Description Usage Arguments Details Value Note Author(s) References See Also Examples
Root Mean Square Error (RMSE) between sim
and obs
, in the same units of sim
and obs
, with treatment of missing values.
RMSE gives the standard deviation of the model prediction error. A smaller value indicates better model performance.
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. |
rmse = sqrt( mean( (sim - obs)^2, na.rm = TRUE) )
Root mean square error (rmse) between sim
and obs
.
If sim
and obs
are matrixes, the returned value is a vector, with the RMSE 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>
http://en.wikipedia.org/wiki/Root_mean_square_deviation
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 26 | obs <- 1:10
sim <- 1:10
rmse(sim, obs)
obs <- 1:10
sim <- 2:11
rmse(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 root mean squared error for the "best" (unattainable) case
rmse(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 root mean squared error
rmse(sim=sim, obs=obs)
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