Description Usage Arguments Details Value See Also Examples

Fit a statistical model using different estimators (e.g., robust and least-squares) or combine fitted models into a single object. Generic methods then produce side-by-side comparisons of the parameter estimates and diagnostic plots.

1 | ```
fit.models(model.list, ...)
``` |

`model.list` |
a list or a character vector containing names of modeling
functions. Only required when |

`...` |
see details. |

There are two distinct ways the `fit.models`

function can be used.

The first is to fit the same model using different estimators. In this
case, `model.list`

should be a character vector or a list where each
element is the name of a modeling function and the remaining arguments (in
...) are the common arguments to the functions in `model.list`

.
For example, the following command fits robust and least squares linear
models to Brownlee's Stack Loss Plant Data.

1 | ```
fit.models(c("rlm", "lm"), stack.loss ~ ., data = stackloss)
``` |

The resulting
`fit.models`

object is a list with the output of

1 | ```
rlm(stack.loss ~ ., data = stackloss)
``` |

in the first element and

1 | ```
lm(stack.loss ~ ., data = stackloss)
``` |

in the second. The
class attribute of the returned list is set (in this case) to `"lmfm"`

which is the `fit.models`

class (fmclass) for comparing linear-model-like
fits.

The second use of fit.models is to combine fitted model objects. In
this case, `fit.models`

combines its arguments into a fit.models object
(a list where element *i* is occupied by argument *i* and sets the
class attribute to the appropriate `fit.models`

class.

The returned object is a list containing the fitted models. The class of the retuned object depends on the classes of the model objects it contains.

`fmclass.add.class`

for adding a class to an existing
fit.models class and `fmclass.register`

to create a new
fit.models class.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
# First, use fit.models to fit robust and least squares linear
# regression models to Brownlee's Stack Loss Plant Data.
# Step 1: rlm (robust linear model) is in the MASS package.
library(MASS)
# Step 2: tell fit.models rlm can be compared to lm
fmclass.add.class("lmfm", "rlm")
fm1 <- fit.models(c("rlm", "lm"), stack.loss ~ ., data = stackloss)
summary(fm1) #rlm does not provide p-values or Multiple R-squared
# Second, use fit.models to combine fitted models into a
# fit.models object.
lm.complete <- lm(stack.loss ~ ., data = stackloss)
lm.clean <- lm(stack.loss ~ ., data = stackloss, subset = 5:20)
fm2 <- fit.models(lm.clean, lm.complete)
summary(fm2)
plot(fm2)
# Name the models in the fit.models object.
fm3 <- fit.models(c(Robust = "rlm", "Least Squares" = "lm"),
stack.loss ~ ., data = stackloss)
fm4 <- fit.models(Clean = lm.clean, Complete = lm.complete)
``` |

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