DIAGNOSTICS {nsRFA} | R Documentation |
Diagnostics of model results, it compares estimated values y
with observed values x
.
R2 (x, y) RMSE (x, y) MAE (x, y) RMSEP (x, y) MAEP (x, y)
x |
observed values |
y |
estimated values |
If x_i are the observed values, y_i the estimated values, with i=1,...,n, and bar{x} the sample mean of x_i, then:
R^2 = 1 - frac{sum_1^n (x_i-y_i)^2}{sum_1^n x_i^2 - n bar{x}^2}
RMSE = sqrt{frac{1}{n} sum_1^n (x-y)^2}
MAE = frac{1}{n} sum_1^n |x-y|
RMSEP = sqrt{frac{1}{n} sum_1^n ((x-y)/x)^2}
MAEP = frac{1}{n} sum_1^n |(x-y)/x|
R2
returns the coefficient of determination R^2 of a model.
RMSE
returns the root mean squared error of a model.
MAE
returns the mean absolute error of a model.
RMSE
returns the percentual root mean squared error of a model.
MAE
returns the percentual mean absolute error of a model.
Alberto Viglione, e-mail: alviglio@tiscali.it.
lm
, summary.lm
, predict.lm
, REGRDIAGNOSTICS
data(hydroSIMN) datregr <- parameters regr0 <- lm(Dm ~ .,datregr); summary(regr0) regr1 <- lm(Dm ~ Am + Hm + Ybar,datregr); summary(regr1) obs <- parameters[,"Dm"] est0 <- regr0$fitted.values est1 <- regr1$fitted.values R2(obs, est0) R2(obs, est1) RMSE(obs, est0) RMSE(obs, est1) MAE(obs, est0) MAE(obs, est1) RMSEP(obs, est0) RMSEP(obs, est1) MAEP(obs, est0) MAEP(obs, est1)