Difference between revisions of "Performance measures"
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Here is a selection of measures you can use for assessing the performance of your model. | Here is a selection of measures you can use for assessing the performance of your model. | ||
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===Nash-Sutcliffe efficiency=== | ===Nash-Sutcliffe efficiency=== | ||
| − | + | The [http://en.wikipedia.org/wiki/Nash-Sutcliffe_efficiency_coefficient Nash-Sutcliffe efficiency] is a widely used performance measure based on the error variance. It is implemented as ''NSeff()'' in the topmodel package. It takes two vectors, containing the simulated (Qsim) and observed (Qobs) discharge: | |
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| + | Eff <- NSeff(Qobs,Qsim) | ||
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| + | The underlying code is very simple: | ||
NSeff <- function (Qobs, Qsim){ | NSeff <- function (Qobs, Qsim){ | ||
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return(NS) | return(NS) | ||
} | } | ||
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| + | ===Coefficient of determination=== | ||
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| + | The [http://en.wikipedia.org/wiki/R-squared coefficient of determination] is the square of the Pearson's product-moment correlation coefficient. Note that the COD only evaluates linear relations between variables! | ||
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| + | COD <- cor(Qobs,Qsim, use="complete.obs")^2 | ||
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| + | ===Root mean squared error=== | ||
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| + | The [http://en.wikipedia.org/wiki/Root_mean_square_deviation root mean squared error] is the square root of the variance: | ||
| + | |||
| + | RMSE <- cov(Qobs,Qsim, use="complete.obs")^0.5 | ||
Revision as of 17:47, 3 August 2008
Introduction
Here is a selection of measures you can use for assessing the performance of your model.
Nash-Sutcliffe efficiency
The Nash-Sutcliffe efficiency is a widely used performance measure based on the error variance. It is implemented as NSeff() in the topmodel package. It takes two vectors, containing the simulated (Qsim) and observed (Qobs) discharge:
Eff <- NSeff(Qobs,Qsim)
The underlying code is very simple:
NSeff <- function (Qobs, Qsim){
Qsim <- Qsim[!is.na(Qobs)]
Qobs <- Qobs[!is.na(Qobs)]
Qobs <- Qobs[!is.na(Qsim)]
Qsim <- Qsim[!is.na(Qsim)]
NS <- 1 - (sum((Qobs - Qsim)^2)/sum((Qobs - mean(Qobs))^2))
return(NS)
}
Coefficient of determination
The coefficient of determination is the square of the Pearson's product-moment correlation coefficient. Note that the COD only evaluates linear relations between variables!
COD <- cor(Qobs,Qsim, use="complete.obs")^2
Root mean squared error
The root mean squared error is the square root of the variance:
RMSE <- cov(Qobs,Qsim, use="complete.obs")^0.5