df.to.mat

Given a data frame with fields <code>x, y</code> and <code>z</code>, <code>df.to.mat</code> uses the <code>x</code> and 
<code>y</code> coordinates to rearrange <code>z</code> into a rectangular matrix image <code>Z</code>.

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.

Andrew ZammitMangion

EFDR

Wavelet-Based Enhanced FDR for Detecting Signals from Complete
or Incomplete Spatially Aggregated Data

Andrew Zammit-Mangion

Hsin-Cheng Huang

df.to.mat function

<dl><dt>df</dt>
<dd>data frame with fields <code>x</code>, <code>y</code> and <code>z</code></dd></dl>

Arguments

Change xyz data-frame into a Z image — df.to.mat

<dl>

<dt>df</dt>
<dd>data frame with fields <code>x</code>, <code>y</code> and <code>z</code></dd>

</dl>

df.to.mat: Change xyz data-frame into a Z image

Description

Usage

Value

Arguments

Details

Examples