A function for termwise feasiblity
twFSA(
formula,
data,
fitfunc = lm,
fixvar = NULL,
quad = FALSE,
cores = 1,
criterion = AIC,
minmax = "min",
checkfeas = NULL,
var4int = NULL,
min.nonmissing = 1,
...
)an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
a data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
the method that should be used to fit the model. For Example: lm, glm, or other methods that rely on formula, data, and other inputs.
variable(s) to fix in the model. Usually a covariate that should always be included (Example: Age, Sex). Will still consider it with interactions. Default is NULL.
Include quadratic terms or not. Logical.
number of cores to use while running. Note: Windows can only use 1 core. See mclapply for details. If function detects a Windows user it will automatically set cores=1.
which criterion function to either maximize or minimize. For linear models one can use: r.squared, adj.r.squared, cv5.lmFSA (5 Fold Cross Validation error), cv10.lmFSA (10 Fold Cross Validation error), apress (Allen's Press Statistic), int.p.val (Interaction P-value), AIC, BIC.
whether to minimize or maximize the criterion function
vector of variables that could be a feasible solution. These variables will be used as the last random start.
specification of which variables to check for marginal feasiblilty. Default is NULL
the combination of predictors will be ignored unless this many of observations are not missing
other arguments passed to fitfunc.
matrix of results