Mean squared error between `sim`

and `obs`

, in the squared units of `sim`

and `obs`

, with treatment of missing values.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`sim` |
numeric, zoo, matrix or data.frame with simulated values |

`obs` |
numeric, zoo, matrix or data.frame with observed values |

`na.rm` |
a logical value indicating whether 'NA' should be stripped before the computation proceeds. |

`...` |
further arguments passed to or from other methods. |

*
mse = mean( (sim - obs)^2, na.rm = TRUE) *

Mean squared error between `sim`

and `obs`

.

If `sim`

and `obs`

are matrixes, the returned value is a vector, with the mean squared error between each column of `sim`

and `obs`

.

`obs`

and `sim`

has to have the same length/dimension

The missing values in `obs`

and `sim`

are removed before the computation proceeds, and only those positions with non-missing values in `obs`

and `sim`

are considered in the computation

Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>

Yapo P. O., Gupta H. V., Sorooshian S., 1996. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology. v181 i1-4. 23-48

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 | ```
obs <- 1:10
sim <- 1:10
mse(sim, obs)
obs <- 1:10
sim <- 2:11
mse(sim, obs)
##################
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts
# Generating a simulated daily time series, initially equal to the observed series
sim <- obs
# Computing the mean squared error for the "best" case
mse(sim=sim, obs=obs)
# Randomly changing the first 2000 elements of 'sim', by using a normal distribution
# with mean 10 and standard deviation equal to 1 (default of 'rnorm').
sim[1:2000] <- obs[1:2000] + rnorm(2000, mean=10)
# Computing the new mean squared error
mse(sim=sim, obs=obs)
``` |

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