Efficient Linear Model ("elm") is used to fit linear
models in an equivalent way to "lm" but in a reduced time
depending on the design matrix.
elm(
formula,
data,
subset,
weights,
na.action,
method = "qr",
model = TRUE,
x = FALSE,
y = FALSE,
qr = TRUE,
singular.ok = TRUE,
contrasts = NULL,
offset,
reduce = TRUE,
...
)an object of class "formula" (or one that can be
coerced to that class): a symbolic description of the model to be fitted.
The details of model specification are given under ‘Details’.
an optional data frame, list or environment (or object coercible
by as.data.frame to a data frame) containing the variables in
the model. If not found in data, the variables are taken from
environment(formula), typically the environment from which lm
is called.
an optional vector specifying a subset of observations to be used in the fitting process.
an optional vector of weights to be used in the fitting
process. Should be NULL or a numeric vector. If non-NULL, weighted
least squares is used with weights weights (that is, minimizing
sum(w*e^2)); otherwise ordinary least squares is used.
a function which indicates what should happen when the data
contain NAs. The default is set by the na.action setting of
options, and is na.fail if that is unset. The
‘factory-fresh’ default is na.omit. Another possible
value is NULL, no action. Value na.exclude can be
useful.
the method to be used in fitting the model. The default method
"eglm.wfit" uses iteratively reweighted least squares (IWLS): the
alternative "model.frame" returns the model frame and does no
fitting. User-supplied fitting functions can be supplied either as a
function or a character string naming a function, with a function which
takes the same arguments as glm.fit from the stats package.
If specified as a character string it is looked up from within the
eflm namespace.
a logical value indicating whether model frame should be included as a component of the returned value.
logical values indicating whether the
model matrix (x) and the response vector (y)
used in the fitting process should be returned as components of the returned
value.
logical values indicating whether the
model matrix (x) and the response vector (y)
used in the fitting process should be returned as components of the returned
value.
logical. If TRUE the corresponding QR decomposition component of the fit is returned.
logical; if FALSE a singular fit is an error.
an optional list. See the contrasts.arg of
model.matrix.default.
this can be used to specify an a priori known component
to be included in the linear predictor during fitting. This should be
NULL or a numeric vector or matrix of extents matching those of the
response. One or more offset terms can be included in the
formula instead or as well, and if more than one are specified their sum is
used. See model.offset.
logical; if TRUE an alternate design matrix of p * p is
used for the fitting instead of the traditional n * p design matrix.
For eglm: arguments to be used to form the default control argument if it is not supplied directly. For weights: further arguments passed to or from other methods.
An object of class "elm" that behaves the same way as the "lm"
class, see the function "lm". This output also includes the
logical "reduce" and, depending on it, the reduced design matrix "xtx"
when the reduce argument is set to TRUE.
Models for elm are specified symbolically.
A typical model has the form response ~ terms where response
is the (numeric) response vector and terms is a series of terms which
specifies a linear predictor for response. A terms specification of
the form first + second indicates all the terms in first
together with all the terms in second with duplicates removed. A
specification of the form first:second indicates the set of
terms obtained by taking the interactions of all terms in first
with all terms in second. The specification first*second
indicates the cross of first and second. This is
the same as first + second + first:second, and exactly the same as
"lm" from the stats package.
elm(mpg ~ wt, data = mtcars)
#>
#> Call:
#> elm(formula = mpg ~ wt, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt
#> 37.285 -5.344
#>