DIAGNOSTICS {nsRFA}R Documentation

Diagnostics of models

Description

Diagnostics of model results, it compares estimated values y with observed values x.

Usage

 R2 (x, y, na.rm=FALSE)
 RMSE (x, y, na.rm=FALSE) 
 MAE (x, y, na.rm=FALSE)
 RMSEP (x, y, na.rm=FALSE)
 MAEP (x, y, na.rm=FALSE)

Arguments

x observed values
y estimated values
na.rm logical. Should missing values be removed?

Details

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|

Value

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.

Author(s)

Alberto Viglione, e-mail: alviglio@tiscali.it.

See Also

lm, summary.lm, predict.lm, REGRDIAGNOSTICS

Examples

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)

[Package nsRFA version 0.3-6 Index]