checks Carry out checks on the inputs
checks(
df,
id_col.target,
t0,
M,
G,
num.cores,
permutation,
q_min,
q_max,
CI,
boots,
cl,
graph,
qmethod,
seed
)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.
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.
Integer indicating period of treatment.
Integer indicating the number of control quantiles to use in the DiSCo method. Default is 1000.
Integer indicating the number of grid points for the grid on which the estimated functions are evaluated. Default is 1000.
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.
logical, whether to use permutation or not
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.
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.
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 vandijcke2024rto;textualDiSCos.
Integer, number of bootstrap samples to use for computing confidence intervals. Default is 500.
Numeric, confidence level for the (two-sided) confidence intervals.
Logical, indicating whether to plot the permutation graph as in Figure 3 of the paper. Default is FALSE.
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.
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.