fit.csim.cv: A Constrained single index model (a wrapper function for...
In syhyunpark/csim: Constrained single index models (CSIM)

Description Usage Arguments Value Author(s) See Also

fit.csim.cv performs a 5 fold cross validation to choose the sparsity tuning parameter, and returns the model with the optimized tuning parameter. If type="AIC", then the function returns the model with the lowest value of AIC.

fit.csim.cv(y, Tr, X, ortho.constr = TRUE, sparse = TRUE,
  type = "AIC", seed = 1234, lam.by = 0.03, n.lam = 100,
  n.max = 10, eps = 10^-4, it.max = 50, trace = F,
  nbasis.t = NULL, rho.grid = c(0, 0.25, 0.5), eff.aug = FALSE,
  X.aug = NULL, linear.link = FALSE, coef.ini = NULL, i.fx = NULL,
  mc.ini = FALSE, unit.norm = FALSE)

`y`	treatment outcomes, n-by-1 vector
`Tr`	treatment indicators, n-by-1 vector; each element represents one of the K available treatment options
`X`	a pretreatment covarate matrix, n-by-p matrix
`ortho.constr`	the constraint that separates the interaction effects from the main effect (without this, the interaction effect can be confounded by the main effect); the default is `TRUE`.
`sparse`	if `TRUE`, apply L1 regularization when estimating the single index coefficients; the default is `TRUE`.
`type`	when `sparse=TRUE`, can choose bewteen `"CV"` and `"BIC"`, for the sparsity tuninig parameter selection.
`seed`	when `type="CV"`, randomization seed for cross validation.
`lam.by`	a value specifying the grid of the sparsity tuning parameters [1, 1+lam.by, ... 1 + n.lam*lam.by].
`n.lam`	a value specifying the grid of the sparsity tuning parameters [1, 1+lam.by, ... 1 + n.lam*lam.by].
`n.max`	a maximum number of nonzero (active) coeffients in CSIM.
`eps`	a value specifying the converge criterion of algorithm.
`it.max`	an integer value specifying the maximum number of iterations for each coordinate.
`trace`	if `TRUE`, show the trace of the fitting procedure; the default is `FALSE`.
`nbasis.t`	a length K+1 vector; each element specifies the number of B-spline basis funtions for approximating the treatment-specific link function; the last element is for the "main effect" link function; the default is `nbasis.t=NULL`, and will be determined depending on the sample size.
`rho.grid`	a grid vector of (ridge-type) smoothing parameters for approximating the link functions.
`eff.aug`	if `TRUE`, perform efficiency augmentation (using a L1 regularized linear regression for the main effects of X); the default is `FALSE`.
`X.aug`	a design matrix to be used for efficinecy augmentation; the default is `NULL`.
`linear.link`	if `TRUE`, restrict the link functions to be linear functions; the default is `FALSE`.
`coef.ini`	an initial solution for alpha.coef.
`i.fx`	a value of the index to be fixed throughout the estimation for model identifiability; the default is `NULL`, hence it is estimated.
`mc.ini`	if `TRUE`, use an estimate obtained from the modified covariate method as an initial solution; this is only applicable for K=2 case; the default is `FALSE`.
`unit.norm`	if `TRUE`, set the estimated coefficient vector to have a unit norm for model identifiability; the default is to set the component with the largest magnitude to be 1.

a list of information of the fitted model including

`coef.obj`	the fitted `fit.csim` object with the optimized tuning parameter.
`coef.opt`	the estimated single index coefficients, given the optimized value of the tuning parameter `lam`.
`coef.pst.opt`	the re-fitted single index coefficients, given the optimized value of the tuning parameter `lam`; the single index coefficients is re-fitted without regularization given a set of selected covariates implied by the nonzero components of `coef`, and store in `coef.pst`.
`eta.coef`	the estimated main effect coefficients if `eff.aug=TRUE`.
`intercept.y`	a list of the treatment-specific intercepts of the fitted model
`center.X`	a vector whose elements are the means of the pretreatment covariates.
`scale.X`	a vector whose elements are the standard deviations of the pretreatment covariates.