appe.lm {APPEstimation} | R Documentation |

##
`L_1`

and `L_2`

errors adjusted for predictor distributions

### Description

Calculates adjusted `L_1`

and `L_2`

errors by predictor
distributions for a linear model.

### Usage

```
appe.lm(mdl, dat.train, dat.test, method = "uLSIF", sigma = NULL,
lambda = NULL, kernel_num = NULL, fold = 5, stabilize = TRUE,
qstb = 0.025, reps = 2000, conf.level = 0.95)
```

### Arguments

`mdl` |
a |

`dat.train` |
same as in |

`dat.test` |
same as in |

`method` |
same as in |

`sigma` |
same as in |

`lambda` |
same as in |

`kernel_num` |
same as in |

`fold` |
same as in |

`stabilize` |
same as in |

`qstb` |
same as in |

`reps` |
same as in |

`conf.level` |
same as in |

### Value

Adjusted and non-adjusted estimates of `L_1`

and `L_2`

errors
are provided as matrix form.
"L1" and "L2" indicate non-adjusted versions, "L1 adjusted by score"
and "L2 adjusted by score" indicate adjusted versions by linear
predictors distribution, "L1 adjusted by predictors" and
"L2 adjusted by predictors" indicate adjusted versions by
predictor distributions (multi-dimensionally).
For confidence intervals, "Percentile" indicates a confidence interval
by percentile method and "Approx" indicates approximated versions
by Normal distribution.

### Examples

```
set.seed(100)
# generating development data
n0 = 100
Z = cbind(rbeta(n0, 3, 3), rbeta(n0, 3, 3))
Y = apply(Z, 1, function(xx) { rlnorm(1, sum(c(1, 1) * xx), 0.3) })
dat = data.frame(Za=Z[,1], Zb=Z[,2], Y=Y)
# the model to be evaluated
mdl = lm(Y~ Za + Zb, data=dat)
# generating validation dataset
n1 = 100
Z1 = cbind(rbeta(n0, 3.5, 2.5), rbeta(n0, 3.5, 2.5))
Y1 = apply(Z1, 1, function(xx) { rlnorm(1, sum(c(1, 1) * xx), 0.3) })
dat1 = data.frame(Za=Z1[,1], Zb=Z1[,2], Y=Y1)
# calculation of L1 and L2 for this model
appe.lm(mdl, dat, dat1, reps=0)
```

*APPEstimation*version 0.1.1 Index]