Description Usage Arguments Author(s)
Implements group lasso with fpc for the linear functional concurrent model
1 2 3 4 5 6 7 | 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)
|
formula |
formula for the regression. should have form |
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 |
lambda |
Constant to multiply the L1 penalty term |
K0 |
number of FPCs |
lam.nuc |
Only Useful when |
method.obj |
the methodology chosen. Should be 'nuclear' or 'nonconvex'. Typically, we recommend 'nuclear' |
delta |
Only useful when |
method.optim |
the optimization method to be used. For details and other options, please refer to |
times |
Only useful when |
fpc.on |
Logical. Whether fpc will be used |
spline.fun2d |
second derivative of |
lam.smo |
penalty factor for the second derivative of basis funtion. Only useful when |
maxit |
|
Hongming Pu phmhappier@163.com
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