predict
Model Predictions
predict
is a generic function for predictions from the results of
various model fitting functions. The function invokes particular
methods which depend on the class
of
the first argument.
 Keywords
 methods
Usage
predict (object, ...)
Arguments
 object
 a model object for which prediction is desired.
 ...
 additional arguments affecting the predictions produced.
Details
Most prediction methods which are similar to those for linear models
have an argument newdata
specifying the first place to look for
explanatory variables to be used for prediction. Some considerable
attempts are made to match up the columns in newdata
to those
used for fitting, for example that they are of comparable types and
that any factors have the same level set in the same order (or can be
transformed to be so).
Time series prediction methods in package stats have an argument
n.ahead
specifying how many time steps ahead to predict.
Many methods have a logical argument se.fit
saying if standard
errors are to returned.
Value

The form of the value returned by
predict
depends on the
class of its argument. See the documentation of the
particular methods for details of what is produced by that method.
References
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
See Also
predict.glm
,
predict.lm
,
predict.loess
,
predict.nls
,
predict.poly
,
predict.princomp
,
predict.smooth.spline
.
SafePrediction for prediction from (univariable) polynomial and spline fits.
For timeseries prediction,
predict.ar
,
predict.Arima
,
predict.arima0
,
predict.HoltWinters
,
predict.StructTS
.
Examples
library(stats)
require(utils)
## All the "predict" methods found
## NB most of the methods in the standard packages are hidden.
for(fn in methods("predict"))
try({
f < eval(substitute(getAnywhere(fn)$objs[[1]], list(fn = fn)))
cat(fn, ":\n\t", deparse(args(f)), "\n")
}, silent = TRUE)
Community examples
``` #creates the xaxis (size) and yaxis (price) size < c(2104, 1416, 1534, 852) price < c(460, 232, 315, 178) data1 < data.frame(size, price) plot(size, price, col = 2, pch = 16, xlab = "Size in feet²", ylab = "Price ($) in 1000", main = "Size versus Price") #makes a linear regression of the data lm_prices < lm(price ~ size, data = data1) #uses the lm_prices' coef to plot the best line abline(coef(lm_prices), lwd = 1) lines(size[order(size)], price[order(price)], col = "blue") #predic the price for 1200 feet² size_1200 < data.frame(size = 1200) predict(lm_prices, size_1200) ```