Exposure mixtures settings
Calculation settings
| Name | Type | Description |
|---|---|---|
Risk type
|
ExposureType | The type of exposure considered in the assessment; acute (short term) or chronic (long-term). |
Target level
|
TargetLevelType | Select to express hazard characterisations at external or internal exposure level. For an aggregate assessement, that is dietary and nondietary exposure data are combined, the target dose level is always internal. When only dietary exposures are available, the target dose level is optional, i.c. external or internal. |
Internal concentration
|
InternalConcentrationType | Internal concentrations are derived form dietary and/or non-dietary concentrations and aggregated using a kinetic or absorption factor model or are human monitoring concentrations. |
Substance weighting
|
ExposureApproachType | Risk based (exposures in equivalents of the reference substance) or standardised (standardised exposures per substance have variance 1). |
Sparseness parameter
|
Numeric |
Sparseness parameter. Value between 0 (not sparse, many substances) and 1 (sparse, few substances). |
SNMU: number of components
|
Numeric |
The number of components to select in SNMU. |
Iterations
|
Numeric |
Maximum number of iterations, e.g. 1000. |
Seed for pseudo-random number generator.
|
Numeric |
Random seed for initialising matrix W and H. |
Convergence criterion
|
Numeric |
Convergence criterion for factorisation algorithm. |
Cutoff MCR
|
Numeric |
For selection of individual(day) exposures with maximum cumulative ratio (MCR = total exposure/maximum) above the cutoff. |
Cutoff percentage in population ranked on total exposure
|
Numeric |
For selection of individual(day) exposures above the cutoff percentage in the set of individual(day)s ranked on total exposure. |
Number of clusters
|
Numeric |
Number of clusters for hierarchical cluster analysis or clustering minimizing within-cluster variance (k-means). |
Method
|
ClusterMethodType | After component selection, a clustering maybe applied on the individuals coefficents matrix V (or H) using K-Means or hierarchical clustering to identify populations sub-groupings. |
Determine number of clusters automatically
|
Boolean |
For hierarchical clustering the number of clusters is determined automatically. |
Network analysis typemethod
|
NetworkAnalysisType | Network analysis on the substance x component (U) matrix. |