R: Predict method for Linear Model Fits

predict.lm {base} | R Documentation

## Predict method for Linear Model Fits

### Description

Predicted values based on linear model object

### Usage

    predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf,
            interval = c("none", "confidence", "prediction"),
            level = 0.95, type = c("response", "terms"),
            terms = NULL, ...)

### Arguments

`object` | Object of class inheriting from `"lm"`
`newdata` | Data frame in which to predict
`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)
`...` | further arguments passed to or from other methods.

### Details

`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`.

### Value

`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 predictions
`residual.scale` | residual standard deviations
`df` | degrees of freedom for residual

### Note

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.

### See Also

The model fitting function `lm`, `predict`.

### Examples

    ## 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")

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