On Python, by scikit-learn, we can do it.
I'll use air quality data to try it.
To prepare the data, on R console, execute the following code on your working directory.
write.csv(airquality, "airquality.csv", row.names=FALSE)
write.csv(airquality, "airquality.csv", row.names=FALSE)
An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.
Hierarchical Bayesian model by Stan: Struggling
I'll try to make Hierarchical Bayesian model to the artificial data by Stan. Hierarchical Bayesian model lets us write the model with a high degree of freedom.
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method.[1] The sub-models combine to form the hierarchical model, and the Bayes’ theorem is used to integrate them with the observed data, and account for all the uncertainty that is present. The result of this integration is the posterior distribution, also known as the updated probability estimate, as additional evidence on the prior distribution is acquired.
What is heteroscedasticity and How to check it on R
Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have. Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.
This time, I’ll make the model again but with Python and Stan.How to deal with heteroscedasticity
On the article below, I wrote about heteroscedasticity. Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have. Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.