# 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", subdir = "release/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: Standard interface

```
library(MetricsWeighted)
y <- 1:10
pred <- c(2:10, 14)
rmse(y, pred) # 1.58
rmse(y, pred, w = 1:10) # 1.93
r_squared(y, pred) # 0.70
r_squared(y, pred, deviance_function = deviance_gamma) # 0.78
```

### Example 2: data.frame interface

Can e.g. be used in a `dplyr`

chain.

```
dat <- data.frame(y = y, pred = pred)
performance(dat, actual = "y", predicted = "pred")
> metric value
> rmse 1.581139
performance(dat, actual = "y", predicted = "pred",
metrics = list(rmse = rmse, `R-squared` = r_squared))
> metric value
> rmse 1.5811388
> R-squared 0.6969697
```