vsflcm: Implements group lasso with fpc for the linear functional...

In Hongming-Pu/vsFlcm: Variable Selection(Group Lasso) for the Functional Linear Concurrent Model

Description

Implements group lasso with fpc for the linear functional concurrent model

Usage

vsflcm(formula, data = NULL, id.time = NULL, intercept = TRUE,
  id.sub = NULL, t.min = NULL, t.max = NULL, K = 5,
  spline.fun = "B-spline", lambda = 0.5, K0 = 2, lam.nuc = 10,
  method.obj = c("nuclear", "nonconvex"), delta = 0.1,
  method.optim = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"),
  times = 1, fpc.on = TRUE, spline.fun2d = NULL, lam.smo = 0,
  maxit = 100)

Arguments

formula	formula for the regression. should have form y ~ V1 + V2 + ... + Vk. Note: don't contain id.time
data	data frame
id.time	the variable that represents time
intercept	logical, whether use intercept function, if FALSE, the predictors and responses won't be normalized automatically
id.sub	variable giving subject ID
t.min	minimum value to be evaluated on the time domain. if 'NULL', taken to be minium observed value.
t.max	maximum value to be evaluated on the time domain. if 'NULL', taken to be minium observed value.
K	number of spline basis functions for coefficients and fpc
spline.fun	spline basis functions. If not default, it should be a function taking a vector(corresponding to time) belonging to [0,1] as input and returning a length(vector)*K spline matrix where each row corresponds to a time point in the vector and each column corresponds to a spline basis function
lambda	Constant to multiply the L1 penalty term
K0	number of FPCs
lam.nuc	Only Useful when method.obj='nuclear'. penalty factor for the nuclear norm
method.obj	the methodology chosen. Should be 'nuclear' or 'nonconvex'. Typically, we recommend 'nuclear'
delta	Only useful when method.obj='nonconvex'. When the square of the norm of the coefficient is smaller than delta, L1 penalty will smoothly turned into L2 penalty
method.optim	the optimization method to be used. For details and other options, please refer to optim
times	Only useful when method.obj='nonconvex'. the algorithm randomly chooses times initial points to optimize and select the best
fpc.on	Logical. Whether fpc will be used
spline.fun2d	second derivative of spline.fun, if spline.fun is default then the second derivative will be computed automaticly and you don't need to provide values for spline.fun2d. Otherwise you need to provide a function with input and output dimensions in accordance with spline.fun if you want to model the smoothness.
lam.smo	penalty factor for the second derivative of basis funtion. Only useful when spline.fun is default or spline.fun2d is not NULL
maxit	maxit of optim

Author(s)

Hongming Pu [e-mail]

Hongming-Pu/vsFlcm documentation built on May 28, 2019, 12:41 p.m.