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fix seed in examples and improve unit testing
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,123 +1,124 @@ | ||
| #' @title Aggregate Group-Time Average Treatment Effects | ||
| #' | ||
| #' @description A function to take group-time average treatment effects | ||
| #' and aggregate them into a smaller number of parameters. There are | ||
| #' several possible aggregations including "simple", "dynamic", "group", | ||
| #' and "calendar." | ||
| #' | ||
| #' @param MP an MP object (i.e., the results of the [att_gt()] method) | ||
| #' @param type Which type of aggregated treatment effect parameter to compute. | ||
| #' One option is "simple" (this just computes a weighted average of all | ||
| #' group-time average treatment effects with weights proportional to group | ||
| #' size). Other options are "dynamic" (this computes average effects across | ||
| #' different lengths of exposure to the treatment and is similar to an | ||
| #' "event study"; here the overall effect averages the effect of the | ||
| #' treatment across all positive lengths of exposure); "group" (this | ||
| #' is the default option and | ||
| #' computes average treatment effects across different groups; here | ||
| #' the overall effect averages the effect across different groups); and | ||
| #' "calendar" (this computes average treatment effects across different | ||
| #' time periods; here the overall effect averages the effect across each | ||
| #' time period). | ||
| #' @param balance_e If set (and if one computes dynamic effects), it balances | ||
| #' the sample with respect to event time. For example, if `balance.e=2`, | ||
| #' `aggte` will drop groups that are not exposed to treatment for | ||
| #' at least three periods. (the initial period when `e=0` as well as the | ||
| #' next two periods when `e=1` and the `e=2`). This ensures that | ||
| #' the composition of groups does not change when event time changes. | ||
| #' @param min_e For event studies, this is the smallest event time to compute | ||
| #' dynamic effects for. By default, `min_e = -Inf` so that effects at | ||
| #' all lengths of exposure are computed. | ||
| #' @param max_e For event studies, this is the largest event time to compute | ||
| #' dynamic effects for. By default, `max_e = Inf` so that effects at | ||
| #' all lengths of exposure are computed. | ||
| #' @param na.rm Logical value if we are to remove missing Values from analyses. Defaults is FALSE. | ||
| #' @param bstrap Boolean for whether or not to compute standard errors using | ||
| #' the multiplier bootstrap. If standard errors are clustered, then one | ||
| #' must set `bstrap=TRUE`. Default is value set in the MP object. If bstrap is `FALSE`, then analytical | ||
| #' standard errors are reported. | ||
| #' @param biters The number of bootstrap iterations to use. The default is the value set in the MP object, | ||
| #' and this is only applicable if `bstrap=TRUE`. | ||
| #' | ||
| #' @param 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 `1-alp`. In order to compute uniform confidence | ||
| #' bands, `bstrap` must also be set to `TRUE`. The default is | ||
| #' the value set in the MP object | ||
| #' @param alp the significance level, default is value set in the MP object. | ||
| #' @param clustervars A vector of variables to cluster on. At most, there | ||
| #' can be two variables (otherwise will throw an error) and one of these | ||
| #' must be the same as idname which allows for clustering at the individual | ||
| #' level. Default is the variables set in the MP object | ||
|
|
||
| #' | ||
| #' @return An [`AGGTEobj`] object that holds the results from the | ||
| #' aggregation | ||
| #' | ||
| #' @section Examples: | ||
| #' | ||
| # | ||
| #' Initial ATT(g,t) estimates from [att_gt()] | ||
| #' | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' data(mpdta) | ||
| #' out <- att_gt(yname="lemp", | ||
| #' tname="year", | ||
| #' idname="countyreal", | ||
| #' gname="first.treat", | ||
| #' xformla=NULL, | ||
| #' data=mpdta) | ||
| #' ``` | ||
| #' | ||
| #' You can aggregate the ATT(g,t) in many ways. | ||
| #' | ||
| #' **Overall ATT:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "simple") | ||
| #' ``` | ||
| #' | ||
| #' **Dynamic ATT (Event-Study):** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "dynamic") | ||
| #' ``` | ||
| #' | ||
| #' **ATT for each group:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "group") | ||
| #' ``` | ||
| #' | ||
| #' **ATT for each calendar year:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "calendar") | ||
| #' ``` | ||
| #' | ||
| #' | ||
| #' @export | ||
| aggte <- function(MP, | ||
| type = "group", | ||
| balance_e = NULL, | ||
| min_e = -Inf, | ||
| max_e = Inf, | ||
| na.rm = FALSE, | ||
| bstrap = NULL, | ||
| biters = NULL, | ||
| cband = NULL, | ||
| alp = NULL, | ||
| clustervars = NULL | ||
| ) { | ||
|
|
||
| call <- match.call() | ||
|
|
||
| compute.aggte(MP = MP, | ||
| type = type, | ||
| balance_e = balance_e, | ||
| min_e = min_e, | ||
| max_e = max_e, | ||
| na.rm = na.rm, | ||
| bstrap = bstrap, | ||
| biters = biters, | ||
| cband = cband, | ||
| alp = alp, | ||
| clustervars = clustervars, | ||
