Mean absolute error between sim and obs, in the same units of them, with treatment of missing values.
1.1
mae(sim, obs,...)## Default S3 method:mae(sim, obs, na.rm=TRUE, fun=NULL,..., epsilon.type=c("none","Pushpalatha2012","otherFactor","otherValue"), epsilon.value=NA)## S3 method for class 'data.frame'mae(sim, obs, na.rm=TRUE, fun=NULL,..., epsilon.type=c("none","Pushpalatha2012","otherFactor","otherValue"), epsilon.value=NA)## S3 method for class 'matrix'mae(sim, obs, na.rm=TRUE, fun=NULL,..., epsilon.type=c("none","Pushpalatha2012","otherFactor","otherValue"), epsilon.value=NA)## S3 method for class 'zoo'mae(sim, obs, na.rm=TRUE, fun=NULL,..., epsilon.type=c("none","Pushpalatha2012","otherFactor","otherValue"), epsilon.value=NA)
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.
fun: function to be applied to sim and obs in order to obtain transformed values thereof before computing this goodness-of-fit index.
The first argument MUST BE a numeric vector with any name (e.g., x), and additional arguments are passed using ....
...: arguments passed to fun, in addition to the mandatory first numeric vector.
epsilon.type: argument used to define a numeric value to be added to both sim and obs before applying fun.
It is was designed to allow the use of logarithm and other similar functions that do not work with zero values.
Valid values of epsilon.type are:
"none" : sim and obs are used by fun without the addition of any numeric value. This is the default option.
"Pushpalatha2012" : one hundredth (1/100) of the mean observed values is added to both sim and obs before applying fun, as described in Pushpalatha et al. (2012).
"otherFactor" : the numeric value defined in the epsilon.value argument is used to multiply the the mean observed values, instead of the one hundredth (1/100) described in Pushpalatha et al. (2012). The resulting value is then added to both sim and obs, before applying fun.
"otherValue" : the numeric value defined in the epsilon.value argument is directly added to both sim and obs, before applying fun.
epsilon.value: -) when epsilon.type="otherValue" it represents the numeric value to be added to both sim and obs before applying fun.
-) when epsilon.type="otherFactor" it represents the numeric factor used to multiply the mean of the observed values, instead of the one hundredth (1/100) described in Pushpalatha et al. (2012). The resulting value is then added to both sim and obs before applying fun.
Details
mae=N1i=1∑N∣Si−Oi)∣
Returns
Mean absolute error between sim and obs.
If sim and obs are matrixes, the returned value is a vector, with the mean absolute error between each column of sim and obs.
Willmott, C.J.; Matsuura, K. (2005). Advantages of the mean absoluteerror (MAE) over the root mean square error (RMSE) in assessing averagemodel performance, Climate Research, 30, 79-82, doi:10.3354/cr030079.
Chai, T.; Draxler, R.R. (2014). Root mean square error (RMSE) or meanabsolute error (MAE)? - Arguments against avoiding RMSE in theliterature, Geoscientific Model Development, 7, 1247-1250.doi:10.5194/gmd-7-1247-2014.
Hodson, T.O. (2022). Root-mean-square error (RMSE) or mean absoluteerror (MAE): when to use them or not, Geoscientific Model Development,15, 5481-5487, doi:10.5194/gmd-15-5481-2022.
obs and sim have 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
################### Example 1: basic ideal caseobs <-1:10sim <-1:10mae(sim, obs)obs <-1:10sim <-2:11mae(sim, obs)################### Example 2: # Loading daily streamflows of the Ega River (Spain), from 1961 to 1970data(EgaEnEstellaQts)obs <- EgaEnEstellaQts
# Generating a simulated daily time series, initially equal to the observed seriessim <- obs
# Computing the 'mae' for the "best" (unattainable) casemae(sim=sim, obs=obs)################### Example 3: mae for simulated values equal to observations plus random noise # on the first half of the observed values. # This random noise has more relative importance for ow flows than # for medium and high flows.# Randomly changing the first 1826 elements of 'sim', by using a normal distribution # with mean 10 and standard deviation equal to 1 (default of 'rnorm').sim[1:1826]<- obs[1:1826]+ rnorm(1826, mean=10)ggof(sim, obs)mae(sim=sim, obs=obs)################### Example 4: mae for simulated values equal to observations plus random noise # on the first half of the observed values and applying (natural) # logarithm to 'sim' and 'obs' during computations.mae(sim=sim, obs=obs, fun=log)# Verifying the previous value:lsim <- log(sim)lobs <- log(obs)mae(sim=lsim, obs=lobs)################### Example 5: mae for simulated values equal to observations plus random noise # on the first half of the observed values and applying (natural) # logarithm to 'sim' and 'obs' and adding the Pushpalatha2012 constant# during computationsmae(sim=sim, obs=obs, fun=log, epsilon.type="Pushpalatha2012")# Verifying the previous value, with the epsilon value following Pushpalatha2012eps <- mean(obs, na.rm=TRUE)/100lsim <- log(sim+eps)lobs <- log(obs+eps)mae(sim=lsim, obs=lobs)################### Example 6: mae for simulated values equal to observations plus random noise # on the first half of the observed values and applying (natural) # logarithm to 'sim' and 'obs' and adding a user-defined constant# during computationseps <-0.01mae(sim=sim, obs=obs, fun=log, epsilon.type="otherValue", epsilon.value=eps)# Verifying the previous value:lsim <- log(sim+eps)lobs <- log(obs+eps)mae(sim=lsim, obs=lobs)################### Example 7: mae for simulated values equal to observations plus random noise # on the first half of the observed values and applying (natural) # logarithm to 'sim' and 'obs' and using a user-defined factor# to multiply the mean of the observed values to obtain the constant# to be added to 'sim' and 'obs' during computationsfact <-1/50mae(sim=sim, obs=obs, fun=log, epsilon.type="otherFactor", epsilon.value=fact)# Verifying the previous value:eps <- fact*mean(obs, na.rm=TRUE)lsim <- log(sim+eps)lobs <- log(obs+eps)mae(sim=lsim, obs=lobs)################### Example 8: mae for simulated values equal to observations plus random noise # on the first half of the observed values and applying a # user-defined function to 'sim' and 'obs' during computationsfun1 <-function(x){sqrt(x+1)}mae(sim=sim, obs=obs, fun=fun1)# Verifying the previous value, with the epsilon value following Pushpalatha2012sim1 <- sqrt(sim+1)obs1 <- sqrt(obs+1)mae(sim=sim1, obs=obs1)