gl1ce

0th

Percentile

Generalized Regression With L1-constraint on the Parameters

Fit a generalized regression problem while imposing an L1 constraint on the parameters. Returns an object of class gl1ce.

Keywords
models, regression, optimize
Usage
gl1ce(formula, data = sys.parent(), weights, subset, na.action,
      family = gaussian, control = glm.control(...), sweep.out = ~ 1,
      x = FALSE, y = TRUE, contrasts = NULL, standardize = TRUE,
      guess.constrained.coefficients = double(p), bound = 0.5, ...)
## S3 method for class 'gl1ce':
family(object, \dots)
Value

an object of class gl1ce is returned by gl1ce(). See gl1ce.object for details.

References

See the references in l1ce.

Justin Lokhorst (1999). The LASSO and Generalised Linear Models, Honors Project, Nov.1999, Dept.Statist., Univ. of Adelaide. Available as file Doc/justin.lokhorst.ps.gz in both shar files from http://www.maths.uwa.edu.au/~berwin/software/lasso.html.

See Also

glm for unconstrained generalized regression modeling.

Aliases
Examples
## example from base:
data(esoph)
summary(esoph)
## effects of alcohol, tobacco and interaction, age-adjusted
modEso <- formula(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp)
glm.E   <- glm(modEso, data = esoph, family = binomial())
gl1c.E <- gl1ce(modEso, data = esoph, family = binomial())
gl1c.E
plot(residuals(gl1c.E) ~ fitted(gl1c.E))

sg1c <- summary(gl1c.E)
sg1c

## Another comparison  glm() / gl1c.E:
plot(predict(glm.E,  type="link"), predict(glm.E,  type="response"),
     xlim = c(-3,0))
points(predict(gl1c.E, type="link"), predict(gl1c.E, type="response"),
       col = 2, cex = 1.5)labels(gl1c.E)#-- oops! empty!!
Documentation reproduced from package lasso2, version 1.2-0, License: GPL version 2 or later

Community examples

Looks like there are no examples yet.