qualV (version 0.2-5)

quantV: Quantitative Validation Methods

Description

Different methods for calculating the difference between two vectors.

Usage

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")

Arguments

o

vector of observed values

p

vector of corresponding predicted values

type

one of "dissimilarity", "normalized", "similarity", "reference", "formula", for the dissimilarity measure, the normalized dissimilarity measure, the similarity measure, or the

ignore

specifies which aspects should be ignored: "raw" compares original values, "centered" removes differences in mean, "scaled" ignores scaling, "ordered" indicates the use of the ordinal geometr

geometry

indicating the geometry to be used for the data and the output, "real" corresponds to arithmetic differences and means, "logarithmic" to handling relative data on a logarithmic scale, "geometric" to geome

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.

Value

• 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 $\frac1n$
• 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

Details

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 [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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

References

Mayer, D. G. and Butler, D. G. (1993) Statistical Validation. Ecological Modelling, 68, 21-32. Jachner, S., K.G. v.d. Boogaart, T. Petzoldt (2007) Statistical methods for the qualitative assessment of dynamic models with time delay (R package qualV), Journal of Statistical Software, 22(8), 1--30. URL http://www.jstatsoft.org/v22/i08/.

See Also

EF, GRI, compareME

Examples

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")