CoOL_6_individual_effects_matrix: Risk contribution matrix based on individual effects (had all...
In ekstroem/CoOL: Causes of Outcome Learning
CoOL_6_individual_effects_matrix R Documentation
Risk contribution matrix based on individual effects (had all other exposures been set to zero)
Description
Estimating the risk contribution for each exposure if each individual had been exposed to only one exposure, with the value the individual actually had.
Usage
CoOL_6_individual_effects_matrix(X, model)
Arguments
X
The exposure data.
model
The fitted the non-negative neural network.
Value
A matrix [Number of individuals, exposures] with the estimated individual effects by each exposure had all other values been set to zero.
References
Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <https://doi.org/10.1093/ije/dyac078>
Examples
#See the example under CoOL_0_working_example
ekstroem/CoOL documentation built on June 1, 2022, 12:33 p.m.
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| CoOL_6_individual_effects_matrix | R Documentation |
Risk contribution matrix based on individual effects (had all other exposures been set to zero)
Description
Estimating the risk contribution for each exposure if each individual had been exposed to only one exposure, with the value the individual actually had.
Usage
CoOL_6_individual_effects_matrix(X, model)
Arguments
X |
The exposure data. |
model |
The fitted the non-negative neural network. |
Value
A matrix [Number of individuals, exposures] with the estimated individual effects by each exposure had all other values been set to zero.
References
Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <https://doi.org/10.1093/ije/dyac078>
Examples
#See the example under CoOL_0_working_example
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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