Calculate the number of type 1 (false positive) and type 2 (false negative) errors in an analysis.
calculate_error_types(causal, significant, bonferroni, analysis_name)
| causal | column in dataset denoting causal SNPs. |
|---|---|
| significant | column in dataset denoting which SNPs are significant. |
| bonferroni | column in dataset denoting which SNPs are significant with Bonferroni correction. |
| analysis_name | name of the analysis. |
A tibble containing the number of true positives, false positives, false negatives and true negatives with each of the significance levels having their own row.
To use this function, the user must run any test with analysis_association()
and use load_results() to merge this with beta.txt from the
simulation. Then, augment_results() needs to be run in order to obtain
columns for information on which SNPs are causal, significant, and significant
with Bonferroni correction.
The standard significance level refers to the one used in
augment_results().