fit.mc: Modified covariate method with efficiency augmentation...
In syhyunpark/csim: Constrained single index models (CSIM)

Description Usage Arguments Value See Also

mc is a function for fitting the modified covariate (linear) model of Tian et al. (2015) to estimate the treatment-by-covariates interactions, with efficiency augmentation. CSIM is a semiparametric generalization of the modified covariate model for modeling possilbly nonlinear interaction effects.

1 2	fit.mc(y, Tr, X, eff.aug = TRUE, X.aug = NULL, use.lasso = TRUE, n.folds = 20)

`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
`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`.
`use.lasso`	if `TRUE`, estimate the treatment-specific linear regression coefficients via the Lasso, with the tuning paramters selected by `n.folds` cross-validation for prediction errors; the default is `FALSE`.
`n.folds`	an integer value specifying the number of folds to be used in cross-validation.

a list of information of the fitted model including

`alpha.coef`	a vector of the estimated interaction effect (contrast) coefficients.
`eta.coef`	a vector of 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.
`MSE`	the mean squared error criterion value.