compCL: Fits regularization paths for longitudinal compositional data...
In jiji6454/Rpac_compReg: Compositional Data Regression

Description Usage Arguments Value Examples

Fits regularization paths for longitudinal compositional data with lasso penalty at a sequence of regularization parameters lambda.

compCL(y, Z, Zc = NULL, intercept = TRUE, pf = rep(1, times = p),
  lam = NULL, nlam = 100, lambda.factor = ifelse(n < p, 0.05, 0.001),
  dfmax = p, pfmax = min(dfmax * 1.5, p), u = 1, mu_ratio = 1.01,
  tol = 1e-10, outer_maxiter = 1e+08, outer_eps = 1e-08,
  inner_maxiter = 1000, inner_eps = 1e-06)

`y`	a vector of response variable with length n.
`Z`	a np* matrix after taking log transformation on compositional data.
`Zc`	a design matrix of other covariates considered. Default is `NULL`.
`intercept`	Whether to include intercept in the model. Default is TRUE.
`pf`	penalty factor, a vector in length of p. Separate penalty weights can be applied to each coefficience β for composition variates to allow differential shrinkage. Can be 0 for some β's, which implies no shrinkage, and results in that composition always being included in the model. Default value for each entry is the 1.
`lam`	a user supplied lambda sequence. Typically, by leaving this option unspecified users can have the program compute its own `lam` sequence based on `nlam` and `lambda.factor` If `lam` is provided but a scaler, `lam` sequence is also created starting from `lam`. Supplying a value of lambda overrides this. It is better to supply a decreasing sequence of lambda values, if not, the program will sort user-defined `lambda` sequence in decreasing order automatically.
`nlam`	the length of `lam` sequence - default is 100.
`lambda.factor`	the factor for getting the minimal lambda in `lam` sequence, where `min(lam)` = `lambda.factor` * `max(lam)`. `max(lam)` is the smallest value of `lam` for which all penalized group are zero's. The default depends on the relationship between n and p1 If n >= p1 the default is `0.001`, close to zero. If n < p1, the default is `0.05`. A very small value of `lambda.factor` will lead to a saturated fit. It takes no effect if there is user-defined lambda sequence.
`dfmax`	limit the maximum number of groups in the model. Useful for very large p, if a partial path is desired - default is p.
`pfmax`	limit the maximum number of groups ever to be nonzero. For example once a group enters the model along the path, no matter how many times it exits or re-enters model through the path, it will be counted only once. Default is `min(dfmax*1.5, p)`.
`u`	`u` is the inital value for penalty parameter of augmented Lanrange method. Default value is 1.
`mu_ratio`	`mu_ratio` is the increasing ratio for `u` - default value is 1.01. Inital values for scaled Lagrange multipliers are set as 0's. If `mu_ratio` < 1, there is no linear constraints included. Group lasso coefficients are estimated.
`tol`	tolerance for vectors beta'ss to be considered as none zero's. For example, coefficient β_j for group j, if max(abs(β_j)) < `tol`, set β_j as 0's. Default value is 0.
`outer_maxiter`	`outer_maxiter` is the maximun munber of loops allowed for Augmented Lanrange method; and `outer_eps` is the convergence termination tolerance.
`outer_eps`	`outer_maxiter` is the maximun munber of loops allowed for Augmented Lanrange method; and `outer_eps` is the convergence termination tolerance.
`inner_maxiter`	`inner_maxiter` is the maximun munber of loops allowed for blockwise-GMD; and `inner_eps` is the convergence termination tolerance.
`inner_eps`	`inner_maxiter` is the maximun munber of loops allowed for blockwise-GMD; and `inner_eps` is the convergence termination tolerance.

An object with S3 calss compCL

`beta`	a matrix of coefficients for `cbind{Z, Zc, 1_n}`, with `nlam` rows.
`lam`	the actual sequence of `lam` values used.
`df`	the number of non-zero β's in estimated coefficients for `Z` at each value of `lam`
`npass`	total iteration conducted in computation.
`error`	error message for path at each each value of `lam`. If 0, no error occurs.
`call`	the call that produced this object.
`dim`	dimension of coefficient matrix

p = 30
n = 50
beta = c(1, -0.8, 0.6, 0, 0, -1.5, -0.5, 1.2)
beta = c( beta, rep(0, times = p - length(beta)) )
Comp_data = comp_simulation(n = n, p = p, rho = 0.2, sigma = 0.5, gamma  = 0.5, add.on = 1:5,
                            beta = beta, intercept = FALSE)
Comp_data$Zc
m1 <- compCL(y = Comp_data$y, Z = Comp_data$X.comp, Zc = Comp_data$Zc,
             intercept = Comp_data$intercept,
             pf = rep(1, times = p),
             lam = NULL, nlam = 100,lambda.factor = ifelse(n < p, 0.05, 0.001),
             dfmax = 20, #pfmax = min(dfmax * 1.5, p),
             mu_ratio = 1, tol = 1e-10,
             outer_maxiter = 1e8, outer_eps = 1e-10,
             inner_maxiter = 1e3, inner_eps = 1e-6)
drop(m1$lam)
coef(m1, s = c(1, 0.5, 0.1))
#coef(m1)
Znew <- Comp_data$Z[1:5,]
print(predict(m1, Znew = Znew[1:5, ], s = m1$lam[15:20]))