Mean Error
Mean error between sim and obs, in the same units of them, with treatment of missing values.
me(sim, obs, ...) ## Default S3 method: me(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'data.frame' me(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'matrix' me(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'zoo' me(sim, obs, na.rm=TRUE, ...)
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. |
me = mean( sim - obs), na.rm = TRUE)
Mean error between sim and obs.
If sim and obs are matrixes, the returned value is a vector, with the mean 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>
Hill, T., Lewicki, P., & Lewicki, P. (2006). Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. StatSoft, Inc.
obs <- 1:10 sim <- 1:10 me(sim, obs) obs <- 1:10 sim <- 2:11 me(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 error for the "best" case me(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 error me(sim=sim, obs=obs)
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