View source: R/ModelSelection.Phase.R
ModelSelection.Phase | R Documentation |
This function tests low dimensional subsests of the set of retained variables from the reduction phase and any squared or interaction terms suggested at the exploratory phase. Lists of well-fitting models of each dimension are returned.
ModelSelection.Phase(X,Y, list.reduction, family=gaussian,
signif=0.01, sq.terms=NULL, in.terms=NULL,
modelSize=NULL, Cox.Hazard = FALSE)
X |
Design matrix. |
Y |
Response vector. |
list.reduction |
Indices of variables that where chosen at the reduction phase. |
family |
A description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function. See |
signif |
Significance level of the likelihood ratio test against the comprehensive model. The default is 0.01. |
sq.terms |
Indices of squared terms suggested at the exploratory phase (See |
in.terms |
Indices of pairs of variables suggested at the exploratory phase (See |
modelSize |
Maximum size of the models to be tested. Curently the maximum is 7. If not provided a default is used. |
Cox.Hazard |
If TRUE fits proportional hazards regression model. The family argument will be ignored if Cox.Hazard=TRUE. |
goodModels |
List of models that are in the confidence set of size 1 to modelSize. An interaction term between, say, variables x_1 and x_2 is displayed as “x_1 * x_2”; a squared term in, say, variable x_1 is displayed as “x_1 ^2”. If an interaction term is present without the corresponding main effects, the main effects should be added. |
The work was supported by the UK Engineering and Physical Sciences Research Council under grant number EP/P002757/1.
Hoeltgebaum, H. H.
Cox, D. R. and Battey, H. S. (2017). Large numbers of explanatory variables, a semi-descriptive analysis. Proceedings of the National Academy of Sciences, 114(32), 8592-8595.
Battey, H. S. and Cox, D. R. (2018). Large numbers of explanatory variables: a probabilistic assessment. Proceedings of the Royal Society of London, A., 474(2215), 20170631.
Hoeltgebaum, H., & Battey, H. S. (2019). HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions. The R Journal, 11(2), 370-379.
Reduction.Phase
, Exploratory.Phase
## Generates a random DGP
dgp = DGP(s=5, a=3, sigStrength=1, rho=0.9, n=100, intercept=5, noise=1,
var=1, d=1000, DGP.seed = 2018)
#Reduction Phase using only the first 70 observations
outcome.Reduction.Phase = Reduction.Phase(X=dgp$X[1:70,],Y=dgp$Y[1:70],
family=gaussian, seed.HC = 1012)
# Exploratory Phase using only the first 70 observations, choosing the variables which
# were selected at least two times in the third dimension reduction
idxs = outcome.Reduction.Phase$List.Selection$`Hypercube with dim 2`$numSelected1
outcome.Exploratory.Phase = Exploratory.Phase(X=dgp$X[1:70,],Y=dgp$Y[1:70],
list.reduction = idxs,
family=gaussian, signif=0.01)
# Model Selection Phase using only the remainer observations
sq.terms = outcome.Exploratory.Phase$mat.select.SQ
in.terms = outcome.Exploratory.Phase$mat.select.INTER
MS = ModelSelection.Phase(X=dgp$X[71:100,],Y=dgp$Y[71:100], list.reduction = idxs,
sq.terms = sq.terms,in.terms = in.terms, signif=0.01)
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