valesta {datana} | R Documentation |
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 - \widehat{y}_i
where y_i
and \widehat{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)
Arguments
y.obs |
observed values of the variable of interest |
y.pred |
predicted values of the variable of interest |
Details
The function computes the three aforementioned statistics expressed in both (a) the units of the response variable and (b) the percentage. Notice that to represent each statistic in percentual terms, we divided them by the mean observed value of the response variable.
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.
Author(s)
Christian Salas-Eljatib.
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. 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. doi:10.4067/S0717-92002002000200009.
Examples
#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))