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

.

```
gl1ce(formula, data = parent.frame(), 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 gl1ce
family(object, …)
```

formula

a `formula`

, with the response on the left
hand side of a `~`

operator, and the terms, separated by a
`+`

operator, on the right hand side.

data

a `data.frame`

in which to interpret the
variables named in the formula, the `weights`

, the
`subset`

and the `sweep.out`

argument. If this is
missing, then the variables in the formula should be globally
available.

weights

vector of observation weights. The length of
`weights`

must be the same as the number of observations. The
weights must be strictly positive, since zero weights are ambiguous,
compared to use of the `subset`

argument.

subset

expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.

na.action

a function to be applied to the `model.frame`

after any
`subset`

argument has been used. The default (with
`na.fail`

) is to create an error if any missing values are
found. A possible alternative is `na.omit`

, which deletes
observations that contain one or more missing values.

family

a `family`

object - a list of functions and
expressions for defining the link and variance functions,
initialization and iterative weights. Families supported are
gaussian, binomial, poisson, Gamma, inverse.gaussian and quasi.
Functions like binomial produce a family object, but can be given
without the parentheses. Family functions can take arguments, as in
`binomial(link=probit)`

.

control

a list of iteration and algorithmic constants. See glm.control for their names and default values. These can also be set as arguments to gl1ce itself.

sweep.out

a formula object, variables whose parameters are not put under the
constraint are swept out first. The variables should appear on the
right of a `~`

operator and be separated by `+`

operators. Default is
`~1`

, i.e. the constant term is not under the constraint. If this
parameter is `NULL`

, then all parameters are put under the constraint.

x

logical flag: if `TRUE`

, the model matrix is returned in component `x`

.

y

logical flag: if `TRUE`

, the response is returned in component `y`

.

contrasts

a list giving contrasts for some or all of the factors appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels.

standardize

logical flag: if `TRUE`

, then the columns of the model matrix that
correspond to parameters that are constrained are standardized to have
empirical variance one. The standardization is done after taking
possible weights into account and after sweeping out variables whose
parameters are not constrained.

guess.constrained.coefficients

initial guess for the parameters that are constrained.

bound

numeric, either a single number or a vector: the constraint(s) that is/are put onto the L1 norm of the parameters.

…

potential arguments for `glm.control`

,
as default for the `control`

argument above.

object

an R object of class `"gl1ce"`

.

an object of class `gl1ce`

is returned by `gl1ce()`

.
See `gl1ce.object`

for details.

See the references in `l1ce`

.

Justin Lokhorst (1999). The LASSO and Generalised Linear Models, Honors Project, Nov. 1999, Dept.Statist., Univ. of Adelaide.

`glm`

for unconstrained generalized regression
modeling.

# NOT RUN { ## 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) # } # NOT RUN { <!-- %%% mabye FIXME!! --> # } # NOT RUN { labels(gl1c.E)#-- oops! empty!! # }