# mvrsquared

Welcome to the `mvrsquared`

package! This package does one
thing: calculate the coefficient of determination or R-squared. However,
this implementation is different from what you may be familiar with. In
addition to the standard R-squared used frequently in linear regression,
`mvrsquared`

calculates R-squared for multivariate outcomes.
(This is why there is an ‘mv’ in `mvrsquared`

).

`mvrsquared`

implements R-squared based on a derivation in
this paper. It’s the same
definition of R-squared you’re probably familiar with (1 - SSE/SST) but
generalized to n-dimensions.

In the standard case, your outcome `y`

and prediction
`yhat`

are vectors. In other words, each observation is a
single number. This is fine if you are predicting a single variable. But
what if you are predicting multiple variables at once? In that case,
`y`

and `yhat`

are matrices. This situation occurs
frequently in topic modeling or simultaneous equation modeling.

### Installation

You can install from CRAN with

`install.packages("mvrsquared")`

You can get the development version with

```
install.packages("remotes")
remotes::install_github("tommyjones/mvrsquared")
```

### Check out the vignette to
see how to…

- Calculate the regular R-squared we all know and love!
- Calculate R-squared for multiple outcome variables at once (like a
multinomial regression)!
- Calculate R-squared for probabilistic (e.g. LDA) and
non-probabilistic (e.g. LSA) topic models!
- Split your BIG DATA into batches and calculate R-squared with a
parallel/distributed map-reduce framework!