compute_aggregation {triplediff} | R Documentation |
Compute Aggregated Treatment Effect Parameters
Description
Does the heavy lifting on computing aggregated group-time average treatment effects
Usage
compute_aggregation(
ddd_obj,
type = "simple",
cluster = NULL,
balance_e = NULL,
min_e = -Inf,
max_e = Inf,
na.rm = FALSE,
boot = FALSE,
nboot = NULL,
cband = NULL,
alpha = 0.05
)
Arguments
ddd_obj |
a ddd object (i.e., the results of the |
type |
Which type of aggregated treatment effect parameter to compute.
|
cluster |
The name of the variable to be used for clustering. The maximum number of cluster variables is 1. Default is |
balance_e |
If set (and if one computes event study), it balances
the sample with respect to event time. For example, if |
min_e |
For event studies, this is the smallest event time to compute
dynamic effects for. By default, |
max_e |
For event studies, this is the largest event time to compute
dynamic effects for. By default, |
na.rm |
Logical value if we are to remove missing Values from analyses. Defaults is FALSE. |
boot |
Boolean for whether or not to compute standard errors using
the multiplier bootstrap. If standard errors are clustered, then one
must set |
nboot |
The number of bootstrap iterations to use. The default is the value set in the ddd object,
and this is only applicable if |
cband |
Boolean for whether or not to compute a uniform confidence
band that covers all of the group-time average treatment effects
with fixed probability |
alpha |
The level of confidence for the confidence intervals. The default is 0.05. Otherwise, it will use the value set in the ddd object. |
Value
Aggregation object (list) of class agg_ddd