get_bias | R Documentation |
Functions to calculate different performance metrics.
In the case of get_bias: Calculate the bias b, i.e. the average difference between predicted y and observed z values:
bias = mean(y - z)
get_bias(predicted, observed, ...)
root_mean_squared(predicted, observed, ...)
mean_absolute_error(predicted, observed, ...)
predicted |
Vector containing the predictions y. |
observed |
Vector containing the observations z. |
... |
relative Boolean. If true give the result as a ratio to the
average observation |
m A number representing the relative or absolute value for the metric.
root_mean_squared()
: Calculate the square root of the average squared difference between
prediction and observation:
RMSE = sqrt(sum(predicted - observed)^2) / length(predicted)
mean_absolute_error()
: Calculate the average of the absolute differences between
prediction and observation:
MAE = mean(abs(predicted - observed))
NA values are completely ignored.
willmott()
predicted = c(21.5, 22.2, 19.1)
observed = c(20, 20, 20)
get_bias(predicted, observed)
get_bias(predicted, observed, relative = FALSE)
root_mean_squared(predicted, observed)
root_mean_squared(predicted, observed, relative = FALSE)
mean_absolute_error(predicted, observed)
mean_absolute_error(predicted, observed, relative = FALSE)
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