# evaluator: Function to evaluate spatial quantiles In cepp: Context Driven Exploratory Projection Pursuit

## Description

This provides an objective function whose minimization yields the spatial quantiles.

## Usage

 `1` ```evaluator(n, p) ```

## Arguments

 `n` The number of rows in the data `p` The number of columns in the data

## Details

Returns another function suitable for passing to an optimizer like `nlm` or `trust`.

## Value

A function that should be passed to an optimizer.

Mohit Dayal

## References

P. Chaudhuri. "On a geometric notion of quantiles for multivariate data." Journal of the American Statistical Association, 91(434):862-872, 1996.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```x <- rnorm(500) dim(x) <- c(250,2) ev <- evaluator(250,2) ##The Spatial Median trust(ev, parinit=c(median(x[1,]), median(x[2,])), u=c(0,0), rinit=0.5, rmax=2e5, samp = x) ##Quantile for vector (0.2,0.3) trust(ev, parinit=c(median(x[1,]), median(x[2,])), u=c(0.2,0.3), rinit=0.5, rmax=2e5, samp = x) ```

### Example output

```Loading required package: trust
\$value
[1] 301.5399

[1] -8.647249e-12 -1.531967e-12

\$hessian
[,1]       [,2]
[1,] 176.571684  -5.100188
[2,]  -5.100188 148.483856

\$argument
[1] 0.05795514 0.07271672

\$converged
[1] TRUE

\$iterations
[1] 6

\$value
[1] 272.1432

[1] -4.121148e-13 -1.421085e-13

\$hessian
[,1]      [,2]
[1,] 170.99491  -1.82928
[2,]  -1.82928 137.38850

\$argument
[1] 0.3421160 0.5412897

\$converged
[1] TRUE

\$iterations
[1] 6
```

cepp documentation built on May 2, 2019, 3:44 p.m.