checks: checks Carry out checks on the inputs
In DiSCos: Distributional Synthetic Controls Estimation

checks

R Documentation

checks Carry out checks on the inputs

Description

checks Carry out checks on the inputs

Usage

checks(
  df,
  id_col.target,
  t0,
  M,
  G,
  num.cores,
  permutation,
  q_min,
  q_max,
  CI,
  boots,
  cl,
  graph,
  qmethod,
  seed
)

Arguments

`df`	Data frame or data table containing the distributional data for the target and control units. The data table should contain the following columns: `y_col` A numeric vector containing the outcome variable for each unit. Units can be individuals, states, etc., but they should be nested within a larger unit (e.g. individuals or counties within a state) `id_col` A numeric vector containing the aggregate IDs of the units. This could be, for example, the state if the units are counties or individuals `time_col` A vector containing the time period of the observation for each unit. This should be a monotonically increasing integer.
`id_col.target`	Variable indicating the name of the target unit, as specified in the id_col column of the data table. This variable can be any type, as long as it is the same type as the id_col column of the data table.
`t0`	Integer indicating period of treatment.
`M`	Integer indicating the number of control quantiles to use in the DiSCo method. Default is 1000.
`G`	Integer indicating the number of grid points for the grid on which the estimated functions are evaluated. Default is 1000.
`num.cores`	Integer, number of cores to use for parallel computation. Default is 1. If the `permutation` or `CI` arguments are set to TRUE, this can be slow and it is recommended to set this to 4 or more, if possible. If you get an error in "all cores" or similar, try setting num.cores=1 to see the precise error value.
`permutation`	logical, whether to use permutation or not
`q_min`	Numeric, minimum quantile to use. Set this together with `q_max` to restrict the range of quantiles used to construct the synthetic control. Default is 0 (all quantiles). Currently NOT implemented for the `mixture` approach.
`q_max`	Numeric, maximum quantile to use. Set this together with `q_min` to restrict the range of quantiles used to construct the synthetic control. Default is 1 (all quantiles). Currently NOT implemented for the `mixture` approach.
`CI`	Logical, indicating whether to compute confidence intervals for the counterfactual quantiles. Default is FALSE. The confidence intervals are computed using the bootstrap procedure described in \insertCitevandijcke2024rto;textualDiSCos.
`boots`	Integer, number of bootstrap samples to use for computing confidence intervals. Default is 500.
`cl`	Numeric, confidence level for the (two-sided) confidence intervals.
`graph`	Logical, indicating whether to plot the permutation graph as in Figure 3 of the paper. Default is FALSE.
`qmethod`	Character, indicating the method to use for computing the quantiles of the target distribution. The default is NULL, which uses the `quantile` function from the stats package. Other options are "`qkden`" (based on smoothed kernel density function) and "`extreme`" (based on parametric extreme value distributions). Both are substantially slower than the default method but may be useful for fat-tailed distributions with few data points at the upper quantiles. Alternatively, one could use the q_max option to restrict the range of quantiles used.
`seed`	Integer, seed for the random number generator. This needs to be set explicitly in the function call, since it will invoke `RNGkind` which will set the seed for each core when using parallel processes. Default is NULL, which does not set a seed.