valmetrics (version 1.0.0)

msdr: msdr

Description

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.

Value

Mean squared deviation ratio (msdr)

Details

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

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).

Examples

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

# }