Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/lmrob.R View source: R/lmrobPredict.R

Predicted values based on robust linear model object.

1 2 3 4 5 6 |

`object` |
object of class inheriting from |

`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 |

`na.action` |
function determining what should be done with missing
values in |

`pred.var` |
the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’. |

`weights` |
variance weights for prediction. This can be a numeric
vector or a one-sided model formula. In the latter case, it is
interpreted as an expression evaluated in |

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

Note that this `lmrob`

method for `predict`

is
closely modeled after the method for `lm()`

,
`predict.lm`

, maybe see there for caveats with missing
value treatment.
The prediction intervals are for a single observation at each case in
`newdata`

(or by default, the data used for the fit) with error
variance(s) `pred.var`

. This can be a multiple of `res.var`

,
the estimated value of *σ^2*: the default is to assume that
future observations have the same error variance as those
used for fitting. If `weights`

is supplied, the inverse of this
is used as a scale factor. For a weighted fit, if the prediction
is for the original data frame, `weights`

defaults to the weights
used for the model fit, with a warning since it might not be the
intended result. If the fit was weighted and `newdata`

is given, the
default is to assume constant prediction variance, with a warning.

`predict.lmrob`

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 |

Andreas Ruckstuhl

`lmrob`

and the (non-robust) traditional
`predict.lm`

method.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
## Predictions --- artificial example -- closely following example(predict.lm)
set.seed(5)
n <- length(x <- sort(c(round(rnorm(25), 1), 20)))
y <- x + rnorm(n)
iO <- c(sample(n-1, 3), n)
y[iO] <- y[iO] + 10*rcauchy(iO)
p.ex <- function(...) {
plot(y ~ x, ...); abline(0,1, col="sky blue")
points(y ~ x, subset=iO, col="red", pch=2)
abline(lm (y ~ x), col = "gray40")
abline(lmrob(y ~ x), col = "forest green")
legend("topleft", c("true", "Least Squares", "robust"),
col = c("sky blue", "gray40", "forest green"), lwd=1.5, bty="n")
}
p.ex()
fm <- lmrob(y ~ x)
predict(fm)
new <- data.frame(x = seq(-3, 10, 0.25))
str(predict(fm, new, se.fit = TRUE))
pred.w.plim <- predict(fm, new, interval = "prediction")
pred.w.clim <- predict(fm, new, interval = "confidence")
pmat <- cbind(pred.w.clim, pred.w.plim[,-1])
matlines(new$x, pmat, lty = c(1,2,2,3,3))# add to first plot
## show zoom-in region :
rect(xleft = -3, ybottom = -20, xright = 10, ytop = 40,
lty = 3, border="orange4")
## now zoom in :
p.ex(xlim = c(-3,10), ylim = c(-20, 40))
matlines(new$x, pmat, lty = c(1,2,2,3,3))
box(lty = 3, col="orange4", lwd=3)
legend("bottom", c("fit", "lwr CI", "upr CI", "lwr Pred.I", "upr Pred.I"),
col = 1:5, lty=c(1,2,2,3,3), bty="n")
## Prediction intervals, special cases
## The first three of these throw warnings
w <- 1 + x^2
fit <- lmrob(y ~ x)
wfit <- lmrob(y ~ x, weights = w)
predict(fit, interval = "prediction")
predict(wfit, interval = "prediction")
predict(wfit, new, interval = "prediction")
predict(wfit, new, interval = "prediction", weights = (new$x)^2) -> p.w2
p.w2
stopifnot(identical(p.w2, ## the same as using formula:
predict(wfit, new, interval = "prediction", weights = ~x^2)))
``` |

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