# gl1ce

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##### 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
##### 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.

glm for unconstrained generalized regression modeling.

##### 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:
col = 2, cex = 1.5)labels(gl1c.E)#-- oops! empty!!