| msdr {valmetrics} | R Documentation |
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. |
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: kristin.piikki@slu.se
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)