1457
Helwig, N. E. (2019b). Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics. NeuroImage, 201, 116030. doi: 10.1016/j.neuroimage.2019.116030
Huh, M.-H., & Jhun, M. (2001). Random permutation testing in multiple linear regression. Communications in Statistics - Theory and Methods, 30(10), 2023-2032. doi: 10.1081/STA-100106060
Janssen, A. (1997). Studentized permutation tests for non-i.i.d. hypotheses and the generalized Behrens-Fisher problem. Statistics & Probability Letters , 36 (1), 9-21. doi: 10.1016/S0167-7152(97)00043-6
Johnson, N. J. (1978). Modified t tests and confidence intervals for asymmetrical populations. Journal of the American Statistical Association, 73 (363), 536-544. doi: 10.2307/2286597
Kennedy, P. E., & Cade, B. S. (1996). Randomization tests for multiple regression. Communications in Statistics - Simulation and Computation, 25(4), 923-936. doi: 10.1080/03610919608813350
Manly, B. (1986). Randomization and regression methods for testing for associations with geographical, environmental and biological distances between populations. Researches on Population Ecology, 28(2), 201-218. doi: 10.1007/BF02515450
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals Of Mathematical Statistics, 18(1), 50-60. doi: 10.1214/aoms/1177730491
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2018). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-0. https://CRAN.R-project.org/package=e1071
Nichols, T. E., Ridgway, G. R., Webster, M. G., & Smith, S. M. (2008). GLM permutation: nonparametric inference for arbitrary general linear models. NeuroImage, 41(S1), S72.
O'Gorman, T. W. (2005). The performance of randomization tests that use permutations of independent variables. Communications in Statistics - Simulation and Computation, 34(4), 895-908. doi: 10.1080/03610910500308230
Pitman, E. J. G. (1937a). Significance tests which may be applied to samples from any populations. Supplement to the Journal of the Royal Statistical Society, 4(1), 119-130. doi: 10.2307/2984124
Pitman, E. J. G. (1937b). Significance tests which may be applied to samples from any populations. ii. the correlation coefficient test. Supplement to the Journal of the Royal Statistical Society, 4(2), 225-232. doi: 10.2307/2983647
Romano, J. P. (1990). On the behavior of randomization tests without a group invariance assumption. Journal of the American Statistical Association, 85(411), 686-692. doi: 10.1080/01621459.1990.10474928
Still, A. W., & White, A. P. (1981). The approximate randomization test as an alternative to the F test in analysis of variance. British Journal of Mathematical and Statistical Psychology, 34(2), 243-252. doi: 10.1111/j.2044-8317.1981.tb00634.x
Student. (1908). The probable error of a mean. Biometrika, 6(1), 1-25. doi: 10.2307/2331554
ter Braak, C. J. F. (1992). Permutation versus bootstrap significance tests in multiple regression and ANOVA. In K. H. J\"ockel, G. Rothe, & W. Sendler (Eds.), Bootstrapping and related techniques. lecture notes in economics and mathematical systems, vol 376 (pp. 79-86). Springer.
Welch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika, 39(3/4), 350-362. doi: 10.2307/2332010
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. doi: 10.2307/3001968
White, H. (1980). A heteroscedasticity-consistent covariance matrix and a direct test for heteroscedasticity. Econometrica, 48(4), 817-838. doi: 10.2307/1912934
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397. doi: 10.1016/j.neuroimage.2014.01.060
Examples
# See examples for...
# flipn (generate all sign flip vectors)
# mcse (Monte Carlo standard errors)
# np.boot (nonparametric bootstrap resampling)
# np.cor.test (nonparametric correlation tests)
# np.loc.test (nonparametric location tests)
# np.reg.test (nonparametric regression tests)
# permn (generate all permutation vectors)
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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