README.Rmd

---
output: github_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  fig.path = "man/figures/README-",
  out.width = "100%",
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 70)
)
```

# OSCAR models with R

Optimal Subset CArdinality Regression (OSCAR) models with R

# Citation

Halkola et al. OSCAR: Optimal subset cardinality regression using the L0-pseudonorm, with applications to modelling prostate cancer biomarkers.

# CRAN

The ```oscar``` package is available from the Central R Archive Network (CRAN), and can be installed with:

```{r cran, eval = FALSE, warning = FALSE, message = FALSE}
install.packages("oscar")
```

Canonical direct CRAN link:

https://CRAN.R-project.org/package=oscar 

# GitHub installation

While the CRAN installation may be most convenient for user installation, the latest modifications and package release come through GitHub. Therefore there may be a slight lag in delivering the latest release to CRAN, and the user may wish to install the latest GitHub-version by:

```{r github, eval = FALSE, warning = FALSE, message = FALSE}
install.packages("devtools")
devtools::install_github("Syksy/oscar")
```

# Brief examples

Below are brief examples on ```oscar``` usage. More elaborate examples can be found from within the package in the examples vignette.

```{r example, eval = FALSE, warning = FALSE, message = FALSE, tidy.opts=list(width.cutoff=70)}

# Load the oscar-package  

library(oscar) 

# Load example dataset (consists of ex_X, ex_Y, ex_K and ex_c) for Cox regression  

data(ex) 

# Test run, notice this uses all the data! Smaller test would be feasible  

fit <- oscar::oscar(x=ex_X, y=ex_Y, k=ex_K, w=ex_c, family="cox") 

# Show model results and other attributes  

fit 

# Visualize fit as a function of allowed kits  

oscar::oscar.visu(fit, y=c("target", "goodness")) 

# Example of cross-validation based k-selection  

cv <- oscar::oscar.cv(fit, fold=5, seed=123) # 5-fold cross-validation, with fixed seed  

# Visualize the cross-validation curve (highest point in c-index in optimal generalizable k-value)  

oscar::oscar.cv.visu(cv) 

# Naive example using events in logistic regression (placeholder PoC, end-point ought to be time-dependent rather)  

fit2 <- oscar::oscar(x=ex_X, y=ex_Y[,2], k=ex_K, w=ex_c, family="logistic")  

oscar::oscar.visu(fit2, y=c("target", "goodness"))  

# Example swiss fertility data  

data(swiss)  

fit_mse <- oscar::oscar(x=swiss[,-1], y=swiss[,1], family="gaussian")  

fit_mse

```