Last updated on 2024-04-18 12:52:41 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.8 | 6.77 | 238.94 | 245.71 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0.8 | 4.78 | 175.77 | 180.55 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.8 | 297.29 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.8 | 307.83 | ERROR | |||
| r-prerel-macos-arm64 | 1.0.8 | 88.00 | OK | |||
| r-prerel-macos-x86_64 | 1.0.8 | 270.00 | OK | |||
| r-prerel-windows-x86_64 | 1.0.8 | 9.00 | 203.00 | 212.00 | OK | |
| r-patched-linux-x86_64 | 1.0.8 | 9.71 | 231.18 | 240.89 | ERROR | |
| r-release-linux-x86_64 | 1.0.8 | 5.98 | 232.58 | 238.56 | ERROR | |
| r-release-macos-arm64 | 1.0.8 | 90.00 | OK | |||
| r-release-macos-x86_64 | 1.0.8 | 225.00 | OK | |||
| r-release-windows-x86_64 | 1.0.8 | 15.00 | 272.00 | 287.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.8 | 90.00 | OK | |||
| r-oldrel-windows-x86_64 | 1.0.8 | 14.00 | 257.00 | 271.00 | OK |
Version: 1.0.8
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘margins’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.0.8
Check: examples
Result: ERROR
Running examples in ‘DeclareDesign-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: declare_estimator
> ### Title: Declare estimator
> ### Aliases: declare_estimator declare_estimators label_estimator
> ### method_handler
>
> ### ** Examples
>
>
> # Setup for examples
> design <-
+ declare_model(
+ N = 500,
+ gender = rbinom(N, 1, 0.5),
+ U = rnorm(N, sd = 0.25),
+ potential_outcomes(Y ~ rbinom(
+ N, 1, prob = pnorm(0.2 * Z + 0.2 * gender + 0.1 * Z * gender + U)
+ ))
+ ) +
+ declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
+ declare_sampling(S = complete_rs(N = N, n = 200)) +
+ declare_assignment(Z = complete_ra(N = N, m = 100)) +
+ declare_measurement(Y = reveal_outcomes(Y ~ Z))
>
> run_design(design)
inquiry estimand
1 ATE 0.138
>
> # default estimator is lm_robust with tidy summary
> design_0 <-
+ design +
+ declare_estimator(Y ~ Z, inquiry = "ATE")
>
> run_design(design_0)
inquiry estimand estimator term estimate std.error statistic p.value
1 ATE 0.082 estimator Z 0.01 0.07025926 0.14233 0.8869641
conf.low conf.high df outcome
1 -0.1285525 0.1485525 198 Y
>
> # Linear regression using lm_robust and tidy summary
> design_1 <-
+ design +
+ declare_estimator(
+ formula = Y ~ Z,
+ .method = lm_robust,
+ .summary = tidy,
+ term = "Z",
+ inquiry = "ATE",
+ label = "lm_no_controls"
+ )
>
> run_design(design_1)
inquiry estimand term estimator estimate std.error statistic p.value
1 ATE 0.106 Z lm_no_controls 0.01 0.06842381 0.146148 0.8839533
conf.low conf.high df outcome
1 -0.124933 0.144933 198 Y
>
> # Use glance summary function to view model fit statistics
> design_2 <-
+ design +
+ declare_estimator(.method = lm_robust,
+ formula = Y ~ Z,
+ .summary = glance)
>
> run_design(design_2)
inquiry estimand estimator r.squared adj.r.squared statistic p.value
1 ATE 0.062 estimator 0.00490049 -0.0001252651 0.9750754 0.3246228
df.residual nobs se_type
1 198 200 HC2
>
> # Use declare_estimator to implement custom answer strategies
> my_estimator <- function(data) {
+ data.frame(estimate = mean(data$Y))
+ }
>
> design_3 <-
+ design +
+ declare_inquiry(Y_bar = mean(Y)) +
+ declare_estimator(handler = label_estimator(my_estimator),
+ label = "mean",
+ inquiry = "Y_bar")
>
> run_design(design_3)
inquiry estimand estimator estimate
1 Y_bar 0.570 mean 0.57
2 ATE 0.136 <NA> NA
>
> # Use `term` to select particular coefficients
> design_4 <-
+ design +
+ declare_inquiry(difference_in_cates = mean(Y_Z_1[gender == 1] - Y_Z_0[gender == 1]) -
+ mean(Y_Z_1[gender == 0] - Y_Z_0[gender == 0])) +
+ declare_estimator(Y ~ Z * gender,
+ term = "Z:gender",
+ inquiry = "difference_in_cates",
+ .method = lm_robust)
>
> run_design(design_4)
inquiry estimand term estimator estimate std.error
1 difference_in_cates 0.1443609 Z:gender estimator 0.1966622 0.1381796
2 ATE 0.1180000 <NA> <NA> NA NA
statistic p.value conf.low conf.high df outcome
1 1.423236 0.1562576 -0.07584754 0.469172 196 Y
2 NA NA NA NA NA <NA>
>
> # Use glm from base R
> design_5 <-
+ design +
+ declare_estimator(Y ~ Z + gender,
