# MetricsWeighted

The goal of this package is to provide weighted versions of metrics, scoring functions and performance measures for machine learning.

## Installation

You can install the released version of MetricsWeighted from CRAN with:

`install.packages("MetricsWeighted")`

To get the bleeding edge version, you can run

```
library(devtools)
install_github("mayer79/MetricsWeighted")
```

## Application

There are two ways to apply the package. We will go through them in the following examples. Please have a look at the vignette on CRAN for further information and examples.

## Example 1: Directly apply the metrics

```
library(MetricsWeighted)
y <- 1:10
pred <- c(2:10, 14)
rmse(y, pred)
rmse(y, pred, w = 1:10)
r_squared(y, pred)
r_squared(y, pred, deviance_function = deviance_gamma)
```

## Example 2: Call the metrics through a common function that can be used within a `dplyr`

chain

```
dat <- data.frame(y = y, pred = pred)
performance(dat, actual = "y", predicted = "pred")
performance(dat, actual = "y", predicted = "pred", metrics = r_squared)
performance(dat, actual = "y", predicted = "pred",
metrics = list(rmse = rmse, `R-squared` = r_squared))
performance(dat, actual = "y", predicted = "pred",
metrics = list(deviance = deviance_gamma, pseudo_r2 = r_squared_gamma))
```