dyn {dyn}R Documentation

dynamic regression class

Description

dyn is constructs objects of class "dyn",

Usage

dyn(x)

Arguments

x an object, typically a "formula" object or an object produced by "lm", "glm" or other regression function.

Details

"dyn" enables regression functions that were not written to handle time series to so handle them. The time series need not have the same indexes (the are automatically intersected) and may have missing values including internal missing values.

"dyn" creates a dynamic regression object by returning x with the "dyn" class name prepended to the class of the argument.

If the argument to "dyn" is a formula its variables may be time series objects of one of the following classes: "ts", "irts", code{"its"}, "zoo" or "zooreg".

"dyn" methods are available for "model.frame", "fitted", "residuals", "predict", "update", "anova" and "$". These methods preprocess their arguments, call the real method which does the actual work and then post process the returned object. In the case of "fitted", "residuals" and "predict" they ensure that the result is a time series. In the case of anova the objects are intersected so that they all have the time indexes to ensure that a meaningful input is provided to "anova".

The $ method is always used with a left argument of "dyn" like this "dyn$lm(x, ...)". This expression is equivalent to "dyn(lm(dyn(x), ...))" but is more convenient to write.

"dyn" currently works with any regression function that makes use of "model.frame" and is written in the style of "lm". This includes "lm", "glm", "loess", "rlm" (from "MASS"), "lqs" (from pkg{"MASS"}), "randomForest" (from "randomForest"), "rq" (from "quantreg") and likely others.

Value

"dyn" returns its argument with the class name "dyn" prepended to its class vector. The "fitted", "residuals" and "predict" "dyn" methods return time series of the appropriate class. "model.frame" creates a model frame with an attribute of "series" that contains a data frame of the time series and factor variables as columns.

Note

"dyn" relies on the underlying time series classes and regression routines for all substantive functionality. In particular note these limitations: "irts" has no "lag" or "diff" methods. The lag function of "its" its called "lagIts". "ts" and "zooreg" series can be lagged outside of the data range (both forward and backward) but other time series classes cannot represent such data and therefore will drop them. If the regression function in questions does not have an associated "fitted", "residuals", etc. method then such method will not be available with "dyn" either.

Internally the system uses "zoo". Additional time series classes not already defined to work with "dyn" can be added by simply defining "as" methods between the new class and "zoo" and then creating new methods (for "model.frame", "predict", "fitted", etc.) In most cases these method names can be set equal to the corresponding "zoo" method name (e.g. "model.frame.newclass <- model.frame.zoo" so that no new function bodies need be written).

The main requirements for new regression routines to work with "dyn" are that they use "model.frame", that their "fitted", "residuals" and "predict" methods return named vectors whose names are the corresponding indexes in the original data and that they follow the same style of processing as "lm". There is no "dyn" code specific to any particular regression routine.

See Also

See Also model.frame, predict, fitted, residuals, anova, update, lm, glm, loess

Examples

y <- ts(1:12, start = c(2000,2), freq = 4)^3
x <- ts(1:9, start = c(2000,3), freq = 4)^2

# can be used with numerous different regression functions
y.lm <- dyn$lm( window(y, start = c(2000,4)) ~ diff(x) )
y.lm <- dyn$lm( y ~ diff(x) )
y.glm <- dyn$glm( y ~ diff(x) )
y.loess <- dyn$loess( y ~ diff(x) )

y.lm <- dyn(lm(dyn(y ~ diff(x))))  # same
y.lm
summary(y.lm)
residuals(y.lm)
fitted(y.lm)
y2.lm <- update(y.lm, . ~ . + lag(x))
y2.lm
anova(y.lm, y2.lm)

# examples of using data
dyn$lm(y ~ diff(x), list(y = y, x = x))
dyn$lm(y ~ diffx, list(y = y, diffx = diff(x)))

# invoke model.frame on formula as a dyn object
dyn$model.frame( y ~ diff(x) )


[Package dyn version 0.2-0 Index]