R: Set up a coefficient plot of a sequence of regression models

setupCoefPlot {robustHD} | R Documentation

## Set up a coefficient plot of a sequence of regression models

### Description

Extract the relevent information for a plot of the coefficients for 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.

### Usage

    setupCoefPlot(object, ...)

    ## S3 method for class 'seqModel'
    setupCoefPlot(object, zeros = FALSE, labels = NULL, ...)

    ## S3 method for class 'tslars'
    setupCoefPlot(object, p, ...)

    ## S3 method for class 'sparseLTS'
    setupCoefPlot(
      object,
      fit = c("reweighted", "raw", "both"),
      zeros = FALSE,
      labels = NULL,
      ...
    )

### Arguments

`object` | the model fit from which to extract information.
`...` | additional arguments to be passed down.
`zeros` | a logical indicating whether predictors that never enter the model and thus have zero coefficients should be included in the plot (`TRUE`) or omitted (`FALSE`, the default). This is useful if the number of predictors is much larger than the number of observations, in which case many coefficients are never nonzero.
`labels` | an optional character vector containing labels for the predictors. Information on labels can be suppressed by setting this to `NA`.
`p` | an integer giving the lag length for which to extract information (the default is to use the optimal lag length).
`fit` | a character string specifying for which estimator to extract information. Possible values are `"reweighted"` (the default) for the reweighted fits, `"raw"` for the raw fits, or `"both"` for both estimators.

### Value

An object inheriting from class `"setupCoefPlot"` with the following components:

`coefficients`

  a data frame containing the following columns:

  `fit`

    the model fit for which the coefficient is computed (only returned if both the reweighted and raw fit are requested in the `"sparseLTS"` method).

  `lambda`

    the value of the penalty parameter for which the coefficient is computed (only returned for the `"sparseLTS"` method).

  `step`

    the step along the sequence for which the coefficient is computed.

  `df`

    the degrees of freedom of the submodel along the sequence for which the coefficient is computed.

  `coefficient`

    the value of the coefficient.

  `variable`

    a character string specifying to which variable the coefficient belongs.

`abscissa`

  a character string specifying available options for what to plot on the x-axis

`lambda`

  a numeric vector giving the values of the penalty parameter. (only returned for the `"sparseLTS"` method).

`step`

  an integer vector containing the steps for which submodels along the sequence have been computed.

`df`

  an integer vector containing the degrees of freedom of the submodels along the sequence (i.e., the number of estimated coefficients; only returned for the `"seqModel"` method).

`includeLabels`

  a logical indicating whether information on labels for the variables should be included in the plot.

`labels`

  a data frame containing the following columns (not returned if information on labels is suppressed):

  `fit`

    the model fit for which the coefficient is computed (only returned if both the reweighted and raw fit are requested in the `"sparseLTS"` method).

  `lambda`

    the smallest value of the penalty parameter (only returned for the `"sparseLTS"` method).

  `step`

    the last step along the sequence.

  `df`

    the degrees of freedom of the last submodel along the sequence.

  `coefficient`

    the value of the coefficient.

  `label`

    the label of the corresponding variable to be displayed in the plot.

`facets`

  default faceting formula for the plots (only returned if both estimators are requested in the `"sparseLTS"` method).

### Author(s)

Andreas Alfons

### See Also

`coefPlot`, `rlars`, `grplars`, `rgrplars`, `tslarsP`, `rtslarsP`, `tslars`, `rtslars`, `sparseLTS`

### Examples

    ## 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)
    # extract information for plotting
    setup <- setupCoefPlot(fitRlars)
    coefPlot(setup)

    ## 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")
    # extract information for plotting
    setup1 <- setupCoefPlot(fitSparseLTS)
    coefPlot(setup1)
    setup2 <- setupCoefPlot(fitSparseLTS, fit = "both")
    coefPlot(setup2)

[Package robustHD version 0.7.2 Index]