Make predictions from a sequence of regression models, such as submodels
along a robust or groupwise least angle regression sequence, or sparse least
trimmed squares regression models for a grid of values for the penalty
parameter. For autoregressive time series models with exogenous inputs,
*h*-step ahead forecasts are performed.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## S3 method for class 'seqModel'
predict(object, newdata, s = NA, ...)
## S3 method for class 'tslarsP'
predict(object, newdata, ...)
## S3 method for class 'tslars'
predict(object, newdata, p, ...)
## S3 method for class 'sparseLTS'
predict(object, newdata, s = NA, fit = c("reweighted",
"raw", "both"), ...)
``` |

`object` |
the model fit from which to make predictions. |

`newdata` |
new data for the predictors. If the model fit was computed with the formula method, this should be a data frame from which to extract the predictor variables. Otherwise this should be a matrix containing the same variables as the predictor matrix used to fit the model (including a column of ones to account for the intercept). |

`s` |
for the |

`p` |
an integer giving the lag length for which to make predictions (the default is to use the optimal lag length). |

`fit` |
a character string specifying for which fit to make
predictions. Possible values are |

`...` |
for the |

The `newdata`

argument defaults to the matrix of predictors used to fit
the model such that the fitted values are computed.

For autoregressive time series models with exogenous inputs with forecast
horizon *h*, the *h* most recent observations of the predictors are
omitted from fitting the model since there are no corresponding values for
the response. Hence the `newdata`

argument for `predict.tslarsP`

and `predict.tslars`

defaults to those *h* observations of the
predictors.

A numeric vector or matrix containing the requested predicted values.

Andreas Alfons

`predict`

, `rlars`

,
`grplars`

, `rgrplars`

, `tslarsP`

,
`rtslarsP`

, `tslars`

, `rtslars`

,
`sparseLTS`

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 | ```
## generate data
# example is not high-dimensional to keep computation time low
library("mvtnorm")
set.seed(1234) # for reproducibility
n <- 100 # number of observations
p <- 25 # number of variables
beta <- rep.int(c(1, 0), c(5, p-5)) # coefficients
sigma <- 0.5 # controls signal-to-noise ratio
epsilon <- 0.1 # contamination level
Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
x <- rmvnorm(n, sigma=Sigma) # predictor matrix
e <- rnorm(n) # error terms
i <- 1:ceiling(epsilon*n) # observations to be contaminated
e[i] <- e[i] + 5 # vertical outliers
y <- c(x %*% beta + sigma * e) # response
x[i,] <- x[i,] + 5 # bad leverage points
## robust LARS
# fit model
fitRlars <- rlars(x, y, sMax = 10)
# compute fitted values via predict method
predict(fitRlars)
head(predict(fitRlars, s = 1:5))
## sparse LTS over a grid of values for lambda
# fit model
frac <- seq(0.2, 0.05, by = -0.05)
fitSparseLTS <- sparseLTS(x, y, lambda = frac, mode = "fraction")
# compute fitted values via predict method
predict(fitSparseLTS)
head(predict(fitSparseLTS, fit = "both"))
head(predict(fitSparseLTS, s = NULL))
head(predict(fitSparseLTS, fit = "both", s = NULL))
``` |

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