Med2top: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
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For fitting to the bottom: | For fitting to the bottom: | ||
>> tsq = residual/res; % (res) is an input | <pre>>> tsq = residual/res; % (res) is an input | ||
>> tsqst = ttestp(1-options.tsqlim,5000,2); % T-test limit from table | >> tsqst = ttestp(1-options.tsqlim,5000,2); % T-test limit from table | ||
>> ii = find(tsq>-tsqst); % finds samples below the line | >> ii = find(tsq>-tsqst); % finds samples below the line</pre> | ||
The ii samples are kept for the next estimate of (yf): | The ii samples are kept for the next estimate of (yf): | ||
>> yf = median(y(ii)); | <pre>>> yf = median(y(ii));</pre> | ||
====Inputs==== | ====Inputs==== | ||
Revision as of 15:43, 7 October 2008
Purpose
Fits a constant to top/(bottom) of data.
Synopsis
- [yf,residual,options] = med2top(y,options)
Description
MED2TOP is similar to LSQ2TOP with a 0 order polynomial, it can be considered an asymmetric estimate of the mean.
For fitting to the bottom:
>> tsq = residual/res; % (res) is an input >> tsqst = ttestp(1-options.tsqlim,5000,2); % T-test limit from table >> ii = find(tsq>-tsqst); % finds samples below the line
The ii samples are kept for the next estimate of (yf):
>> yf = median(y(ii));
Inputs
- y = trace to be filtered, Mx1 vector.
Outputs
- yf = scalar, estimate of filtered data.
- residual = y - yf.
- options = input options echoed back, the field initwt may have been modified.
Options
- ''' options = a structure array with the following fields.
- display: [ {'off'} | 'on'] Governs screen display to command line.
- trbflag: [ {'top'} | 'bottom' | 'middle'] flag that tells algorithm to fit to the top, bottom, or middle of the data cloud.
- tsqlim: [ 0.99 ] limit that govers whether a data point is outside the fit residual defined by input (res).
- stopcrit: [1e-4 1e-4 1000 360] stopping criteria, iteration is continued until one of the stopping criterion is met [(rel tol) (abs tol) (max \# iterations) (max time [seconds])].
- initwt: [ ] empty or Mx1 vector of initial weights (0<=w<=1).