predict.lm {stats} | R Documentation |

Predicted values based on linear model object

## S3 method for class 'lm': predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, interval = c("none", "confidence", "prediction"), level = 0.95, type = c("response", "terms"), terms = NULL, na.action = na.pass, ...)

`object` |
Object of class inheriting from `"lm"` |

`newdata` |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |

`se.fit` |
A switch indicating if standard errors are required. |

`scale` |
Scale parameter for std.err. calculation |

`df` |
Degrees of freedom for scale |

`interval` |
Type of interval calculation |

`level` |
Tolerance/confidence level |

`type` |
Type of prediction (response or model term) |

`terms` |
If `type="terms"` , which terms (default is all terms) |

`na.action` |
function determining what should be done with missing
values in `newdata` . The default is to predict `NA` . |

`...` |
further arguments passed to or from other methods. |

`predict.lm`

produces predicted values, obtained by evaluating
the regression function in the frame `newdata`

(which defaults to
`model.frame(object)`

. If the logical `se.fit`

is
`TRUE`

, standard errors of the predictions are calculated. If
the numeric argument `scale`

is set (with optional `df`

), it
is used as the residual standard deviation in the computation of the
standard errors, otherwise this is extracted from the model fit.
Setting `intervals`

specifies computation of confidence or
prediction (tolerance) intervals at the specified `level`

, sometimes
referred to as narrow vs. wide intervals.

If the fit is rank-deficient, some of the columns of the design matrix
will have been dropped. Prediction from such a fit only makes sense
if `newdata`

is contained in the same subspace as the original
data. That cannot be checked accurately, so a warning is issued.

If `newdata`

is omitted the predictions are based on the data
used for the fit. In that case how cases with missing values in the
original fit is determined by the `na.action`

argument of that
fit. If `na.action = na.omit`

omitted cases will not appear in
the residuals, whereas if `na.action = na.exclude`

they will
appear (in predictions, standard errors or interval limits),
with residual value `NA`

. See also `napredict`

.

`predict.lm`

produces a vector of predictions or a matrix of
predictions and bounds with column names `fit`

, `lwr`

, and
`upr`

if `interval`

is set. If `se.fit`

is
`TRUE`

, a list with the following components is returned:

`fit` |
vector or matrix as above |

`se.fit` |
standard error of predicted means |

`residual.scale` |
residual standard deviations |

`df` |
degrees of freedom for residual |

Variables are first looked for in `newdata`

and then searched for
in the usual way (which will include the environment of the formula
used in the fit). As from **R** 2.0.0 a warning will be given if the
variables found are not of the same length as those in `newdata`

if it was supplied.

Offsets specified by `offset`

in the fit by `lm`

will not be included in predictions, whereas those specified by an
offset term in the formula will be.

The model fitting function `lm`

, `predict`

,
`SafePrediction`

## Predictions x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <- data.frame(x = seq(-3, 3, 0.5)) predict(lm(y ~ x), new, se.fit = TRUE) pred.w.plim <- predict(lm(y ~ x), new, interval="prediction") pred.w.clim <- predict(lm(y ~ x), new, interval="confidence") matplot(new$x,cbind(pred.w.clim, pred.w.plim[,-1]), lty=c(1,2,2,3,3), type="l", ylab="predicted y")

[Package *stats* version 2.2.1 Index]