accurate: Accurate Computation

In aTSA: Alternative Time Series Analysis

Accurate Computation

Description

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.

Author(s)

Debin Qiu

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)

aTSA documentation built on May 29, 2024, 8:50 a.m.