plot.dglars {dglars} | R Documentation

## Plot from a dglars Object

### Description

Produces plots to study the sequence of models identified by dgLARS method.

### Usage

    ## S3 method for class 'dglars'
    plot(x, type = c("both", "AIC", "BIC"), ...)

### Arguments

`x` | fitted `dglars` object.
`type` | a description of the measure of goodness-of-fit used to compare the sequence of models fitted by `dglars` or `dglars.fit`. See below for a more details.
`...` | further arguments passed to the functions `AIC.dglars` or `BIC.dglars`.

### Details

`plot.dglars` method produces different plots to study the sequence of models fitted by dgLARS method.

First plot gives information about the goodness-of-fit of the sequence of models fitted by dgLARS method. The user can plot the sequence of AIC (`type = "AIC"`) or BIC values (`type = "BIC"`). By default, AIC and BIC values are shown on the same plot (`type = "both"`). More general measures of goodness-of-fit can be specified by using the argument “...” to pass futher arguments to function `AIC.dglars` (see the examples below). The value of the tuning parameter corresponding to the minimum of the used measure of goodness-of-fit is indentified by a vertical dashed red line, while the g values at which corresponds a change in the active set are labeled by vertical dashed gray lines. Second plot shows the coefficient profile plot; if the predictor-corrector algorithm is used to fit the model, the third plot shows the Rao's score test statistics as function of g.

### Author(s)

Luigi Augugliaro and Hassan Pazira
Maintainer: Luigi Augugliaro luigi.augugliaro@unipa.it

### See Also

`dglars`, `summary.dglars` and `AIC.dglars`.

### Examples

    ###########################
    # Logistic regression model
    set.seed(123)
    n <- 100
    p <- 10
    X <- matrix(rnorm(n * p), n, p)
    b <- 1:2
    eta <- b[1] + X[, 1] * b[2]
    mu <- binomial()$linkinv(eta)
    y <- rbinom(n, 1, mu)
    fit <- dglars.fit(X, y, family = binomial)
    plot(fit)
    plot(fit, type = "AIC")
    plot(fit, type = "BIC")
    plot(fit, type = "AIC", k = 5)
    plot(fit, type = "AIC", complexity = "gdf")

[Package dglars version 2.1.2 Index]