Experimentreadr and Maf: Difference between pages

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imported>Jeremy
(New page: ===Purpose=== Read an Experiment file and import corresponding files. ===Synopsis=== :x = experimentreadr(''filename'') ===Description=== Experiment files include a list of data files...)
 
imported>Donal
 
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===Purpose===
===Purpose===
 
Maximum Autocorrelative Factors.
Read an Experiment file and import corresponding files.
 
===Synopsis===
===Synopsis===
 
:[scores,loads,mn,ssq] = maf(x)
:x = experimentreadr(''filename'')
 
===Description===
===Description===


Experiment files include a list of data files and their corresponding
NOTE: The function MAF is obsolete and will be removed in future versions. Please use [[maxautofactors]].
"properties of interest" (y-values). An experiment file is expected to be
a plain text file or a Microsoft Excel-formatted file. The file should
consist of at least one column of text strings indicating the files to be
read and used as samples in the X-block of a regression or classification
model and one column of numerical values indicating the values to use as
the corresponding y-block. If no experiment file filename is specified on
the command line, the user is prompted to locate a suitable file.
 
Once loaded, the experiment file can be manipulated, excluding samples
using the include field of the Row Labels tab, or y-block columns using
the include field of the Column Labels tab. Samples can be marked as in
the Calibration or Validation set using the Row Labels tab.
 
When all manipulations are complete, the user clicks the check-mark
toolbar button to import all the indicated files and automatically load
the experiment data into the Analysis GUI.  


====X-block File Formats====
Similar to PCA, MAF uses singular value decomposition to provide a model of data (x) which captures maximum spatially-correlated variance. The resulting scores (scores), loadings (loads), and mean spectrum (mn) can be used to reconstruct the mean-centered data matrix X_mn:
The x-block files named can be in any standard readable
X_mn = scores\*loads
file format. However, experiment files do not currently allow for any
The difference between PCA and MAF is that MAF extracts loadings which are highly correlated in the spatial dimension of an image. In addition, MAF always returns the entire set of components up to the rank of the data matrix.
multi-file formats. Named files must contain only one sample (row) of
Input X is either an Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable).
data per file.
Output ssq is an experimental sum of squares captured table but because the nature of the decomposition, this table is only approximate. It includes the component number (column 1), estimated eigenvalue (column 2), and the estimated captured per variable and total (columns 3 and 4, respectively).


====Header Row====
MAF is accessible in the Analysis window by choosing the PCA analysis method, then clicking on the toolbar icon "Edit Analysis Method Options" and changing the "algorithm" setting from "svd" to "maf", and click "Ok".
An experiment file can include an optional header row for the
filenames and properties of interest. This row can contain text lables
which will be used to label the y-block columns (i.e. giving a text
description of the property of interest.)
 
====Calibration/Validation====
Experiment files can also contain information
used to split the data into calibration and validation sets. To use this
feature, include an additional column with the keywords "Calibration" or
"Validation" next to each file. When the experiment is imported, the data
will be automatically loaded into the appropriate data blocks.
NOTE: other valid synonyms include (all are case insensitive)
:Calibration = Cal = C   
:Validation  = Val = V = Test = T
 
===Example===
<tt>
  filename,concentration,cal/val
  file1.spc,13.2,cal
  file2.spc,19.0,cal
  file3.spc,5.3,cal
  file4.spc,8.3,val
</tt>
 
The above experiment file would define an experiment with three samples with X-block data
stored in the indicated files and y-values of 13.2, 19.0, 5.3, and 8.3
(with a text description of the y-values as "concentration"). The first
three files would be used for calibration, the last file for validation.


===See Also===
===See Also===
 
[[mcr]], [[parafac]], [[pca]]
[[autoimport]], [[jcampreadr]], [[spcreadr]], [[xclreadr]]

Latest revision as of 10:24, 26 May 2015

Purpose

Maximum Autocorrelative Factors.

Synopsis

[scores,loads,mn,ssq] = maf(x)

Description

NOTE: The function MAF is obsolete and will be removed in future versions. Please use maxautofactors.

Similar to PCA, MAF uses singular value decomposition to provide a model of data (x) which captures maximum spatially-correlated variance. The resulting scores (scores), loadings (loads), and mean spectrum (mn) can be used to reconstruct the mean-centered data matrix X_mn: 

X_mn = scores\*loads

The difference between PCA and MAF is that MAF extracts loadings which are highly correlated in the spatial dimension of an image. In addition, MAF always returns the entire set of components up to the rank of the data matrix. Input X is either an Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable). Output ssq is an experimental sum of squares captured table but because the nature of the decomposition, this table is only approximate. It includes the component number (column 1), estimated eigenvalue (column 2), and the estimated captured per variable and total (columns 3 and 4, respectively).

MAF is accessible in the Analysis window by choosing the PCA analysis method, then clicking on the toolbar icon "Edit Analysis Method Options" and changing the "algorithm" setting from "svd" to "maf", and click "Ok".

See Also

mcr, parafac, pca

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