Description Usage Arguments Details
View source: R/calculate_metrics.R
One use case for these metrics is to calculate the accuracy of a certain prediction compared to a reference trajectory. However, these metrics can also be used for other purposes, such as clustering of trajectories.
1 2 | calculate_metrics(dataset, model, metrics = dyneval::metrics$metric_id,
expression_source = dataset$expression)
|
dataset |
The first trajectory, in most cases a gold standard trajectory |
model |
The second trajectory, in most cases a predicted trajectory |
metrics |
Which metrics to evaluate. Check |
expression_source |
The expression data matrix, with features as columns.
|
Some metrics are asymmetric (see dyneval::metrics$symmetric
), in which case the order of the dataset and model parameters matters.
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