# msdr

##### 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).

##### 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*