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 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.

### Value

`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.

### Note

For information on the package and the Author, and for all the references, see `nsRFA`.

### 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 0.6-9 Index]