DSC_CI: Confidence Interval in the DSC Method
In hesyserena/DSC: DSC - Distributional Synthetic Controls

Description Usage Arguments Value Examples

View source: R/DSC_CI.R

Function for computing the confidence interval (at a specific period) in the DSC method

DSC_CI(
  df_control,
  df_target,
  M = 100,
  solver = "SCS",
  w_t = 0,
  cl = 0.99,
  num.redraws = 500,
  evgrid = seq(from = 0, to = 1, length.out = 1001),
  graph = TRUE,
  y_name = "y",
  x_name = "x"
)

`df_control`	a list or matrix: in list form, each list element contains a vector of observations for the given control unit; in matrix form, each column corresponds to one unit and each row is one observation.
`df_target`	a vector of observations.
`M`	an integer specifying the number of draws from the uniform distribution for approximating the integral.
`solver`	a solver for the optimization problem; see `installed_solvers` in CVXR for more options.
`w_t`	0 or a vector. By default, i.e. 0, `DSC_CI` calculates the confidence interval of a pre-treatment period, weights of control units are not needed to be specified. To calculate the confidence interval of a post-treatment period, a vector of optimal weights (pre-calculated using pre-treatment observations) is needed.
`cl`	a float specifying the confidence level.
`num.redraws`	an integer specifying the number of redraws used in the bootstrap approach.
`evgrid`	a vector of gridpoints used to evaluate quantile functions.
`graph`	`TRUE/FALSE`, indicating whether to output a plot of the confidence interval.
`y_name`	a string for the title of the y-axis.
`x_name`	a string for the title of the x-axis.

DSC_CI returns a list containing the following components:

`CI.u`	a vector of the upper bound.
`CI.l`	a vector of the lower bound.

#simulated data from Gaussian mixture
#ex_gmm() calls the simulated data
#detail can be found by ??ex_gmm
DSC_CI(df_control=ex_gmm()$control, df_target=ex_gmm()$target, M=100, solver="SCS", w_t=0, cl=0.99, num.redraws=500, evgrid = seq(from=0, to=1, length.out=1001),graph=TRUE, y_name='y', x_name='x')