test_AD_2_CurveSamples: Non parametric goodness-of-fit test of 2 samples of curves

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/test_AD_2_CurveSamples.R

Description

Returns a vector of p-values and test statistics of Anderson-Darling goodness-of-fit test of 2 samples of curves. It estimates fpca scores and conducts 2-sample AD test.

Usage

1
test_AD_2_CurveSamples(Mat1, Mat2, varpct = 0.95)

Arguments

Mat1

Matrix 1 with each column being the discretized version of a separate curve

Mat2

Matrix 1 with each column being the discretized version of a separate curve

varpct

Desired percentage of variance explained by the functional PCA scores

Value

pval

Vector of p-values, of length p

testStat

Vector of test statistics, of length p

Author(s)

Subhrangshu Nandi; Statistics PhD student, UW Madison; snandi@wisc.edu or nands31@gmail.com

References

Pomann, G.M., Staicu, A.M., and Ghosh,S. (2016). A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis. Journal of the Royal Statistical Society: Series C (Applied Statistics).

Scholz, F. W., & Stephens, M. A. (1987). K-sample Anderson<e2><80><93>Darling tests. Journal of the American Statistical Association, 82(399), 918-924.

See Also

adk.test test_AD_2sample

Examples

1
2
3
4
5
data( growth, package = 'fda' )
Mat1 <- growth[['hgtm']]
Mat2 <- growth[['hgtf']]
testOutput <- test_AD_2_CurveSamples(Mat1 = Mat1, Mat2 = Mat2, varpct = 0.95)
testOutput

snandi/Registration documentation built on May 30, 2019, 5:04 a.m.