JAMMR_MASSIVE_comparison: A dataset containing results for reproducing Figure 5 in...
In igbucur/MASSIVE: MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Description
A dataset containing results for reproducing Figure 5 in Section 5 of the UAI article.
Usage
1
Format
A data frame containing evaluations of JAM-MR and MASSIVE in various
simulated scenarios.
- Algorithm
Name of algorithm, either JAM-MR or MASSIVE.
- valid
Integer number of valid instruments in the simulation.
- beta
Numeric value of beta (causal effect from X to Y) in the simulation.
- rmse
Root-mean-square error (RMSE) achieved by the algorithm.
- rmse_se
Standard error for RMSE metric across replications.
- rmse_lower_ci
Bootstrapped confidence interval 2.5% quantile for RMSE across replications.
- rmse_upper_ci
Bootstrapped confidence interval 97.5% quantile for RMSE across replications.
- N:sigma
Simulation configuration (number of observations):(noise magnitude).
igbucur/MASSIVE documentation built on Oct. 26, 2020, 1:26 a.m.
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Description
A dataset containing results for reproducing Figure 5 in Section 5 of the UAI article.
Usage
1 |
Format
A data frame containing evaluations of JAM-MR and MASSIVE in various simulated scenarios.
- Algorithm
Name of algorithm, either JAM-MR or MASSIVE.
- valid
Integer number of valid instruments in the simulation.
- beta
Numeric value of beta (causal effect from X to Y) in the simulation.
- rmse
Root-mean-square error (RMSE) achieved by the algorithm.
- rmse_se
Standard error for RMSE metric across replications.
- rmse_lower_ci
Bootstrapped confidence interval 2.5% quantile for RMSE across replications.
- rmse_upper_ci
Bootstrapped confidence interval 97.5% quantile for RMSE across replications.
- N:sigma
Simulation configuration (number of observations):(noise magnitude).
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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