Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/predict.lmWinsor.R
Model predictions for object of class 'lmWinsor'.
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object |
Object of class inheriting from 'lmWinsor' |
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 of predictions are required |
scale |
Scale parameter for std.err. calculation |
df |
degrees of freedom for scale |
interval |
type of prediction (response or model term) |
level |
Tolerance/confidence level |
type |
Type of prediction (response or model term); see predict.lm |
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'. |
pred.var |
the variance(s) for future observations to be assumed for prediction intervals. See predict.lm '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 'newdata' |
... |
additional arguments for other methods |
1. Identify inputs and outputs via mdly <- mdlx <- formula(object); mdly[[3]] <- NULL; mdlx[[2]] <- NULL; xNames <- all.vars(mdlx); yNames <- all.vars(mdly). Give an error if as.character(mdly[[2]]) != yNames.
2. If 'newdata' are provided, clip all numeric xNames to (object[["lower"]], object[["upper"]]).
3. Call predict.lm
4. Clip the responses to the relevant components of (object[["lower"]], object[["upper"]]).
5. Done.
If class(object) == c('lmWinsor', 'lm'), 'predict.lmWinsor' produces a vector of predictions or a matrix of predictions with limits or a list, as produced by predict.lm. Otherwise, 'object' is a list of such objects and will therefore return a list of such predictions.
Spencer Graves
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 | # example from 'anscombe'
# trim = 0
lm.1 <- lmWinsor(y1~x1, data=anscombe)
newD <- data.frame(x1=seq(1, 22, .1))
predW <- predict(lm.1, newdata=newD)
plot(y1~x1, anscombe, xlim=c(1, 22),
main="Anscombe, example 1")
lines(newD[["x1"]], predW, col='blue')
abline(h=lm.1[['lower']]['y1'], col='red', lty='dashed')
abline(h=lm.1[['upper']]['y1'], col='red', lty='dashed')
abline(v=lm.1[['lower']]['x1'], col='green', lty='dashed')
abline(v=lm.1[['upper']]['x1'], col='green', lty='dashed')
# clipped at range(anscombe[, 'x1']) = c(4, 14)
# trim = 0.25
lm.1.25 <- lmWinsor(y1~x1, data=anscombe, trim=0.25)
newD <- data.frame(x1=seq(1, 22, .1))
predW.25 <- predict(lm.1.25, newdata=newD)
plot(y1~x1, anscombe, xlim=c(1, 22))
lines(newD[["x1"]], predW.25, col='blue', lwd=2)
abline(h=lm.1.25[['lower']]['y1'], col='red', lty='dotted')
abline(h=lm.1.25[['upper']]['y1'], col='red', lty='dotted')
abline(v=lm.1.25[['lower']]['x1'], col='green', lty='dotted')
abline(v=lm.1.25[['upper']]['x1'], col='green', lty='dotted')
# clipped at range(anscombe[, 'y1']) = c(4.26 10.84)
# list example
lm.1. <- lmWinsor(y1~x1, data=anscombe, trim=c(0, 0.25, .4, .5))
pred.1. <- predict(lm.1.)
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