PLS Model Building

PLS analysis is carried out on input data to build a multiple linear regression model that can then be applied to other data sets. Both training set and testing set are required as input, these can be created using the Partitioning or Row Splitter KNIME nodes. Input data must contain 2 or more columns of numerical data, for the independent variables (X) and the dependent variable (Y).

Backend implementation

Options

Column containing structure titles

Select the column containing the title for each structure

Include input columns in output data

If selected, output plot data will also contain all input columns

Output PLS model name

The name of the output PLS model should be entered here

X variables

Select the independent X variables for the analysis

Y variable

Select the dependent Y variable (must not be the same as any X variable)

Maximum number of PLS factors

Specify the maximum number of PLS factors to use in the regression model. Regression models are built for increasing numbers of PLS factors up to this number.

Stop adding PLS Factors when SD drops to N

Select this option to stop adding PLS factors when the standard deviation of the regression drops below the value specified in the text box. Using this option could result in fewer PLS factors than the number specified in the Maximum number of PLS factors box.

Autoscale X variables

Scale the X variables by dividing the values of each property by the standard deviation in the value of the property.

Eliminate X variables with [t-value] less than N

Eliminate X variables whose t-value is less than the value given in the text box.

Views

Log view of PLS Model Building

Log view of PLS Model Building

Program out view of PLS Model Building

Program out view of PLS Model Building

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