# gl1ce

From lasso2 v1.1-0
by Martin Maechler

##### 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

##### See Also

`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:
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 1.1-0, License: GPL version 2 or later*

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