| recresid {strucchange} | R Documentation |
A generic function for computing the recursive residuals (standardized one step prediction errors) of a linear regression model.
## Default S3 method: recresid(x, y, ...) ## S3 method for class 'formula': recresid(formula, data = list(), ...) ## S3 method for class 'lm': recresid(x, data = list(), ...)
x, y, formula |
specification of the linear regression model:
either by a regressor matrix x and a response variable y,
or by a formula or by a fitted object x of class "lm". |
data |
an optional data frame containing the variables in the model. By
default the variables are taken from the environment which recresid is
called from. Specifying data might also be necessary when applying
recresid to a fitted model of class "lm" if this does not
contain the regressor matrix and the response. |
... |
currently not used. |
Under the usual assumptions for the linear regression model the recdursive residuals are (asymptotically) normal and i.i.d. (see Brown, Durbin, Evans (1975) for details).
A vector containing the recursive residuals.
Brown R.L., Durbin J., Evans J.M. (1975), Techniques for testing constancy of regression relationships over time, Journal of the Royal Statistal Society, B, 37, 149-163.
x <- rnorm(100) x[51:100] <- x[51:100] + 2 rr <- recresid(x ~ 1) plot(cumsum(rr), type = "l") plot(efp(x ~ 1, type = "Rec-CUSUM"))