msdr: msdr

In valmetrics: Metrics and Plots for Model Evaluation

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.

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)

Author(s)

Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg, E-mail: [e-mail]

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. doi: 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

obs<-c(1:10)
pred<-c(1, 1 ,3, 2, 4, 5, 6, 8, 7, 10)
msdr(o=obs, p=pred)

valmetrics documentation built on Jan. 16, 2021, 5:11 p.m.