Single-Series Transformations (x=f(x))

Creates single-series transformations of the kind x=f(x) for selected continuous variables; computes a summary graph and spreadsheet of the series after performing transformation; autocorrelation functions can also be computed.

 Available transformations include: Adding a constant, power transformation, inverse power transformation, natural log transformation, exponent transformation, mean subtraction, standardization, trend subtraction, and autocorrelation subtraction.

Element Name Description
General
Type of transformation Select the desired type of transformation.
Constant C Specifies Constant C for transformation. Add a constant (x=x+C); constant C is added to all values in the transformation variables.
Estimate parameters from data If you select this option, the mean and/or standard deviation, the intercept (a) and slope (b) are computed from the data (via least squares regression) for the transformations:

 Mean subtract
 Standardize
 Trend subtract
 Autocorr. subtract
User-defined mean Only applicable if Estimate parameters from data is False, and for some transformations. Specify a user-defined mean the transformations:

 Mean subtract
 Standardize
User-defined std deviation Only applicable if Estimate parameters from data is False, and for some transformations. Specify a user-defined standard deviation for the transformations:

 Mean subtract
 Standardize
User-defined intercept Only applicable if Estimate parameters from data is False, and for some transformations. Specify a user-defined intercept parameter for the transformations:

 Trend subtract
 Autocorr. subtract
User-defined slope Only applicable if Estimate parameters from data is False, and for some transformations. Specify a user-defined slope parameter (parameter b) for the transformations:

 Trend subtract
 Autocorr. subtract
Number of lags Only applicable to Autocorrelation subtract transformation; specify the lag for which to subtract the autocorrelation.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.
Autocorrelations and Plots
Labels plots with case names Labels the horizontal axis in plots with case numbers or case names (if available).
Creates autocorrelations Creates the autocorrelation function for the differenced series.
Number of lags Enter a value in the Number of lags box to determine the maximum number of lags for which the autocorrelations are to be computed.
Missing Data
Replace missing data Specifies how missing data is to be replaced. Missing data can be replaced by the overall mean, interpolated from adjacent points, replaced by the mean or median of N adjacent points (on both sides of the hole), or estimated (predicted) from linear trend regression.

 Note that as long as the missing data are at the end of the series (trailing missing data) or the beginning of the series (leading missing data), the missing data will simply be ignored.
Number of adjacent points Applicable if missing data are replaced by the mean or median of N adjacent points; specify N.

 The missing data are replaced by the mean or median computed from the N adjacent points on both sides of the hole of missing data.