Description Usage Arguments Details Value References See Also Examples

Different methods for calculating the difference between two vectors.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
generalME(o, p,
ignore = c("raw", "centered", "scaled", "ordered"),
geometry = c("real", "logarithmic", "geometric", "ordinal"),
measure = c("mad", "var", "sd"),
type = c("dissimilarity", "normalized", "similarity",
"reference", "formula", "name", "function"),
method = NULL)
MAE(o, p, type = "dissimilarity")
MAPE(o, p, type = "dissimilarity")
MSE(o, p, type = "dissimilarity")
RMSE(o, p, type = "dissimilarity")
CMAE(o, p, type = "dissimilarity")
CMSE(o, p, type = "dissimilarity")
RCMSE(o, p, type = "dissimilarity")
SMAE(o, p, type = "dissimilarity")
SMSE(o, p, type = "dissimilarity")
RSMSE(o, p, type = "dissimilarity")
MALE(o, p, type = "dissimilarity")
MAGE(o, p, type = "dissimilarity")
RMSLE(o, p, type = "dissimilarity")
RMSGE(o, p, type = "dissimilarity")
SMALE(o, p, type = "dissimilarity")
SMAGE(o, p, type = "dissimilarity")
SMSLE(o, p, type = "dissimilarity")
RSMSLE(o, p, type = "dissimilarity")
RSMSGE(o, p, type = "dissimilarity")
MAOE(o, p, type = "dissimilarity")
MSOE(o, p, type = "dissimilarity")
RMSOE(o, p, type = "dissimilarity")
``` |

`o` |
vector of observed values |

`p` |
vector of corresponding predicted values |

`type` |
one of |

`ignore` |
specifies which aspects should be ignored: |

`geometry` |
indicating the geometry to be used for the data and
the output, |

`measure` |
indicates how distances should be measured: as mean absolute distances like in MAD, as squared distances like in a variance, or as the root of mean squared distances like in sd. |

`method` |
optionally the function to be used can specified directly as a function or as a string. |

These comparison criteria are designed for a semiquantitative
comparison of observed values `o`

with predicted values
`p`

to validate the performance of the prediction.

The general naming convention follows the grammar scheme

`[R][C|S]M[S|A][L|G|O]E`

corresponding to
`[Root] [Centered | Scaled] Mean [Squared | Absolute]`

`[Logarithmic, Geometric, Ordinal] Error`

- Root
is used together with squared errors to indicate, that a root is applied to the mean.

- Centered
indicates that an additive constant is allowed.

- Scaled
indicates that a scaling of the predictive sequence is allowed. Scaled implies centered for real scale.

- Squared
indicates that squared error is used.

- Absolute
indicates that absolute error is used.

- Logarithmic
indicates that the error is calculated based on the logarithms of the values. This is useful for data on a relative scale.

- Geometric
indicates that the result is to be understood as a factor, similar to a geometric mean.

- Ordinal
indicates that only the order of the observations is taken into account by analyzing the data by ranks scaled to the interval [0, 1].

The mean errors for squared error measures are based on the number of degrees of freedom of the residuals.

`generalME` |
selects the best deviance measure according to the description given in the parameters. It has the two additional possibilities of name and function in the type parameter. |

`MAE` |
mean absolute error |

`MAPE` |
mean absolute percentage error |

`MSE` |
mean squared error |

`RMSE` |
root mean squared error |

`CMAE` |
centered mean absolute error |

`CMSE` |
centered mean squared error |

`RCMSE` |
root centered mean squared error |

`SMAE` |
scaled mean absolute error |

`SMSE` |
scaled mean squared error |

`RSMSE` |
root scaled mean squared error |

`MALE` |
mean absolute logarithmic error |

`MAGE` |
mean absolute geometric error |

`MSLE` |
mean squared logarithmic error |

`MSGE` |
mean squared geometric error |

`RMSLE` |
root mean squared logarithmic error |

`SMALE` |
scaled mean absolute logarithmic error |

`SMAGE` |
scaled mean absolute relative error |

`SMSLE` |
scaled mean squared logarithmic error |

`RSMSLE` |
root scaled mean squared logarithmic error |

`RSMSGE` |
root scaled mean squared geometric error |

`MAOE` |
mean absolute ordinal error |

`MSOE` |
mean squared ordinal error |

`RMSOE` |
root mean squared ordinal error |

Mayer, D. G. and Butler, D. G. (1993) Statistical Validation. Ecological Modelling, 68, 21-32.

Jachner, S., van den Boogaart, K.G. and Petzoldt, T. (2007) Statistical methods for the qualitative assessment of dynamic models with time delay (R package qualV), Journal of Statistical Software, 22(8), 1–30. doi: 10.18637/jss.v022.i08.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ```
data(phyto)
obsb <- na.omit(obs[match(sim$t, obs$t), ])
simb <- sim[na.omit(match(obs$t, sim$t)), ]
o <- obsb$y
p <- simb$y
generalME(o, p, ignore = "raw", geometry = "real")
MAE(o, p)
MAPE(o, p)
MSE(o, p)
RMSE(o, p)
CMAE(o, p)
CMSE(o, p)
RCMSE(o, p)
SMAE(o, p)
SMSE(o, p)
RSMSE(o, p)
MALE(o, p)
MAGE(o, p)
RMSLE(o, p)
RMSGE(o, p)
SMALE(o, p)
SMAGE(o, p)
SMSLE(o, p)
RSMSLE(o, p)
RSMSGE(o, p)
MAOE(o, p)
MSOE(o, p)
RMSOE(o, p)
MAE(o, p)
MAPE(o, p)
MSE(o, p, type = "s")
RMSE(o, p, type = "s")
CMAE(o, p, type = "s")
CMSE(o, p, type = "s")
RCMSE(o, p, type = "s")
SMAE(o, p, type = "s")
SMSE(o, p, type = "s")
RSMSE(o, p, type = "s")
MALE(o, p, type = "s")
MAGE(o, p, type = "s")
RMSLE(o, p, type = "s")
RMSGE(o, p, type = "s")
SMALE(o, p, type = "s")
SMAGE(o, p, type = "s")
SMSLE(o, p, type = "s")
RSMSLE(o, p, type = "s")
RSMSGE(o, p, type = "s")
MAOE(o, p, type = "s")
MSOE(o, p, type = "s")
RMSOE(o, p, type = "s")
``` |

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