lasso2 (version 1.2-21.1)

summary.l1ce: Summary Method for ``l1ce'' Objects (Regression with L1 Constraint)

Description

Returns a summary list for a regression model with an L1 constraint on the parameters. A null value will be returned if printing is invoked.

Usage

# S3 method for l1ce
summary(object, correlation = TRUE,
         type = c("OPT", "Tibshirani"),
         gen.inverse.diag = 0, sigma = NULL, …)
# S3 method for summary.l1ce
print(x, digits = max(3, getOption("digits") - 3), …)

Arguments

object

fitted model of class "l1ce".

correlation

logical indicating if the correlation matrix for the coefficients should be included in the summary.

type

character string specifying whether to use the covariance formula of Osborne, Presnell and Turlach or the formula of Tibshirani.

gen.inverse.diag

if Tibshirani's formula for the covariance matrix is used, this value is used for the diagonal elements of the generalised inverse that appears in the formula that corresponds to parameters estimated to be zero. The default is 0, i.e. use the Moore-Penrose inverse. Tibshirani's code uses gen.inverse.diag=1e11.

sigma

the residual standard error estimate. If not provided, then it is estimated by the deviance of the model divided by the error degrees of freedom.

x

an R object of class summary.l1ce.

digits

number of significant digits to use.

further potential arguments passed to methods.

Value

an object of class summary.l1ce (for which there's a print method). It is basically a list with the following components:

correlation

the computed correlation coefficient matrix for the coefficients in the model.

cov.unscaled

the unscaled covariance matrix; i.e, a matrix such that multiplying it by an estimate of the error variance produces an estimated covariance matrix for the coefficients.

df

the number of degrees of freedom for the model and for residuals.

coefficients

a matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic.

residuals

the model residuals. These are the weighted residuals if weights were given in the model.

sigma

the residual standard error estimate.

terms

the terms object used in fitting this model.

call

the call object used in fitting this model.

bound

the bound used in fitting this model.

relative.bound

the relative bound used in fitting this model (may not be present).

Lagrangian

the Lagrangian of the model.

Details

This function is a method for the generic function summary() for class "l1ce". It can be invoked by calling summary(x) for an object x of the appropriate class, or directly by calling summary.l1ce(x) regardless of the class of the object.

See Also

l1ce, l1ce.object, summary.

Examples

Run this code
# NOT RUN {
<!-- %%- or just those in ./l1ce.Rd -->
# }
# NOT RUN {
data(Prostate)
summary(l1ce(lpsa ~ .,Prostate))

# Produces the following output:
# }
# NOT RUN {
Call:
 l1ce(formula = lpsa ~ ., data = Prostate)
Residuals:
    Min      1Q Median    3Q  Max
 -1.636 -0.4119  0.076 0.452 1.83


Coefficients:
             Value Std. Error Z score Pr(>|Z|)
(Intercept) 0.7285 1.3898     0.5242  0.6002
     lcavol 0.4937 0.0919     5.3711  0.0000
    lweight 0.2682 0.1774     1.5115  0.1307
        age 0.0000 0.0111     0.0000  1.0000
       lbph 0.0093 0.0587     0.1581  0.8744
        svi 0.4551 0.2525     1.8023  0.0715
        lcp 0.0000 0.0947     0.0000  1.0000
    gleason 0.0000 0.1685     0.0000  1.0000
      pgg45 0.0002 0.0046     0.0391  0.9688


Residual standard error: 0.7595 on 88.36 degrees of freedom
The relative L1 bound was      : 0.5
The absolute L1 bound was      : 0.9219925
The Lagrangian for the bound is:  13.05806


Correlation of Coefficients:
        (Intercept)  lcavol lweight     age    lbph     svi     lcp gleason
 lcavol  0.1988
lweight -0.4815     -0.2071
    age -0.3938     -0.0603 -0.0974
   lbph  0.3629     -0.0201 -0.5165 -0.1303
    svi -0.0624     -0.2273 -0.1442  0.0635  0.0648
    lcp  0.0457     -0.4153  0.0598  0.0665  0.0632 -0.3779
gleason -0.7666     -0.2009  0.1163 -0.0774 -0.0617  0.1084 -0.0243
  pgg45  0.4988      0.0956 -0.0380 -0.0630 -0.1111 -0.1921 -0.2935 -0.6526
# }

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