ssq | R Documentation |
Sum of the Squared Residuals between sim
and obs
, with treatment of missing values.
Its units are the squared measurement units of sim
and obs
.
ssq(sim, obs, ...)
## Default S3 method:
ssq(sim, obs, na.rm = TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'data.frame'
ssq(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'matrix'
ssq(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'zoo'
ssq(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
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. |
fun |
function to be applied to The first argument MUST BE a numeric vector with any name (e.g., |
... |
arguments passed to |
epsilon.type |
argument used to define a numeric value to be added to both It is was designed to allow the use of logarithm and other similar functions that do not work with zero values. Valid values of 1) "none": 2) "Pushpalatha2012": one hundredth (1/100) of the mean observed values is added to both 3) "otherFactor": the numeric value defined in the 4) "otherValue": the numeric value defined in the |
epsilon.value |
-) when |
ssr = \sum_{i=1}^N { (S_i - O_i )^2 }
Sum of the squared residuals between sim
and obs
.
If sim
and obs
are matrixes, the returned value is a vector, with the SSR 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>
Willmott, C.J.; Matsuura, K.; Robeson, S.M. (2009). Ambiguities inherent in sums-of-squares-based error statistics, Atmospheric Environment, 43, 749-752, doi:10.1016/j.atmosenv.2008.10.005.
pbias
, pbiasfdc
, mae
, mse
, rmse
, ubRMSE
, nrmse
, gof
, ggof
obs <- 1:10
sim <- 1:10
ssq(sim, obs)
obs <- 1:10
sim <- 2:11
ssq(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 'rNSeff' for the "best" (unattainable) case
ssq(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 'rNSeff'
ssq(sim=sim, obs=obs)
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