Class "StatModel"

A class for unfitted statistical models.

Objects from the Class

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

Slots

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".

Methods

fit

signature(model = "StatModel", data = "ModelEnv"): fit model to data.

Details

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.)

Examples

  ### 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)
#> Linear model with coefficients:
#> (Intercept)            x  
#>      0.6243      -0.7374  
  lm(y ~ x, data = df)
#> 
#> Call:
#> lm(formula = y ~ x, data = df)
#> 
#> Coefficients:
#> (Intercept)            x  
#>      0.6243      -0.7374  
#> 

  ### predictions
  Predict(mylm, newdata =  data.frame(x = runif(10)))
#>           1           2           3           4           5           6 
#>  0.46210168  0.08753049  0.46425172  0.28791558  0.37892806  0.20524112 
#>           7           8           9          10 
#>  0.43835476  0.28217645 -0.05232398 -0.09301416