# trace.beta: Trace Plot In BayesBridge: Bridge Regression

## Description

Trace plot using expectation maximization.

## Usage

 ```1 2``` ```trace.beta(y, X, alpha=0.5, ratio.grid=exp(seq(-20,20,0.1)), tol=1e-9, max.iter=30, use.cg=FALSE, plot.it=FALSE) ```

## Arguments

 `y` An N dimensional vector of data. `X` An N x P dimensional design matrix. `alpha` A parameter. `ratio.grid` A grid of ratio=tau/sigma. `tol` The threshold at which the algorithm terminates. `max.iter` The maximum number of iterations to use. `use.cg` Use the conjugate gradient method. `plot.it` Plot it.

## References

Nicolas G. Poslon, James G. Scott, and Jesse Windle. The Bayesian Bridge. http://arxiv.org/abs/1109.2279.

`bridge.reg, bridge.EM`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# Load the diabetes data... data(diabetes, package="BayesBridge"); cov.name = colnames(diabetes\$x); y = diabetes\$y; X = diabetes\$x; # Center the data. y = y - mean(y); mX = colMeans(X); for(i in 1:442){ X[i,] = X[i,] - mX; } # Expectation maximization. out = trace.beta(y, X); ```

### Example output

```
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BayesBridge documentation built on May 29, 2017, 10:40 a.m.