Description Usage Arguments Details Value Author(s) References See Also Examples

Evaluates all models in a set of candidates, and ranks them by IC such as AIC. Either lm or lme can be used for the model.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Find the minimum IC
## Default S3 method:
FindMinIC(coly, candidates = c(""), fixed = c(""), data = list(),
modeltype = "lm", random = ~1, ic = "AIC", ...)
## S3 method for class 'formula'
FindMinIC(formula, data=list(), na.action=na.omit, fixed = c(""), random = ~1, ...)
# find the minimum IC, fmi is the shorter name form of FindMinIC
## Default S3 method:
fmi(coly, candidates = c(""), fixed = c(""), data = list(),
modeltype = "lm", random = ~1, ic = "AIC", ...)
## S3 method for class 'formula'
fmi(formula, data=list(), na.action=na.omit, fixed = c(""), random = ~1, ...)
``` |

`formula` |
A formula containing the response variable and terms. All the terms of the formula become candidates for inclusion as covariates. |

`na.action` |
action to use when data contains NAs. Options include na.omit, na.exclude, na.fail |

`coly` |
The name of the column to use for the response variable |

`candidates` |
A list of names of columns that are candidates for inclusion as covariates in the model |

`fixed` |
A list of names of columns (can be empty) that must always be included in every model |

`data` |
An object containing the variables for use in the model. |

`modeltype` |
Currently a choice between |

`random` |
When |

`ic` |
Type of information criterion to used. Defaults to |

`...` |
Extra arguments are passed directly into the call to |

FindMinIC tries all possible model combinations of the candidate covariates, while always including the same response variable and fixed variables. It returns a list of candidate models ranked by IC. The model combinations include all 2-way interactions among the candidate variables. Other interactions (like age^2) can be directly included in the candidates or fixed lists.

FindMinIC returns a list of candidate models sorted by information criterion IC. The first model has the "best" IC. The list is of class(`"cmList"`

) while each element of that list is of class(`"cm"`

)
see `cmList`

for more details

Nicholas Lange, Tom Fletcher, Kristen Zygmunt

Burnham, K. P.; Anderson, D. R. (2004), "Multimodel inference: understanding AIC and BIC in Model Selection", Sociological Methods and Research 33: 261-304.

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 33 34 35 36 37 | ```
data(iris)
coly="Sepal.Length"
fixed="Sepal.Width"
candidates=c("Species","-1","Sepal.Width:Species")
results.lm = FindMinIC(coly, candidates, fixed, iris)
# model with lowest IC:
first.model = getFirstModel(results.lm)
print(summary(first.model))
# model with 3rd lowest IC:
third.model = getNthModel(results.lm, 3)
print(summary(third.model))
# list of first 5 models, ordered by AIC
print(summary(results.lm)$table[1:5,])
# list of first 5 models, ordered by BIC
results.bic = FindMinIC(coly, candidates, fixed, iris, ic="BIC")
print(summary(results.bic)$table[1:5,])
fm = FindMinIC(Infant.Mortality ~ ., data = swiss)
summary(fm)
fm2 = FindMinIC(Infant.Mortality ~ Fertility + Agriculture + Education * Catholic,
data = swiss)
summary(fm2)
# list of first 5 models, ordered by AICc
if (require(nlme)) {
results.aicc = FindMinIC(distance~age, data=Orthodont,
ic="AICc", model="lme",
random= ~ 1 | Subject)
print(summary(results.aicc))
}
``` |

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