| call = call) | ||
| } | ||
| #' @title Aggregate Group-Time Average Treatment Effects | ||
| #' | ||
| #' @description A function to take group-time average treatment effects | ||
| #' and aggregate them into a smaller number of parameters. There are | ||
| #' several possible aggregations including "simple", "dynamic", "group", | ||
| #' and "calendar." | ||
| #' | ||
| #' @param MP an MP object (i.e., the results of the [att_gt()] method) | ||
| #' @param type Which type of aggregated treatment effect parameter to compute. | ||
| #' One option is "simple" (this just computes a weighted average of all | ||
| #' group-time average treatment effects with weights proportional to group | ||
| #' size). Other options are "dynamic" (this computes average effects across | ||
| #' different lengths of exposure to the treatment and is similar to an | ||
| #' "event study"; here the overall effect averages the effect of the | ||
| #' treatment across all positive lengths of exposure); "group" (this | ||
| #' is the default option and | ||
| #' computes average treatment effects across different groups; here | ||
| #' the overall effect averages the effect across different groups); and | ||
| #' "calendar" (this computes average treatment effects across different | ||
| #' time periods; here the overall effect averages the effect across each | ||
| #' time period). | ||
| #' @param balance_e If set (and if one computes dynamic effects), it balances | ||
| #' the sample with respect to event time. For example, if `balance.e=2`, | ||
| #' `aggte` will drop groups that are not exposed to treatment for | ||
| #' at least three periods. (the initial period when `e=0` as well as the | ||
| #' next two periods when `e=1` and the `e=2`). This ensures that | ||
| #' the composition of groups does not change when event time changes. | ||
| #' @param min_e For event studies, this is the smallest event time to compute | ||
| #' dynamic effects for. By default, `min_e = -Inf` so that effects at | ||
| #' all lengths of exposure are computed. | ||
| #' @param max_e For event studies, this is the largest event time to compute | ||
| #' dynamic effects for. By default, `max_e = Inf` so that effects at | ||
| #' all lengths of exposure are computed. | ||
| #' @param na.rm Logical value if we are to remove missing Values from analyses. Defaults is FALSE. | ||
| #' @param bstrap Boolean for whether or not to compute standard errors using | ||
| #' the multiplier bootstrap. If standard errors are clustered, then one | ||
| #' must set `bstrap=TRUE`. Default is value set in the MP object. If bstrap is `FALSE`, then analytical | ||
| #' standard errors are reported. | ||
| #' @param biters The number of bootstrap iterations to use. The default is the value set in the MP object, | ||
| #' and this is only applicable if `bstrap=TRUE`. | ||
| #' | ||
| #' @param 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 `1-alp`. In order to compute uniform confidence | ||
| #' bands, `bstrap` must also be set to `TRUE`. The default is | ||
| #' the value set in the MP object | ||
| #' @param alp the significance level, default is value set in the MP object. | ||
| #' @param clustervars A vector of variables to cluster on. At most, there | ||
| #' can be two variables (otherwise will throw an error) and one of these | ||
| #' must be the same as idname which allows for clustering at the individual | ||
| #' level. Default is the variables set in the MP object | ||
|
|
||
| #' | ||
| #' @return An [`AGGTEobj`] object that holds the results from the | ||
| #' aggregation | ||
| #' | ||
| #' @section Examples: | ||
| #' | ||
| # | ||
| #' Initial ATT(g,t) estimates from [att_gt()] | ||
| #' | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' data(mpdta) | ||
| #' set.seed(09152024) | ||
| #' out <- att_gt(yname="lemp", | ||
| #' tname="year", | ||
| #' idname="countyreal", | ||
| #' gname="first.treat", | ||
| #' xformla=NULL, | ||
| #' data=mpdta) | ||
| #' ``` | ||
| #' | ||
| #' You can aggregate the ATT(g,t) in many ways. | ||
| #' | ||
| #' **Overall ATT:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "simple") | ||
| #' ``` | ||
| #' | ||
| #' **Dynamic ATT (Event-Study):** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "dynamic") | ||
| #' ``` | ||
| #' | ||
| #' **ATT for each group:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "group") | ||
| #' ``` | ||
| #' | ||
| #' **ATT for each calendar year:** | ||
| #' ```{r, comment = "#>", collapse = TRUE} | ||
| #' aggte(out, type = "calendar") | ||
| #' ``` | ||
| #' | ||
| #' | ||
| #' @export | ||
| aggte <- function(MP, | ||
| type = "group", | ||
| balance_e = NULL, | ||
| min_e = -Inf, | ||
| max_e = Inf, | ||
| na.rm = FALSE, | ||
| bstrap = NULL, | ||
| biters = NULL, | ||
| cband = NULL, | ||
| alp = NULL, | ||
| clustervars = NULL | ||
| ) { | ||
|
|
||
| call <- match.call() | ||
|
|
||
| compute.aggte(MP = MP, | ||
| type = type, | ||
| balance_e = balance_e, | ||
| min_e = min_e, | ||
| max_e = max_e, | ||
| na.rm = na.rm, | ||
| bstrap = bstrap, | ||
| biters = biters, | ||
| cband = cband, | ||
| alp = alp, | ||
| clustervars = clustervars, | ||
| call = call) | ||
| } |
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