msdr

0th

Percentile

msdr

Calculates the Mean squared deviation ratio (msdr) from observed and predicted values.

Usage
msdr(o, p)
Arguments
o

A numeric vector. Observed values.

p

A numeric vector. Predicted values.

Details

Interpretation: closer to 1 is better. Sometimes called standardised squared predictor error (SSPE) or scaled root mean squared error (SRMSE).

Value

Mean squared deviation ratio (msdr)

References

Piikki K., Wetterlind J., Soderstrom M., Stenberg B. (2021). Perspectives on validation in digital soil mapping of continuous attributes. A review. Soil Use and Management. 10.1111/sum.12694

Voltz, M., & Webster, R. (1990). A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Journal of soil Science, 41(3), 473-490. (there called: standardized square deviation).

Aliases
  • msdr
Examples
# NOT RUN {
obs<-c(1:10)
pred<-c(1, 1 ,3, 2, 4, 5, 6, 8, 7, 10)
msdr(o=obs, p=pred)

# }
Documentation reproduced from package valmetrics, version 1.0.0, License: MIT + file LICENSE

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