Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
When I tried to run the model using conversion (CPA) metric, it was successfully executed, but when I run robyn_output it returned me this error message
>>> Calculating clusters for model selection using Pareto fronts...
>> Auto selected k = 6 (clusters) based on minimum WSS variance of 5%
Error : One or both dimensions exceed the maximum (50000px).
- Use `options(ragg.max_dim = ...)` to change the max
Warning: May cause the R session to crash
In addition: There were 36 warnings (use warnings() to see them)
Error in clusterCollect$data : $ operator is invalid for atomic vectors
Provide reproducible example
Issues are often related to custom input data that is difficult to debug without. If necessary, please modify your data to mask real values and share a dataset that is able to reproduce the issue. Please also share your model configuration and exported JSON files if available.
the configuration that I used was the same like demo.R in the repo just different dataset
Environment & Robyn version
Make sure you're using the latest Robyn version before you post an issue.
Project Robyn
Describe issue
When I tried to run the model using
conversion
(CPA) metric, it was successfully executed, but when I runrobyn_output
it returned me this error messageProvide reproducible example
Issues are often related to custom input data that is difficult to debug without. If necessary, please modify your data to mask real values and share a dataset that is able to reproduce the issue. Please also share your model configuration and exported JSON files if available.
demo.R
in the repo just different datasetEnvironment & Robyn version
Make sure you're using the latest Robyn version before you post an issue.
3.10.5.9007
R version 4.2.3 (2023-03-15)