| dynlm {dynlm} | R Documentation |
Interface to lm.wfit for fitting dynamic linear models
and time-series regression relationships.
dynlm(formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, contrasts = NULL, offset, start = NULL, end = NULL, ...)
formula |
a "formula" describing the linear model to be fit.
For details see below and lm. |
data |
an optional "data.frame" or time-series object (e.g.,
"ts" or "zoo"), containing the variables
in the model. If not found in data, the variables are taken
from environment(formula), typically the environment from which
lm is called. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used
in the fitting process. If specified, weighted least squares is used
with weights weights (that is, minimizing sum(w*e^2));
otherwise ordinary least squares is used. |
na.action |
a function which indicates what should happen
when the data contain NAs. The default is set by
the na.action setting of options, and is
na.fail if that is unset. The “factory-fresh”
default is na.omit. Another possible value is
NULL, no action. Note, that for time-series regression
special methods like na.contiguous, na.locf
and na.approx are available. |
method |
the method to be used; for fitting, currently only
method = "qr" is supported; method = "model.frame" returns
the model frame (the same as with model = TRUE, see below). |
model, x, y, qr |
logicals. If TRUE the corresponding
components of the fit (the model frame, the model matrix, the
response, the QR decomposition) are returned. |
singular.ok |
logical. If FALSE (the default in S but
not in R) a singular fit is an error. |
contrasts |
an optional list. See the contrasts.arg
of model.matrix.default. |
offset |
this can be used to specify an a priori
known component to be included in the linear predictor
during fitting. An offset term can be included in the
formula instead or as well, and if both are specified their sum is used. |
start |
start of the time period which should be used for fitting the model. |
end |
end of the time period which should be used for fitting the model. |
... |
additional arguments to be passed to the low level regression fitting functions. |
The interface and internals of dynlm are very similar to lm,
but currently dynlm offers two advantages over the direct use of
lm: 1. extended formula processing, 2. preservation of time-series
attributes.
For specifying the formula of the model to be fitted, there are
additional functions available which facilitate the specification of
dynamic models. An example would be d(y) ~ L(y, 2), where
d(x, k) is diff(x, lag = k) and L(x, k) is
lag(x, lag = -k), note the difference in sign. The default
for k is in both cases 1.
The specification of dynamic relationships only makes sense if there is an
underlying ordering of the observations. Currently, lm offers only limited
support for such data, hence a major aim of dynlm is to preserve
time-series properties of the data. Explicit support is currently available
for "ts" and "zoo" series. Internally, the data is kept as a "zoo"
series and coerced back to "ts" if the original dependent variable was of
that class (and not internal NAs were created by the na.action).
## multiplicative SARIMA(1,0,0)(1,0,0)_12 model fitted
## to UK seatbelt data
uk <- log10(UKDriverDeaths)
dfm <- dynlm(uk ~ L(uk, 1) + L(uk, 12))
dfm
## explicitely set start and end
dfm <- dynlm(uk ~ L(uk, 1) + L(uk, 12), start = c(1975, 1), end = c(1982, 12))
dfm
## remove lag 12
dfm0 <- update(dfm, . ~ . - L(uk, 12))
anova(dfm0, dfm)
## add season term
dfm1 <- dynlm(uk ~ 1, start = c(1975, 1), end = c(1982, 12))
dfm2 <- dynlm(uk ~ season(uk), start = c(1975, 1), end = c(1982, 12))
anova(dfm1, dfm2)
plot(uk)
lines(fitted(dfm0), col = 2)
lines(fitted(dfm2), col = 4)
## Examples 7.11/7.12 from Greene (1993)
if(require(lmtest)) {
data(USDistLag)
dfm1 <- dynlm(consumption ~ gnp + L(consumption), data = USDistLag)
dfm2 <- dynlm(consumption ~ gnp + L(gnp), data = USDistLag)
plot(USDistLag[, "consumption"])
lines(fitted(dfm1), col = 2)
lines(fitted(dfm2), col = 4)
encomptest(dfm1, dfm2)
}