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 nonmissing 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 rainfallrunoff models: sensitivity to calibration data. Journal of Hydrology. v181 i14. 2348
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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