Mean Squared Error

Description

Mean squared error between sim and obs, in the squared units of sim and obs, with treatment of missing values.

Usage

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mse(sim, obs, ...)

## Default S3 method:
mse(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'data.frame'
mse(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'matrix'
mse(sim, obs, na.rm=TRUE, ...)

## S3 method for class 'zoo'
mse(sim, obs, na.rm=TRUE, ...)

Arguments

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.
When an 'NA' value is found at the i-th position in obs OR sim, the i-th value of obs AND sim are removed before the computation.

...

further arguments passed to or from other methods.

Details

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

Value

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.

Note

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

Author(s)

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

References

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

See Also

mae, me, gof

Examples

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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)