mvForwardStepwise: Multivariate forward stepwise regression

In GabrielHoffman/mvBIC: Multivariate Information Criteria to identify important predictors in high dimensional multivariate regression

Multivariate forward stepwise regression

Description

Multivariate forward stepwise regression evluated by multivariate BIC

Usage

mvForwardStepwise(
  exprObj,
  baseFormula,
  data,
  variables,
  criterion = c("BIC", "sum BIC", "AIC", "AICC", "CAIC", "sum AIC"),
  shrink.method = c("EB", "none", "var_equal", "var_unequal"),
  nparamsMethod = c("edf", "countLevels", "lme4"),
  deltaCutoff = 5,
  pca = TRUE,
  verbose = TRUE,
  ...
)

Arguments

exprObj	matrix of expression data (g genes x n samples), or ExpressionSet, or EList returned by voom() from the limma package
baseFormula	specifies baseline variables for the linear (mixed) model. Must only specify covariates, since the rows of exprObj are automatically used as a response. e.g.: ~ a + b + (1|c) Formulas with only fixed effects also work, and lmFit() followed by contrasts.fit() are run.
data	data.frame with columns corresponding to formula
variables	array of variable names to be considered in the regression. If variable should be considered as random effect, use '(1|A)'.
criterion	multivariate criterion ('AIC', 'BIC') or summing score assuming independence of reponses ('sum AIC', 'sum BIC')
shrink.method	Shrink covariance estimates to be positive definite. Using "var_equal" assumes all variance on the diagonal are equal. This method is the fastest because it is linear time. Using "var_unequal" allows each response to have its own variance term, however this method is quadratic time. Using "none" does not apply shrinkge, but is only valid when there are very few responses
nparamsMethod	"edf": effective degrees of freedom. "countLevels" count number of levels in each random effect. "lme4" number of variance compinents, as used by lme4. See description in nparam
deltaCutoff	stop interating of the model improvement is less than deltaCutoff. default is 5
pca	use PCA to transform variables
verbose	Default TRUE. Print messages
...	additional arguements passed to logDet

Value

list with formula of final model, and trace of iterations during model selection

Examples

Y = with(iris, rbind(Sepal.Width, Sepal.Length))

# fit forward stepwise regression starting with model: ~1. 
bestModel = mvForwardStepwise( Y, ~ 1, data=iris, variables=colnames(iris)[3:5])

bestModel

GabrielHoffman/mvBIC documentation built on Sept. 5, 2022, 1:57 a.m.