This contains a series of R scripts to reproduce the simulation results in the above-titled paper.
You will need a recent version of R, along with the following packages c("stdReg", "ggplot2", "data.table", "parallel", "sandwich")
.
This uses the targets
package for reproducibility. Read more about how to use it here: https://books.ropensci.org/targets/
- Download the repository
- Adjust the parameters
B
andcores
andboots
in the_targets.R
script, then source it. - Run
tar_make()
ortar_make_clustermq()
to run the simulation. Warning, it takes a long time if you use a lot of replicates because each simulation replicate includes bootstrapping. - Inspect the results using
tar_read
on one of the target names, with, e.g.,tar_read(sim_summary)
. Also there is a pdf generated for the example analysis.
source("_targets.R")
tar_make()
tar_read(sim_summary)
tar_read(funk_compare)
tar_read(odd_text)
graph LR
subgraph legend
direction LR
x7420bd9270f8d27d([""Up to date""]):::uptodate --- xbf4603d6c2c2ad6b([""Stem""]):::none
end
subgraph Graph
direction LR
xda319e07d75bcf17(["simulation_results"]):::uptodate --> x389ac23a8bc5790a(["sdev_table"]):::uptodate
xda319e07d75bcf17(["simulation_results"]):::uptodate --> x528a48b3a0beaa4a(["funk_compare"]):::uptodate
xda319e07d75bcf17(["simulation_results"]):::uptodate --> x096535d843ac58b4(["odd_text"]):::uptodate
xda319e07d75bcf17(["simulation_results"]):::uptodate --> x8df1cfa1cec0f4ca(["sim_summary"]):::uptodate
x9e69104330fdf6f8(["simulate_inverse_gaussian_inverse_gaussian_weighted_standardized_2000_200"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x71467bd26b2548ad(["simulate_linear_linear_compare_100_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
xe01042d5af18e30e(["simulate_linear_linear_compare_1000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x116f146dea47cfe7(["simulate_linear_linear_compare_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x9eae9ce78ce473f4(["simulate_linear_linear_compare_500_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x00130b7e30e0600b(["simulate_linear_ols_weighted_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x9d6fa315a53f6e26(["simulate_linear_ols_weighted_standardized_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x1e015faaad899be1(["simulate_linearodd_ols_weighted_standardized_odd_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x5ea0a7387f8b2ef9(["simulate_log_poisson_poisson_weighted_standardized_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
xac3d6e69f58e5f12(["simulate_logit_binomial_log_binomial_weighted_standardized_2000_20"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x99723e3e80a40ee6(["simulate_logit_binomial_logit_binomial_weighted_standardized_2000_2"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x4cc6fc8d5fa9053a(["simulate_logit_binomial_logit_compare_100_5"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
x7649e25e048e52a8(["simulate_logit_binomial_logit_compare_1000_5"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
xee6f069e485dd697(["simulate_logit_binomial_logit_compare_2000_5"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
xe7cf092df7d54f75(["simulate_logit_binomial_logit_compare_500_5"]):::uptodate --> xda319e07d75bcf17(["simulation_results"]):::uptodate
xe6eda53558c41c5e(["example"]):::uptodate --> xe6eda53558c41c5e(["example"]):::uptodate
end
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