setup_lambda: Compute sequence of lambda values
In plmmr: Penalized Linear Mixed Models for Correlated Data

setup_lambda

R Documentation

Compute sequence of lambda values

This function allows you compute a sequence of lambda values for plmm models.

setup_lambda(
  X,
  y,
  alpha,
  lambda_min,
  nlambda,
  penalty_factor,
  intercept = TRUE
)

`X`	Rotated and standardized design matrix which includes the intercept column if present. May include clinical covariates and other non-SNP data. This can be either a 'matrix' or 'FBM' object.
`y`	Continuous outcome vector.
`alpha`	Tuning parameter for the Mnet estimator which controls the relative contributions from the MCP/SCAD penalty and the ridge, or L2 penalty. alpha=1 is equivalent to MCP/SCAD penalty, while alpha=0 would be equivalent to ridge regression. However, alpha=0 is not supported; alpha may be arbitrarily small, but not exactly 0.
`lambda_min`	The smallest value for lambda, as a fraction of lambda.max. Default is .001 if the number of observations is larger than the number of covariates and .05 otherwise. A value of lambda_min = 0 is not supported.
`nlambda`	The desired number of lambda values in the sequence to be generated.
`penalty_factor`	A multiplicative factor for the penalty applied to each coefficient. If supplied, penalty_factor must be a numeric vector of length equal to the number of columns of X. The purpose of penalty_factor is to apply differential penalization if some coefficients are thought to be more likely than others to be in the model. In particular, penalty_factor can be 0, in which case the coefficient is always in the model without shrinkage.
`intercept`	Logical: does X contain an intercept column? Defaults to TRUE.