https://github.com/akai01/micecon

Microeconometric analysis of social housing in Austria.
https://github.com/akai01/micecon

automl bayesian-optimization boosting-algorithms microeconometrics r

Last synced: 9 months ago
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Microeconometric analysis of social housing in Austria.

• Host: GitHub
• URL: https://github.com/akai01/micecon
• Owner: Akai01
• License: other
• Created: 2020-02-20T14:15:24.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-07T08:38:56.000Z (over 5 years ago)
• Last Synced: 2025-04-12T21:14:07.267Z (about 1 year ago)
• Topics: automl, bayesian-optimization, boosting-algorithms, microeconometrics, r
• Language: R
• Homepage:
• Size: 73.2 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE.md

Awesome Lists containing this project

README

          
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# MicEcon

[![CRAN status](https://www.r-pkg.org/badges/version/MicEcon)](https://CRAN.R-project.org/package=MicEcon)

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

The goal of MicEcon is to ...

## Installation

``` r

if(!require(devtools)){

install.packages("devtools")

}

if(!require(catboost)){

devtools::install_github('catboost/catboost', subdir = 'catboost/R-package')

}

devtools::install_github("Akai01/MicEcon")

```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}

# A toy example

library(MicEcon)

data(iris, package = "datasets")

fit <- auto_catboost_reg(

  iris,

  label_col_name = "Petal.Length",

  cat_features = "Species",

  has_time = FALSE,

  fold_count = 3,

  type = "Classical",

  partition_random_seed = 0,

  shuffle = TRUE,

  stratified = FALSE,

  early_stopping_rounds = NULL,

  iterations = list(lower = 100, upper = 110),

  learning_rate = list(lower = 0.001, upper = 0.05),

  l2_leaf_reg = list(lower = 0, upper = 5),

  depth = list(lower = 1, upper = 10),

  bagging_temperature = list(lower = 0, upper = 100),

  rsm = list(lower = 0, upper = 1),

  border_count = list(lower = 1, upper = 254),

   logging_level = 'Silent',

  bo_iters = 2

)

varimp <- get_var_imp(fit$model)

plot_varimp(varimp)

```