The program uses an iterative method to find the best-fit asymptote for a selected block of data, according to the simple model: Y = ln (asymptote - data).  You can chose any number of iterations between 3 and 50 (the default is 6).  Using a lot of iterations might increase the accuracy of the estimate (this doesn't always occur, especially for > 20 iterations), but will also increase the analysis time.  In practice, you usually don't need to use more than 6 to 10 iterations for good accuracy.  Note that the algorithm isn't terribly 'smart':   if you give it 'messy' data that do not conform reasonably well to first-order kinetics, the program may take a long time to produce an estimate, and that estimate may have fairly glaring errors (GIGO: garbage in, garbage out).

After completing the analysis, LabAnalyst shows the asymptote, the coefficient of determination or C.D. (an estimate of the precision of the fit of the data to the model, and hence the precision of the estimated asymptote), the rate constant (the fraction of the change between a starting value and the asymptote that is completed during 1 time unit), and the time to complete a fraction of the total change between a starting value and the asymptote (values from 1% to 99% are selectable from a pop-up menu).  You can use your choice of time units (seconds, minutes, hours, or days) for slopes and rate constants.

For relatively small block sizes, LabAnalyst also draws a goodness-of-fit plot that illustrates how closely the model matches the data for large blocks you can get this plot by clicking the 'Show Regression' button.  CAUTION:   for large files (e.g, > 1,000,000 points), plotting is a slow process.  As shown at right, points are plotted as the log (base e) of the absolute difference between the model predictions and the data:

Y value = ln (abs (asymptote - data))

Individual points are drawn only if the total number of points in the plot is less than 60.  In these examples, data are plotted in yellow on a red background and the best-fit regression line predicted by the model is white.  You can select other color combinations in the 'Plot Styles' option in the VIEW menu.   After the goodness-of-fit plot is drawn, you can also show the residuals from the regression, as shown below (Caution: this is slower than drawing the regression itself):

Some additional considerations:

• The time needed for finding an asymptote increases with the size of the data block (as well as the number of iterations).  On a typical machine, a block containing several thousand data points will be analyzed within a second or so. For large data blocks, the program will put up a notification that processing may be lengthy.
• You cannot send results to disk or printer by hitting the 'p' key, as usual.  Instead, use the 'print' button (this button is available only if output has been selected from the FILE menu).
• This option DOES NOT print to a tabular file (the output format is incompatible).