DIAGNOSTICS | 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 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_i-y_i)^2}
MAE = \frac{1}{n} \sum_1^n |x_i-y_i|
RMSEP = \sqrt{\frac{1}{n} \sum_1^n ((x_i-y_i)/x_i)^2}
MAEP = \frac{1}{n} \sum_1^n |(x_i-y_i)/x_i|
See https://en.wikipedia.org/wiki/Coefficient_of_determination, https://en.wikipedia.org/wiki/Mean_squared_error and https://en.wikipedia.org/wiki/Mean_absolute_error for other details.
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.
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)
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