# plotCGP: Jackknife (leave-one-out) actual by predicted diagnostic plot In CGP: Composite Gaussian Process Models

## Description

Draw jackknife (leave-one-out) actual by predicted plot to measure goodness-of-fit.

## Usage

 `1` ```plotCGP(object) ```

## Arguments

 `object` An object of class "`CGP`"

## Details

Draw the actual observed values on the y-axis and the jackknife (leave-one-out) predicted values on the x-axis. The goodness-of-fit can be measured by how well the points lie along the 45 degree diagonal line.

## Value

This function draws the jackknife (leave-one-out) actual by predicted plot.

## Author(s)

Shan Ba <shanbatr@gmail.com> and V. Roshan Joseph <roshan@isye.gatech.edu>

## References

Ba, S. and V. Roshan Joseph (2012) “Composite Gaussian Process Models for Emulating Expensive Functions”. Annals of Applied Statistics, 6, 1838-1860.

`CGP`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```x1<-c(0,.02,.075,.08,.14,.15,.155,.156,.18,.22,.29,.32,.36, .37,.42,.5,.57,.63,.72,.785,.8,.84,.925,1) x2<-c(.29,.02,.12,.58,.38,.87,.01,.12,.22,.08,.34,.185,.64, .02,.93,.15,.42,.71,1,0,.21,.5,.785,.21) X<-cbind(x1,x2) yobs<-x1^2+x2^2 ## Not run: #The CGP model mod<-CGP(X,yobs,nugget_l=0.001) plotCGP(mod) ## End(Not run) ```

### Example output

```There were 50 or more warnings (use warnings() to see the first 50)
```

CGP documentation built on May 2, 2019, 1:09 p.m.