- formula
an S formula object, e.g.

Y ~ rcs(x1,5)*lsp(x2,c(10,20))

- data
name of an S data frame containing all needed variables. Omit this to use a
data frame already in the S ``search list''.

- weights
an optional vector of weights to be used in the fitting
process. If specified, weighted least squares is used with
weights `weights`

(that is, minimizing \(sum(w*e^2)\));
otherwise ordinary least squares is used.

- subset
an expression defining a subset of the observations to use in the fit. The default
is to use all observations. Specify for example `age>50 & sex="male"`

or
`c(1:100,200:300)`

respectively to use the observations satisfying a logical expression or those having
row numbers in the given vector.

- na.action
specifies an S function to handle missing data. The default is the function `na.delete`

,
which causes observations with any variable missing to be deleted. The main difference
between `na.delete`

and the S-supplied function `na.omit`

is that
`na.delete`

makes a list
of the number of observations that are missing on each variable in the model.
The `na.action`

is usally specified by e.g. `options(na.action="na.delete")`

.

- method
specifies a particular fitting method, or `"model.frame"`

instead to return the model frame
of the predictor and response variables satisfying any subset or missing value
checks.

- model
default is `FALSE`

. Set to `TRUE`

to return the model frame
as element `model`

of the fit object.

- x
default is `FALSE`

. Set to `TRUE`

to return the expanded design matrix as element `x`

(without intercept indicators) of the
returned fit object. Set both `x=TRUE`

if you are going to use
the `residuals`

function later to return anything other than ordinary residuals.

- y
default is `FALSE`

. Set to `TRUE`

to return the vector of response values
as element `y`

of the fit.

- se.fit
default is `FALSE`

. Set to `TRUE`

to compute the estimated standard errors of
the estimate of \(X\beta\) and store them in element `se.fit`

of the fit.

- linear.predictors
set to `FALSE`

to cause predicted values not to be stored

- penalty
see `lrm`

- penalty.matrix
see `lrm`

- tol
tolerance for information matrix singularity

- sigma
If `sigma`

is given, it is taken as the actual root mean squared error parameter for the model. Otherwise `sigma`

is estimated from the data using the usual formulas (except for penalized models). It is often convenient to specify
`sigma=1`

for models with no error, when using `fastbw`

to find an
approximate model that predicts predicted values from the full model with
a given accuracy.

- var.penalty
the type of variance-covariance matrix to be stored in the `var`

component of the fit when penalization is used. The default is the
inverse of the penalized information matrix. Specify
`var.penalty="sandwich"`

to use the sandwich estimator (see below
under `var`

), which limited simulation studies have shown yields
variances estimates that are too low.

- ...
arguments to pass to `lm.wfit`

or
`lm.fit`