predict.dglars {dglars} | R Documentation

## Predict Method for dgLARS Fits.

### Description

`predict.dglars` is used to obtain general predictions from a `dglars` object.

### Usage

    ## S3 method for class 'dglars'
    predict(object, xnew, ynew, g = NULL,
    type = c("coefficients", "nnonzero", "predictors", "eta",
    "mu", "probability", "class", "deviance"), ...)

### Arguments

`object` | fitted `dglars` object.
`xnew` | matrix of new values of the predictors at which predictions are to be made. This argument is not used for `type` “`coefficients`”, “`nnonzero`” and “`predictors`”.
`ynew` | vector of new values of the responce variable. This argument is used only when `type` ie equal to “`deviance`”.
`g` | value(s) of the tuning parameter g at which the predictions are required. By default, the predictions are made using the sequence of g values storaged in `dglars`.
`type` | type of prediction required; see below for more details.
`...` | additional argument used to ensure the compatibility with the generic method function “`predict`”.

### Value

The object returned by `predict.dglars` depends on `type` argument:

``coefficients`:` | a named list with components “`beta`”, i.e., the matrix corresponding to the dgLARS solution curve, and “`phi`”, i.e., the sequence of Pearson estimates of the dispersion parameter;
``nnonzero`:` | the number of nonzero estimates;
``predictors`:` | a named list; each component is a vector containing the indices of the variables that are in the active set;
``eta`:` | a matrix with the linear preditors. If `xnew` is not specified then the linear predictors are computed using `object$X`;

``mu`:`

a matrix with the fitted expeted values, obtained by transforming the linear predictor by the inverse of the link function. For models with ‘binomial’ family, canonical link function (‘`logit`’) and response a vector with elements 0 (failure) or 1 (success), `type = "mu"` and `type = "probability"` give the same result. If `xnew` is not spefified then these values are computed using `object$X`;

``probability`:`

available only for ‘`binomial`’ family. In this case `predict.dglars` returns a matrix with the fitted probabilities; furthermore, if the model is specified by using the canonical link function (‘`logit`’) and response a vector with entries 0/1, `type = "mu"` and `type = "probability"` give the same result. If `xnew` is not specified then these values are computed using `object$X`;

``class`:` | available only for ‘`binomial`’ family. In this case `predict.dglars` returns a matrix with the fitted class. If `xnew` is not specified then these values are computed using `object$X` otherwise `xnew` is used to compute the fitted probabilities;
``deviance`:` | a vector with the scaled residual deviances.

### Author(s)

Luigi Augugliaro
Maintainer: Luigi Augugliaro luigi.augugliaro@unipa.it

### See Also

`dglars` and `coef.dglars`.

### Examples

    ######################
    # Logistic regression model
    set.seed(123)
    n <- 100
    p <- 10
    X <- matrix(rnorm(n * p), n, p)
    Xnew <- 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, binomial)
    coef(fit)
    predict(fit, type = "coefficients")
    g <- seq(3, 1, by = -0.1)
    coef(fit, g = g)
    predict(fit, type = "coefficients", g = g)
    predict(fit, type = "nnonzero")
    predict(fit, type = "nnonzero", g = g)
    predict(fit, type = "predictors")
    predict(fit, type = "predictors", g = g)
    predict(fit, type = "eta", g = g)
    predict(fit, type = "eta", g = g, xnew = Xnew)
    predict(fit, type = "mu", g = g)
    predict(fit, type = "mu", g = g, xnew = Xnew)
    predict(fit, type = "probability", g = g)
    predict(fit, type = "probability", g = g, xnew = Xnew)
    predict(fit, type = "class", g = g)
    predict(fit, type = "class", g = g, xnew = Xnew)

[Package dglars version 2.1.2 Index]