forecast.mlm {forecast} | R Documentation |
forecast.mlm
is used to predict multiple linear models, especially those involving trend and seasonality components.
## S3 method for class 'mlm' forecast(object, newdata, h = 10, level = c(80, 95), fan = FALSE, lambda = object$lambda, biasadj = NULL, ts = TRUE, ...)
object |
Object of class "mlm", usually the result of a call to |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, it is assumed that the only variables are trend and season, and |
level |
Confidence level for prediction intervals. |
fan |
If |
h |
Number of periods for forecasting. Ignored if |
lambda |
Box-Cox transformation parameter. Ignored if |
biasadj |
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. |
ts |
If |
... |
Other arguments passed to |
forecast.mlm
is largely a wrapper for forecast.lm()
except that it allows forecasts to be generated on multiple series. Also, the output is reformatted into a mforecast
object.
An object of class "mforecast
".
The function summary
is used to obtain and print a summary of the
results, while the function plot
produces a plot of the forecasts and prediction intervals.
The generic accessor functions fitted.values
and residuals
extract useful features of the value returned by forecast.lm
.
An object of class "mforecast"
is a list containing at least the following elements:
model |
A list containing information about the fitted model |
method |
The name of the forecasting method as a character string |
mean |
Point forecasts as a multivariate time series |
lower |
Lower limits for prediction intervals of each series |
upper |
Upper limits for prediction intervals of each series |
level |
The confidence values associated with the prediction intervals |
x |
The historical data for the response variable. |
residuals |
Residuals from the fitted model. That is x minus fitted values. |
fitted |
Fitted values |
Mitchell O'Hara-Wild
tslm
, forecast.lm
, lm
.
lungDeaths <- cbind(mdeaths, fdeaths) fit <- tslm(lungDeaths ~ trend + season) fcast <- forecast(fit, h=10) carPower <- as.matrix(mtcars[,c("qsec","hp")]) carmpg <- mtcars[,"mpg"] fit <- lm(carPower ~ carmpg) fcast <- forecast(fit, newdata=data.frame(carmpg=30))