A class for unfitted statistical models.

Objects can be created by calls of the form `new("StatModel", ...)`

.

`name`

:Object of class

`"character"`

, the name of the model.`dpp`

:Object of class

`"function"`

, a function for data preprocessing (usually formula-based).`fit`

:Object of class

`"function"`

, a function for fitting the model to data.`predict`

:Object of class

`"function"`

, a function for computing predictions.`capabilities`

:Object of class

`"StatModelCapabilities"`

.

- fit
`signature(model = "StatModel", data = "ModelEnv")`

: fit`model`

to`data`

.

This is an attempt to provide unified infra-structure for unfitted
statistical models. Basically, an unfitted model provides a function for
data pre-processing (`dpp`

, think of generating design matrices),
a function for fitting the specified model to data (`fit`

), and
a function for computing predictions (`predict`

).

Examples for such unfitted models are provided by `linearModel`

and
`glinearModel`

which provide interfaces in the `"StatModel"`

framework
to `lm.fit`

and `glm.fit`

, respectively. The functions
return objects of S3 class `"linearModel"`

(inheriting from `"lm"`

) and
`"glinearModel"`

(inheriting from `"glm"`

), respectively. Some
methods for S3 generics such as `predict`

, `fitted`

, `print`

and `model.matrix`

are provided to make use of the `"StatModel"`

structure. (Similarly, `survReg`

provides an experimental interface to
`survreg`

.)

# NOT RUN { ### linear model example df <- data.frame(x = runif(10), y = rnorm(10)) mf <- dpp(linearModel, y ~ x, data = df) mylm <- fit(linearModel, mf) ### equivalent print(mylm) lm(y ~ x, data = df) ### predictions Predict(mylm, newdata = data.frame(x = runif(10))) # }