nonadaptivefence: Nonadaptive Fence model selection
In fence: Using Fence Methods for Model Selection

Description Usage Arguments Value Author(s) References Examples

Nonadaptive Fence model selection

1	nonadaptivefence(mf, f, ms, d, lf, pf, cn)

`mf`	function for fitting the model
`f`	formula of full model
`ms`	list of formula of candidates models
`d`	data
`lf`	measure lack of fit (to minimize)
`pf`	model selection criteria, e.g., model dimension
`cn`	given a specific c value

`models`	list all model candidates in the model space
`lack_of_fit`	list a vector of Qs for all model candidates
`formula`	list the model of the selected parsimonious model
`sel_model`	list the selected (parsimonious) model given the adaptive c value

Jiming Jiang Jianyang Zhao J. Sunil Rao Thuan Nguyen

Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692
Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629
Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662

## Not run: 
require(fence)

#### Example 1 #####
data(iris)
full = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + (1|Species)
test_naf = fence.lmer(full, iris, fence = "nonadaptive", cn = 12)
test_naf$sel_model

## End(Not run)