OOPSEG: a data smoothing program for quantitation and isolation of random measurement error

We describe a data smoothing program, OOPSEG, which automatically quantitates and filters the random measurement error in a given data series. The measurement error is initially guessed, and the Optimal Segments technique is used to filter a corresponding amount of variation from the data, thus generating a relatively smooth curve. The residuals about this smooth curve are tested for serial correlation. If correlation is detected, a new measurement error is guessed, and the data are filtered again. This process continues until a smooth curve has been found for which the residuals do not exhibit serial correlation. Such residuals represent the random component of the data, i.e. (presumably) the measurement error. The corresponding smooth curve thus approximates the original, error-free curve. The smooth curve and the estimated coefficient of variation of the data are returned. OOPSEG handles end effects by performing an expansion of the data set, smoothing this expanded data set, then deleting the artificial points before assembling the final, smooth curve. Also, data may be smoothed as a sequence of independent regions to accommodate time courses with known changes in experimental conditions. We suggest that OOPSEG may have numerous applications in data analysis and experimental design.