funreg.permutation {funreg}

Do a permutation test for functional regression

Description

Performs a permutation F test (Ramsay, Hooker, and Graves, 2009, p. 145) for the significance of a functional covariate, and a permutation likelihood ratio test. The permutation test function currently doesn't allow models with multiple functional covariates, but subject-level covariates are allowed.

Usage

funreg.permutation(object, num.permute = 500, seed = NULL)

Arguments

object

An object of class funreg

num.permute

The number of permutations to use. Ramsay, Hooker and Graves (2009) recommended “several hundred” (p. 145), but for a quicker initial look it might suffice to use 100.

seed

An optional random number seed.

Value

Returns a list with several components. First, pvalue.F is the p-value for the F test. Second, conf.int.for.pvalue.F is the confidence interval for estimating the p-value that would be obtained from the dataset as num.permute approached infinity. The idea of a confidence interval for a p-value is explained further by Sen (2013), with a STATA example. Third, orig.F is the F statistic calculated on the original dataset. Last, permuted.F is the vector of F statistics calculated on each of the random permuted datasets. Also included are pvalue.LR, conf.int.for.pvalue.LR, orig.LR, permuted.LR for the permutation test with a likelihood ratio statistic. A more conservative alternative formula for the p-value is used in pvalue.F.better and pvalue.LR.better. It is not obvious whether to define the p-value as the proportion of permuted datasets with statistics less than or equal to the original, or simply less than the original. This should usually not matter, as a tie is not likely. We made the arbitrary decision to use the former here because it was presented in this way in the Wikipedia article for permutation tests. The conservative alternative formula is the number of less extreme permuted datasets plus one, over the total number of datasets plus one. Adding one to the numerator and denominator is suggested by some authors, partly in order to prevent a nonsensical zero p-value (Onghena & May, 1995; Phipson, Belinda & Smyth, 2010).

References

Onghena, P., & May, R. B. (1995). Pitfalls in computing and interpreting randomization test p values: A commentary on Chen and Dunlap. Behavior Research Methods, Instruments, & Computers, 27(3), 408-411. DOI: 10.3758/BF03200438.

Phipson, Belinda and Smyth, Gordon K. (2010) Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn. Statistical Applications in Genetics and Molecular Biology: Vol. 9: Iss. 1, Article 39. DOI: 10.2202/1544-6115.1585.

Ramsay, J. O., Hooker, G., & Graves, S. (2009). Functional data analysis with R and MATLAB. NY: Springer.

Sen, S. (2014) Permutation Tests.