Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies in an image. The image is first transformed into the wavelet domain in order to decorrelate any noise components, following which the coefficients at each resolution are standardised. Statistical tests (in a multiple hypothesis testing setting) are then carried out to find the anomalies. The power of EFDR exceeds that of standard FDR, which would carry out tests on every wavelet coefficient: EFDR choose which wavelets to test based on a criterion described in Shen et al. (2002). The package also provides elementary tools to interpolate spatially irregular data onto a grid of the required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N. 'Nonparametric hypothesis testing for a spatial signal.' Journal of the American Statistical Association 97.460 (2002): 1122-1140.
|Author||Andrew Zammit-Mangion [aut, cre], Hsin-Cheng Huang [aut]|
|Date of publication||2015-04-04 13:33:23|
|Maintainer||Andrew Zammit-Mangion <email@example.com>|
|License||GPL (>= 2)|
df.to.mat: Change xyz data-frame into a Z image
diagnostic.table: 2x2 diagnostic table
EFDR: Wavelet-Based Enhanced FDR for Signal Detection in Noisy...
fdrpower: Power function
nei.efdr: Find wavelet neighbourhood
regrid: Regrid ir/regular data
test_image: Create a test image
wavelet-test: Test for anomalies in wavelet space
wav_th: Indices of wavelets exceeding a given threshold