R: Coefficient plot of a sequence of regression models

coefPlot {robustHD} | R Documentation

## Coefficient plot of a sequence of regression models

### Description

Produce a plot of the coefficients 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.

### Usage

    coefPlot(object, ...)

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

    ## S3 method for class 'tslars'
    coefPlot(object, p, zeros = FALSE, labels = NULL, ...)

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

    ## S3 method for class 'setupCoefPlot'
    coefPlot(
      object,
      abscissa = NULL,
      size = c(0.5, 2, 4),
      offset = 1,
      facets = object$facets,
      ...
    )

### Arguments

`object` | the model fit to be plotted.
`...` | additional arguments to be passed down, eventually to `geom_line` and `geom_point`.
`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. Plotting labels can be suppressed by setting this to `NA`.
`p` | an integer giving the lag length for which to produce the plot (the default is to use the optimal lag length).
`fit` | a character string specifying for which estimator to produce the plot. Possible values are `"reweighted"` (the default) for the reweighted fits, `"raw"` for the raw fits, or `"both"` for both estimators.
`abscissa` | a character string specifying what to plot on the x-axis. For objects inheriting from class `"seqModel"`, possible values are `"step"` for the step number (the default), or `"df"` for the degrees of freedom. For code"sparseLTS" objects, possible values are code"lambda" for the value of the penalty parameter (the default), or `"step"` for the step number.
`size` | a numeric vector of length three giving the line width, the point size and the label size, respectively.
`offset` | an integer giving the offset of the labels from the corresponding coefficient values from the last step (i.e., the number of blank characters to be prepended to the label).
`facets` | a faceting formula to override the default behavior. If supplied, `facet_wrap` or `facet_grid` is called depending on whether the formula is one-sided or two-sided.

### Value

An object of class `"ggplot"` (see `ggplot`).

### Author(s)

Andreas Alfons

### See Also

`ggplot`, `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)
    # create plot
    coefPlot(fitRlars)

    ## 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")
    # create plot
    coefPlot(fitSparseLTS)
    coefPlot(fitSparseLTS, fit = "both")

[Package robustHD version 0.7.2 Index]