Hello, World!

This first post is to check whether the features I want for this blog work as desired.

These can be summarized in a non-exhaustive list as:

• math
• R / Python code
• interactive graphs (plotly, leaflet, …)
• syntax highlighting
• markdown
• …

Math

The OLS estimator is given by the equation \(\hat\beta_\text{OLS} = (X^\mathsf{T} X)^{-1} X^\mathsf{T} y\).

On the other hand, the ridge estimator is given by the following formula

\[\hat\beta_\text{ridge} = (X^\mathsf{T} X + \lambda I)^{-1} X^\mathsf{T} y\]

where \(\lambda \in [0, \infty)\) controls the amount of shrinkage applied to the coefficients.

R code

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)

p <- iris %>% 
  ggplot(aes(x = Petal.Length, y = Petal.Width, color = Species)) + 
  geom_point() + 
  theme_classic()

vanilla ggplot

plot(p)

plotly plot

plotly::ggplotly(p)