gbt.ksval {agtboost} | R Documentation |

## Kolmogorov-Smirnov validation of model

### Description

`gbt.ksval`

transforms observations to U(0,1) if the model
is correct and performs a Kolmogorov-Smirnov test for uniformity.

### Usage

```
gbt.ksval(object, y, x)
```

### Arguments

`object` |
Object or pointer to object of class |

`y` |
Observations to be tested |

`x` |
design matrix for training. Must be of type |

### Details

Model validation of model passed as `object`

using observations `y`

.
Assuming the loss is a negative log-likelihood and thus a probabilistic model,
the transformation

`u = F_Y(y;x,\theta) \sim U(0,1),`

is usually valid.
One parameter, `\mu=g^{-1}(f(x))`

, is given by the model. Remaining parameters
are estimated globally over feature space, assuming they are constant.
This then allow the above transformation to be exploited, so that the
Kolmogorov-Smirnov test for uniformity can be performed.

If the response is a count model (`poisson`

or `negbinom`

), the transformation

`u_i = F_Y(y_i-1;x,\theta) + Uf_Y(y_i,x,\theta), ~ U \sim U(0,1)`

is used to obtain a continuous transformation to the unit interval, which, if the model is correct, will give standard uniform random variables.

### Value

Kolmogorov-Smirnov test of model

### Examples

```
## Gaussian regression:
x_tr <- as.matrix(runif(500, 0, 4))
y_tr <- rnorm(500, x_tr, 1)
x_te <- as.matrix(runif(500, 0, 4))
y_te <- rnorm(500, x_te, 1)
mod <- gbt.train(y_tr, x_tr)
gbt.ksval(mod, y_te, x_te)
```

*agtboost*version 0.9.3 Index]