DIAGNOSTICS {nsRFA} | R Documentation |
Diagnostics of model results, it compares estimated values y
with observed values x
.
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)
x |
observed values |
y |
estimated values |
na.rm |
logical. Should missing values be removed? |
If xi are the observed values, yi the estimated values, with i=1,...,n, and <x> the sample mean of xi, then:
R2 = 1 - (sum(xi-yi)^2)/(sum(xi^2-n<x>^2)
RMSE = sqrt(1/n sum(xi-yi)^2)
MAE = 1/n sum(|xi-yi|)
RMSEP = sqrt(1/n sum((xi-yi)/xi)^2)
MAEP = 1/n sum(|(xi-yi)/xi|
See http://en.wikipedia.org/wiki/Coefficient_of_determination, http://en.wikipedia.org/wiki/Mean_squared_error and http://en.wikipedia.org/wiki/Mean_absolute_error for other details.
R2
returns the coefficient of determination R2 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.
For information on the package and the Author, and for all the references, see nsRFA
.
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)