# rmse

From valmetrics v1.0.0
by Kristin Piikki

##### rmse

Calculates the Root mean square error (RMSE) from observed and predicted values.

##### Usage

`rmse(o, p)`

##### Arguments

- o
A numeric vector. Observed values.

- p
A numeric vector. Predicted values.

##### Details

Interpretation: smaller is better. RMSE is sometimes abbreviated RMS, RMSD or RMSEP. A smaller value means a smaller error. RMSE is similar to mean absolute error (MAE), median absolute deviation (MAD) and root median squared error (RmdSE) but is more sensitive to large errors.

##### Value

Root mean square error (RMSE)

##### 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

##### Examples

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

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

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