valesta: Function to compute prediction statistics based on observed values

Description

Computes three prediction statistics as a way to compare observed versus predicted values of a response variable of interest. The statistics are: the aggregated difference ($AD$), the root mean square differences ($RMSD$), and the aggregated of the absolute value differences ($AAD$). All of them area based on $$r_i = y_i - \hat{y}_i$$ where $y_i$ and $\hat{y}_i$ are the observed and the predicted value of the response variable $y$ for the i-th observation, respectively. Both the observed and predicted values must be expressed in the same units.

Usage

valesta(y.obs = y.obs, y.pred = y.pred)

Value

The main output following six prediction statistics as a vector: (RMSD, RMSD.p, AD, AD.p, AAD, AAD.p); where RMSD.p stands for RMSD expressed as a percentage, and the same applies to AD.p and AAD.p.

Arguments

y.obs: observed values of the variable of interest
y.pred: predicted values of the variable of interest

Author

Christian Salas-Eljatib.

Details

The function computes the three aforementioned statistics expressed in (i) as the units of the response variable and (i) as a percentage. Notice that to represent each statistic in percentual terms, we divided them by the mean observed value of the response variable.

References

- Salas C, Ene L, Gregoire TG, Nasset E, Gobakken T. 2010. Modelling tree diameter from airborne laser scanning derived variables: a comparison of spatial statistical models. Remote Sensing of Environment 114(6):1277-1285. tools:::Rd_expr_doi("10.1016/j.rse.2010.01.020")

- Salas C. 2002. Ajuste y validación de ecuaciones de volumen para un relicto del bosque de roble-laurel-lingue. Bosque 23(2):81–92. tools:::Rd_expr_doi("10.4067/S0717-92002002000200009").

Examples

Run this code


#Creates a fake dataframe
set.seed(1234)
df <- as.data.frame(cbind(Y=rnorm(30, 30,9), X=rnorm(30, 450,133)))
#fitting a candidate model
mod1 <- lm(Y~X, data=df)
#Using the valesta function
valesta(y.obs=df$Y,y.pred=fitted(mod1))

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