+ family = "gaussian",
+ inquiry = "ATE",
+ .method = glm)
>
> run_design(design_5)
inquiry estimand estimator term estimate std.error statistic p.value
1 ATE 0.074 estimator Z 0.07 0.07 1 0.3185372
conf.low conf.high
1 -0.06719748 0.2071975
>
> # If we use logit, we'll need to estimate the average marginal effect with
> # margins::margins. We wrap this up in function we'll pass to model_summary
>
> library(margins) # for margins
Error in library(margins) : there is no package called ‘margins’
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.0.8
Check: examples
Result: ERROR
Running examples in ‘DeclareDesign-Ex.R’ failed
The error most likely occurred in:
> ### Name: declare_estimator
> ### Title: Declare estimator
> ### Aliases: declare_estimator declare_estimators label_estimator
> ### method_handler
>
> ### ** Examples
>
>
> # Setup for examples
> design <-
+ declare_model(
+ N = 500,
+ gender = rbinom(N, 1, 0.5),
+ U = rnorm(N, sd = 0.25),
+ potential_outcomes(Y ~ rbinom(
+ N, 1, prob = pnorm(0.2 * Z + 0.2 * gender + 0.1 * Z * gender + U)
+ ))
+ ) +
+ declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
+ declare_sampling(S = complete_rs(N = N, n = 200)) +
+ declare_assignment(Z = complete_ra(N = N, m = 100)) +
+ declare_measurement(Y = reveal_outcomes(Y ~ Z))
>
> run_design(design)
inquiry estimand
1 ATE 0.138
>
> # default estimator is lm_robust with tidy summary
> design_0 <-
+ design +
+ declare_estimator(Y ~ Z, inquiry = "ATE")
>
> run_design(design_0)
inquiry estimand estimator term estimate std.error statistic p.value
1 ATE 0.082 estimator Z 0.01 0.07025926 0.14233 0.8869641
conf.low conf.high df outcome
1 -0.1285525 0.1485525 198 Y
>
> # Linear regression using lm_robust and tidy summary
> design_1 <-
+ design +
+ declare_estimator(
+ formula = Y ~ Z,
+ .method = lm_robust,
+ .summary = tidy,
+ term = "Z",
+ inquiry = "ATE",
+ label = "lm_no_controls"
+ )
>
> run_design(design_1)
inquiry estimand term estimator estimate std.error statistic p.value
1 ATE 0.106 Z lm_no_controls 0.01 0.06842381 0.146148 0.8839533
conf.low conf.high df outcome
1 -0.124933 0.144933 198 Y
>
> # Use glance summary function to view model fit statistics
> design_2 <-
+ design +
+ declare_estimator(.method = lm_robust,
+ formula = Y ~ Z,
+ .summary = glance)
>
> run_design(design_2)
inquiry estimand estimator r.squared adj.r.squared statistic p.value
1 ATE 0.062 estimator 0.00490049 -0.0001252651 0.9750754 0.3246228
df.residual nobs se_type
1 198 200 HC2
>
> # Use declare_estimator to implement custom answer strategies
> my_estimator <- function(data) {
+ data.frame(estimate = mean(data$Y))
+ }
>
> design_3 <-
+ design +
+ declare_inquiry(Y_bar = mean(Y)) +
+ declare_estimator(handler = label_estimator(my_estimator),
+ label = "mean",
+ inquiry = "Y_bar")
>
> run_design(design_3)
inquiry estimand estimator estimate
1 Y_bar 0.570 mean 0.57
2 ATE 0.136 <NA> NA
>
> # Use `term` to select particular coefficients
> design_4 <-
+ design +
+ declare_inquiry(difference_in_cates = mean(Y_Z_1[gender == 1] - Y_Z_0[gender == 1]) -
+ mean(Y_Z_1[gender == 0] - Y_Z_0[gender == 0])) +
+ declare_estimator(Y ~ Z * gender,
+ term = "Z:gender",
+ inquiry = "difference_in_cates",
+ .method = lm_robust)
>
> run_design(design_4)
inquiry estimand term estimator estimate std.error
1 difference_in_cates 0.1443609 Z:gender estimator 0.1966622 0.1381796
2 ATE 0.1180000 <NA> <NA> NA NA
statistic p.value conf.low conf.high df outcome
1 1.423236 0.1562576 -0.07584754 0.469172 196 Y
2 NA NA NA NA NA <NA>
>
> # Use glm from base R
> design_5 <-
+ design +
+ declare_estimator(Y ~ Z + gender,
+ family = "gaussian",
+ inquiry = "ATE",
+ .method = glm)
>
> run_design(design_5)
inquiry estimand estimator term estimate std.error statistic p.value
1 ATE 0.074 estimator Z 0.07 0.07 1 0.3185372
conf.low conf.high
1 -0.06719748 0.2071975
>
> # If we use logit, we'll need to estimate the average marginal effect with
> # margins::margins. We wrap this up in function we'll pass to model_summary
>
> library(margins) # for margins
Error in library(margins) : there is no package called ‘margins’
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc