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Remove the num_taxa argument from .computeEntropy() #91

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Remove the num_taxa argument from .computeEntropy() #91

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4 changes: 2 additions & 2 deletions tutorials/model_testing_ppred_inf/scripts/PosteriorPredictive_TreeSummary.Rev
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ for ( i in 1:num_post_sims) {
tree_length <- sim_tree_trace.computeTreeLengths()

## This calculates the entropy statistic
entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)


## calculate the stuff we care about for a single pps posterior
Expand Down Expand Up @@ -73,7 +73,7 @@ rf_dists <- emp_tree_trace.computePairwiseRFDistances(credibleTreeSetSize=1.0,ve
tree_length <- emp_tree_trace.computeTreeLengths()

## This calculates the entropy statistic
entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)

## calculate the stuff we care about for a single posterior
mean_rf = mean(rf_dists)
Expand Down
4 changes: 2 additions & 2 deletions tutorials/model_testing_pps/pps_inference.md
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Expand Up @@ -449,7 +449,7 @@ Example topological test statistic calculations from a posterior distribution {%
{% endfigure %}

Since these values are calculated by iterating over the entire series of posterior predictive analyses
they are a bit unweildy to run line by line. However, we will talk about some of the individual functions
they are a bit unwieldy to run line by line. However, we will talk about some of the individual functions
and how they work so if you wish you can develop your own test statistics at a later time.
Here are some examples of these functions from the
**PosteriorPredictive_TreeSummary.Rev** script:
Expand All @@ -469,7 +469,7 @@ Here are some examples of these functions from the
> various topologies become increasingly uneven, entropy decreases causing the difference between the entropy of the prior and the
> posterior to increase. Equation $\ref{equation1}$.

```entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,numTaxa=data.ntaxa(),verbose=FALSE)```
```entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)```

Assuming a uniform prior on topologies, the change in entropy can be calculated as:

Expand Down
4 changes: 2 additions & 2 deletions tutorials/model_testing_pps/scripts/PosteriorPredictive_TreeSummary.Rev
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ for ( i in 1:num_post_sims) {
tree_length <- sim_tree_trace.computeTreeLengths()

## This calculates the entropy statistic
entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)


## calculate the stuff we care about for a single pps posterior
Expand Down Expand Up @@ -73,7 +73,7 @@ rf_dists <- emp_tree_trace.computePairwiseRFDistances(credibleTreeSetSize=1.0,ve
tree_length <- emp_tree_trace.computeTreeLengths()

## This calculates the entropy statistic
entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)

## calculate the stuff we care about for a single posterior
mean_rf = mean(rf_dists)
Expand Down