R: Kolmogorov-Smirnov validation of model

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 `ENSEMBLE`
`y` | Observations to be tested
`x` | design matrix for training. Must be of type `matrix`.

### 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,θ) \sim U(0,1),

is usually valid. One parameter, μ=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,θ) + Uf_Y(y_i,x,θ), ~ 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)

[Package agtboost version 0.9.3 Index]