mse: Mean Squared Error

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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)


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