accurate
From aTSA v3.1.2
by Debin Qiu
Accurate Computation
Computes the accurate criterion of smoothed (fitted) values.
Usage
accurate(x, x.hat, k, output = TRUE)
Arguments
 x
 a numeric vector of original values.
 x.hat
 a numeric vector of smoothed (fitted) values.
 k
 the number of parameters in obtaining the smoothed (fitted) values.
 output
 a logical value indicating to print the results in R console. The default is
TRUE
.
Details
See http://www.dms.umontreal.ca/~duchesne/chap12.pdf in page 616  617 for the details of calculations for each criterion.
Value

A vector containing the following components:
 SST
 the total sum of squares.
 SSE
 the sum of the squared residuals.
 MSE
 the mean squared error.
 RMSE
 the root mean square error.
 MAPE
 the mean absolute percent error.
 MPE
 the mean percent error.
 MAE
 the mean absolute error.
 ME
 the mean error.
 R.squared
 R^2 = 1  SSE/SST.
 R.adj.squared
 the adjusted R^2.
 RW.R.squared
 the random walk R^2.
 AIC
 the Akaike's information criterion.
 SBC
 the Schwarz's Bayesian criterion.
 APC
 the Amemiya's prediction criterion
Note
If the model fits the series badly, the model error sum of squares SSE
may be larger than SST
and the R.squared
or RW.R.squared
statistics
will be negative. The RW.R.squared
uses the random walk model for the purpose of
comparison.
Examples
X < matrix(rnorm(200),100,2)
y < 0.1*X[,1] + 2*X[,2] + rnorm(100)
y.hat < fitted(lm(y ~ X))
accurate(y,y.hat,2)
Community examples
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