DiSCo_iter {DiSCos}  R Documentation 
Estimate DiSCo in a single period
Description
This function implements the DiSCo method for a single time period, as well as the mixture of distributions approach. Its return values contain valuable periodspecific estimation outputs.
Usage
DiSCo_iter(
yy,
df,
evgrid,
id_col.target,
M,
G,
T0,
qmethod = NULL,
qtype = 7,
q_min = 0,
q_max = 1,
simplex = FALSE,
controls.id,
grid.cat,
mixture
)
Arguments
yy 
Integer indicating the current year being processed. 
df 
Data frame or data table containing the distributional data for the target and control units. The data table should contain the following columns:

evgrid 
A vector of grid points on which to evaluate the quantile functions. 
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. 
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. 
T0 
Integer indicating the last pretreatment period starting from 1. 
qmethod 
Character, indicating the method to use for computing the quantiles of the target distribution. The default is NULL, which uses the 
qtype 
Integer, indicating the type of quantile to compute when using 
q_min 
Numeric, minimum quantile to use. Set this together with 
q_max 
Numeric, maximum quantile to use. Set this together with 
simplex 
Logical, indicating whether to use to constrain the optimal weights to the unit simplex. Default is FALSE, which only constrains the weights to sum up to 1 but allows them to be negative. 
controls.id 
List of strings specifying the column names for the control units' identifiers. 
grid.cat 
List, containing the discrete support points for a discrete grid to be used with the mixture of distributions approach. This is useful for constructing synthetic distributions for categorical variables. Default is NULL, which uses a continuous grid based on the other parameters. 
mixture 
Logical, indicating whether to use the mixture of distributions approach instead.
See Section 4.3. in Gunsilius (2023). This approach minimizes the distance between the CDFs
instead of the quantile functions, and is preferred for categorical variables. When working with such variables, one should
also provide a list of support points in the 
Details
This function is part of the DiSCo method, called for each time period. It calculates the optimal weights for the DiSCo method and the mixture of distributions approach for a single time period. The function processes data f or both the target and control units, computes the quantile functions, and evaluates these on a specified grid. The function is designed to be used within the broader context of the DiSCo function, which aggregates results across multiple time periods.
Value
A list with the following elements:

DiSCo_weights
Weights calculated using the DiSCo method. 
mixture

weights
Optimal weights for the mixture approach. 
distance
Value of the objective function for the mixture approach. 
mean
Weighted mixture of the controls' CDFs.


target

cdf
Empirical CDF of the target. Only computed whenmixture=TRUE
. 
grid
Grid on which the quantile and CDF functions were evaluated. 
data
Original data for the target unit. 
quantiles
Quantiles for the target unit, evaluated on the specified grid.


controls

data
Original data for the control units. 
cdf
Empirical CDFs of the control units. Only computed whenmixture=TRUE
. 
quantiles
Quantiles for the control units, evaluated on the specified grid. .


controls.q
Quantiles for the control units, evaluated on the specified grid.