Mdcheck: Difference between revisions
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===Options=== | ===Options=== | ||
* ''options'' = a structure array with the following fields: | * '''''options''''' = a structure array with the following fields: | ||
* frac_ssq: [{0.95}] desired fraction between 0 and 1 of variance to be captured by the PCA model, | * '''frac_ssq''': [{0.95}] desired fraction between 0 and 1 of variance to be captured by the PCA model, | ||
* max_pcs: [{5}] maximum number of PCs in the model, if 0, then it uses the mean, | * '''max_pcs''': [{5}] maximum number of PCs in the model, if 0, then it uses the mean, | ||
* meancenter: ['no' | {'yes'}], tells whether to use mean centering in the algorithm, | * '''meancenter''': ['no' | {'yes'}], tells whether to use mean centering in the algorithm, | ||
* recalcmean: ['no' | {'yes'}], recalculate mean center after each cycle of replacement (may improve results for small matricies), | * '''recalcmean''': ['no' | {'yes'}], recalculate mean center after each cycle of replacement (may improve results for small matricies), | ||
* display: [{'off'} | 'on'], governs level of display, | * '''display''': [{'off'} | 'on'], governs level of display, | ||
* tolerance: [{1e-6 100}] convergence criteria, the first element is the minimum change and the second is the maximum number of iterations, | * '''tolerance''': [{1e-6 100}] convergence criteria, the first element is the minimum change and the second is the maximum number of iterations, | ||
* max_missing: [{0.4}] maximum fraction of missing data with which MDCHECK will operate, and | * '''max_missing''': [{0.4}] maximum fraction of missing data with which MDCHECK will operate, and | ||
* toomuch: [{'error'} | 'exclude'] what action should be taken if too much missing data is found. 'error' exit with error message, 'exclude' will exclude elements (rows/columns/slabs/etc) which contain too much missing data from the data before replacement. 'exclude' requires a dataset object as input for (data), | * '''toomuch''': [{'error'} | 'exclude'] what action should be taken if too much missing data is found. 'error' exit with error message, 'exclude' will exclude elements (rows/columns/slabs/etc) which contain too much missing data from the data before replacement. 'exclude' requires a dataset object as input for (data), | ||
* ''algorithm'': [ {'svd'} | 'nipals' ] specified the missing data algorithm to use, NIPALS typically used for large amounts of missing data or large multi-way arrays. | * '''''algorithm''''': [ {'svd'} | 'nipals' ] specified the missing data algorithm to use, NIPALS typically used for large amounts of missing data or large multi-way arrays. | ||
Note: MDCHECK captures up to ''options.frac_ssq'' of the variance using ''options.max_pcs'' or fewer PCA components. | Note: MDCHECK captures up to ''options.frac_ssq'' of the variance using ''options.max_pcs'' or fewer PCA components. | ||
The default options can be retreived using: options = mdcheck('options');. | The default options can be retreived using: options = mdcheck('options');. | ||
===See Also=== | ===See Also=== | ||
[[parafac]], [[pca]] | [[parafac]], [[pca]] | ||
Revision as of 19:56, 2 September 2008
Purpose
Missing Data Checker and infiller.
Synopsis
- [flag,missmap,infilled] = mdcheck(data,options)
- options = mdcheck('options')
Description
This function checks for missing data and infills it using a PCA model if desired. The input is the data to be checked data as either a double array or a dataset object. Optional input options is a structure containing options for how the function is to run (see below). Outputs are the fraction of missing data flag, a map of the locations of the missing data as an unint8 variable missmap, and the data with the missing values filled in infilled. Depending on the plots option, a plot of the missing data may also be output.
Options
- options = a structure array with the following fields:
- frac_ssq: [{0.95}] desired fraction between 0 and 1 of variance to be captured by the PCA model,
- max_pcs: [{5}] maximum number of PCs in the model, if 0, then it uses the mean,
- meancenter: ['no' | {'yes'}], tells whether to use mean centering in the algorithm,
- recalcmean: ['no' | {'yes'}], recalculate mean center after each cycle of replacement (may improve results for small matricies),
- display: [{'off'} | 'on'], governs level of display,
- tolerance: [{1e-6 100}] convergence criteria, the first element is the minimum change and the second is the maximum number of iterations,
- max_missing: [{0.4}] maximum fraction of missing data with which MDCHECK will operate, and
- toomuch: [{'error'} | 'exclude'] what action should be taken if too much missing data is found. 'error' exit with error message, 'exclude' will exclude elements (rows/columns/slabs/etc) which contain too much missing data from the data before replacement. 'exclude' requires a dataset object as input for (data),
- algorithm: [ {'svd'} | 'nipals' ] specified the missing data algorithm to use, NIPALS typically used for large amounts of missing data or large multi-way arrays.
Note: MDCHECK captures up to options.frac_ssq of the variance using options.max_pcs or fewer PCA components. The default options can be retreived using: options = mdcheck('options');.