general-lassosig-methods: Call 'LassoSig' for multiple response variables

Description Usage Arguments Value See Also

Description

Call LassoSig for multiple response variables

Usage

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GeneralLassoSig(Y, X, B = 100, s = c("lambda.min", "lambda.1se", "halfway",
  "usefixed"), gamma.min = 0.05, fixedP = NULL, include = NULL,
  nfolds = 10, intercept = TRUE)

Arguments

Y

Response matrix for general regression with GeneralLassoSig

X

Design matrix

B

Number of times to partition the sample

s

The value of lambda to use in lasso. Can be:

  • An actual value of lambda you have determined

  • lambda.min The lambda that minimises the mean squared error from n-fold cross-validation (recommended)

  • lambda.1se The lambda that accounts for the smallest number of parameters and is within 1 standard error from lambda.min

  • halfway The lambda that is halfway between lambda.min and lambda.1se

  • usefixed Use the smallest lambda that accounts for a given number of parameters (set by fixedP)

include

Set of predictors to be force-included in OLS analysis

gamma.min

Lower bound for gamma in the adaptive search for the best p-value (default 0.05)

fixedP

The fixed number of parameters to use (if s == "usefixed")

nfolds

Number of folds of cross-validation in the glmnet n-fold crossvalidation

intercept

Whether to include an intercept in the OLS regression (default = TRUE)

Value

An n-by-m matrix of p-values for n predictors and m response variables

See Also

LassoSig


kieranrcampbell/SpatialStats documentation built on May 18, 2017, 7:44 p.m.