PR addressing Issue #43 #191
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Thanks @Max-Bladen. Can you please add a unit test that tests for this issue, where the test fails before the fix and passes after it? If this behaviour might be affecting other functions, it would be great to add tests for those as well. |
Included a test to check that the error rate values yielded by `perf.mint.splsda()` and `tune.mint.splsda()` are the same (to 4 decimal places). This is checked for all three distance metrics, using both standard and balanced error rates.
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@aljabadi latest commit adds a test which checks for homogenity between the two methods for all distance ( |
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After extensive exploration, it was determined that the
overallerror rates were consistent across the two functions (perf.mint.splsda()andtune.mint.splsda()). Hence, only theBERcalculation needed to be rectified.The inconsistency was caused by the way the global BER was calculated in each function:
tune.mint.splsda(): Calculated the confusion matrix of a model for each study and then found the mean of these study-specific BER valuesperf.mint.splsda(): Calculated the confusion matrix of a model on all samples, irrespective of study. A single BER was yielded from this.The
perf.mint.splsda()had its functionality changed such that it was brought in line with the way in whichtune.mint.splsda()calculated global BER.The way in which
global$overallis calculated inperf.mint.splsda()was only adjusted for consistency withglobal$BER- the outputted values in the previousoverallmethod and newoverallmethod are the same.A minor addition was made to BOTH functions (in the case of
tune.mint.splsda(), this occured inLOGOCV()). Rather than the global BER being just a basic average of each study-specific BER, both now find the average BER across studies, weighted by the study sample sizes