Software Listing of Author : "Andrea Padoan"

INPUT: 3 columns array. column1 (the left): sample ID or sample number column2 and column3: instrument1 and instrument 2 outout GRAPH OUTPUT: Bland Altman scatter plot with mean difference line and +-2 SD lines. CONSOLE OUTPUT: diffmean = mean of the difference (the same you see in plot) sdev = standard deviation of the differences.

  • Platform: Matlab, Scripts

Decision tree learning is a common method used in data mining. Most of the commercial packages offer complex Tree classification algorithms, but they are very much expensive. This matlab code uses 'classregtree' function that implement GINI algorithm to determine the best split for each node. The main function of this code is named Tree. It imports data directly from an excel or csv file, using the first row as variable names (necessary). The first column is the outcome group and It must be numeric. To start the classification tree type in Matlab workspace: Tree('filename.xls') or Tree('filename.csv') (be careful that your excel file contains a first row with variable names and the outcome group in the first column). It can also import directly from matlab file (.mat extention). Please create a file with this 3 variables: X (matrix of...

  • Platform: Matlab, Scripts

When a large number of samples is analyzed by mass spectrometry instrument, a common issue is the multiple spectra peaks alignment. In fact, the statistical analysis generally requires a data matrix, easily to import from a file to the software. Furthermore, in a multiple mass spectrometry analyses, this kind of matrix must contains sample IDs as rows and identified features as columns. Mass spectrometry instrumentation and the concerning software gives as result a mass spectra for each sample and therefore a lots of files will be produced. The first analysis step is normally the peak finding. A lot of literature has been produced since the last 10 years about peak finding and also a lot of bioinformatic functions have been provided by Matlab in the bioinformatic tool. But, once that this step has been successful passed, the way to...

  • Platform: Matlab, Scripts

Background: Nowadays, various measures have been proposed to capture discrimination in diagnostic tests, but the area under the receiver operating characteristic (ROC) curve (AUC) is still the most popular metric. Researchers, usually, evaluate new bio-markers on their ability to increase the AUC. However, it quickly became apparent that, for models containing standard risk factors and possessing reasonably good discrimination, very large 'independent' associations of the new marker with the outcome are required to result in a meaningfully larger AUC. Pencina et. al, proposed a new way of assessing improvement named "Net Reclassification Improvement". The NRI focuses on reclassification tables constructed separately for participants with and without events, and quantifies the correct movement in categories d-deOCL upwards for events...

  • Platform: Matlab, Scripts

Passing & Bablok described this procedure in the 1983. This procedure is ideal if you want to compare 2 different methods (instrument) which measure the same chemical analyte in the same sample. Classical linear regression method assume that variables X and Y are normal distributed and with a measurement error costant over the range of concentrations. However, in method comparison studies we generally find that the distribution of measurements is not normal and that variance of errors is not costant. Passing & Bablok regression does not require specific assumptions regarding the distributions of the expected values or the error terms. Description of the Function PassingBablok (x,y): INPUT: x and y are arrays in column of values obtained measuring the same sample with 2 different analytical methods. It means that if you have i.e. i =...

  • Platform: Matlab, Scripts

Input: z value Output: One tail and two tail p-value.

  • Platform: Matlab, Scripts
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