# maclogp:
Measures of Uncertainty for Model Selection

The goal of maclogp is to compute measures of uncertainty for a model
selection method based on an information criterion. Two measures were
proposed by Liu,
et.al. The first measure is a kind of model confidence set that
measures the variation of model selection, called MAC. The second
measure focuses on error of model selection, called LogP. Another
similar model confidence set adapted from Bayesian Model Averaging can
also be computed using this package.

## Installation

You can install the released version of maclogp from github with:

`devtools::install_github("YuanyuanLi96/maclogp")`

## Example

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

```
library(maclogp)
set.seed(0)
n= 100
B=100
p=5
x = matrix(rnorm(n*p, mean=0, sd=1), n, p)
true_b = c(1:3, rep(0,p-3))
y = x%*% true_b+rnorm(n)
alpha=c(0.1,0.05,0.01)
data=list(x=x,y=y)
models=Models_gen(1:p)
result=MAC(models, data, B, alpha)#default selection criterion is "BIC".
plot_MAC(models, alpha, result$con_sets, p)
```

```
#> [[1]]
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] TRUE TRUE TRUE FALSE FALSE
#>
#> [[2]]
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] TRUE TRUE TRUE FALSE FALSE
#> [2,] TRUE TRUE TRUE TRUE FALSE
#>
#> [[3]]
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] TRUE TRUE TRUE FALSE FALSE
#> [2,] TRUE TRUE TRUE TRUE FALSE
#> [3,] TRUE TRUE TRUE FALSE TRUE
```

## References Liu, X., Li, Y. & Jiang, J. Simple measures of
uncertainty for model selection. *TEST* (2020).
https://doi.org/10.1007/s11749-020-00737-9.