DataLab is a compact statistics package aiming at exploratory data analysis. Please visit the DataLab Web site for more information....


Command: Math -> Scaling of Data...

The command Math/Scaling of Data... (toolbar button ), provides some commonly used methods of data scaling. After clicking this command the following window appears:

The scaling can be applied either to the entire dataset, to all columns or all rows, or to marked data only (again column- or row-wise). The type of marked data (type A or B, red or blue) is of no importance. The current state of the data table is copied into an auxiliary table when calling the scaling command, thus enabling the user to undo any changes as long as the scaling window is open.

The following scaling modes are available:

Mean centering The selected data items (columns, rows, or all data) are scaled in such a way that the mean of each item becomes zero.
Standardisation The selected data items are scaled to a zero mean and a standard deviation of 1.0. Please note that the standardisation of data may be problematic if the data contains unusually large outliers. In this case q-normalisation is a better alternative. Note: the standardisation applied to spectra is also called "standard normal variate" (SNV).
Constant sum The selected items are scaled to a constant sum defined by the parameter A.
Constant sum of squares The selected items are scaled to a constant sum of squares. The sum is specified by the parameter A.
Maximum amplitude The selected items are scaled in such a way that the maximum absolute value of each item becomes A.
Range The selected data values are scaled to cover a range between A and B.
Q Normalisation The selected data range is scaled to zero median and a difference between the median and the p-percentile of 1.0, with p (in %) given by the parameter A. Q normalisation is largely insensitive to outliers and should be used whenever you are expecting severe outliers.
Squashing Function The selected data range is compressed by applying a sigmoid function ("squashing function") to the interval [-1,+1]. The parameter A specifies the origin (offset) of the squashing function, the parameter B defines the slope of the function.