Regression analysis advanced (analysis)

Analysis title

Regression analysis advanced

Provider

Institute of Systems Biology

Class

RegressionAnalysisAdvanced

Plugin

biouml.plugins.machinelearning (Machine learning)

Description

Create and save regression model or load regression model for prediction of response or cross-validation of regression model.

Parameters:

• Regression mode – Select regression mode
• Regression type – Select regression type
• Path to data matrix – Path to table or file with data matrix
• Variable names – Select variable names
• Response name – Select response name
• Path to folder with saved model – Path to folder with saved model
• Parameters for OLS-regression – Please, determine parameters for Odinary least squares regression
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
• Parameters for WLS-regression – Please, determine parameters for Weighted least squares regression
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
• Parameters for PC-regression – Please, determine parameters for Principal component regression
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
  • Number of principal components – Number of principal components
  • Principal component sorting type – Sorting type of principal components
• Parameters for Tree-based regression – Please, determine parameters for Tree-based regression
  • Minimal node size – Minimal size of node
  • Minimal variance – Minimal variance
• Parameters for Ridge regression – Please, determine parameters for Ridge regression
  • Max number of rotations – Maximal number of rotations for calculation of inverse matrix or eigen vectors
  • Epsilon for rotations – Epsilon for calculation of inverse matrix or eigen vectors
  • Shrinkage parameter – Shrinkage parameter, k >= 0
• Parameters for combined regression – Please, determine parameters for combined regression
  • Number of regressions – Number of regressions
  • Regressions type – Select regressions type
  • Number of variables for regressions – Number of variables for each regression
  • Number of outlier detection steps – Number of outlier detection steps
  • Multiplier for sigma, t – Observation x is outlier if Abs(x - predicted x) > t * sigma
  • Classification type – Select classification type
  • Number of variables for classification – Number of variables for classification
  • Type of variable selection in classification – Type of variable selection in classification
• Parameters for cross-validation – Please, determine parameters for cross-validation
  • Percentage of data for training – Proportion (in %) of data for training
• Parameters for variable selection – Parameters for variable selection
  • Number of selected variables – Number of selected variables
  • Variable selection criterion – Please, determine variable selection criterion
  • Variable selection type – Please, determine variable selection type
• Parameters for outlier detection – Parameters for outlier detection
  • Multiplier for sigma, t – Observation x is outlier if Abs(x - predicted x) > t * sigma
  • Number of outlier detection steps – Number of outlier detection steps
• Path to output folder – Path to output folder