Get estimates, inquiries, assignment vectors, or samples from a design given data
Source:R/get_functions.R
get_functions.RdGet estimates, inquiries, assignment vectors, or samples from a design given data
get_estimates(design, data = NULL, start = 1, end = length(design))Arguments
- design
A design object, typically created using the + operator
- data
A data.frame object with sufficient information to get the data, estimates, inquiries, an assignment vector, or a sample.
- start
(Defaults to 1) a scalar indicating which step in the design to begin with. By default all data steps are drawn, from step 1 to the last step of the design.
- end
(Defaults to
length(design)) a scalar indicating which step in the design to finish with.
Examples
design <-
declare_model(
N = 100,
U = rnorm(N),
potential_outcomes(Y ~ Z + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N, n = 75)) +
declare_assignment(Z = complete_ra(N, m = 50)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
dat <- draw_data(design)
draw_data(design, data = dat, start = 2)
#> ID U Y_Z_0 Y_Z_1 S Z Y
#> 1 001 0.94562943 0.94562943 1.94562943 1 0 0.94562943
#> 2 002 -0.56851580 -0.56851580 0.43148420 1 1 0.43148420
#> 3 003 -0.12330626 -0.12330626 0.87669374 1 1 0.87669374
#> 4 004 -0.12814218 -0.12814218 0.87185782 1 1 0.87185782
#> 5 005 -0.28093671 -0.28093671 0.71906329 1 1 0.71906329
#> 6 006 0.87587350 0.87587350 1.87587350 1 0 0.87587350
#> 7 007 0.12999647 0.12999647 1.12999647 1 1 1.12999647
#> 8 008 0.30948733 0.30948733 1.30948733 1 1 1.30948733
#> 9 010 -0.44583190 -0.44583190 0.55416810 1 1 0.55416810
#> 10 011 -0.14106492 -0.14106492 0.85893508 1 0 -0.14106492
#> 11 012 -2.28125698 -2.28125698 -1.28125698 1 1 -1.28125698
#> 12 013 0.38642423 0.38642423 1.38642423 1 1 1.38642423
#> 13 015 -1.93806411 -1.93806411 -0.93806411 1 1 -0.93806411
#> 14 016 -0.69870050 -0.69870050 0.30129950 1 1 0.30129950
#> 15 018 -0.04906215 -0.04906215 0.95093785 1 0 -0.04906215
#> 16 019 1.53137897 1.53137897 2.53137897 1 1 2.53137897
#> 17 021 -0.58363743 -0.58363743 0.41636257 1 1 0.41636257
#> 18 023 1.06646542 1.06646542 2.06646542 1 1 2.06646542
#> 19 024 -0.97577660 -0.97577660 0.02422340 1 1 0.02422340
#> 20 025 -1.01083183 -1.01083183 -0.01083183 1 1 -0.01083183
#> 21 026 -1.29972186 -1.29972186 -0.29972186 1 1 -0.29972186
#> 22 028 -1.19740664 -1.19740664 -0.19740664 1 0 -1.19740664
#> 23 030 -1.43753589 -1.43753589 -0.43753589 1 0 -1.43753589
#> 24 032 0.01364331 0.01364331 1.01364331 1 1 1.01364331
#> 25 033 -1.91067022 -1.91067022 -0.91067022 1 0 -1.91067022
#> 26 035 -0.48985099 -0.48985099 0.51014901 1 1 0.51014901
#> 27 036 0.44234093 0.44234093 1.44234093 1 1 1.44234093
#> 28 038 -1.68633028 -1.68633028 -0.68633028 1 0 -1.68633028
#> 29 039 -2.16358581 -2.16358581 -1.16358581 1 0 -2.16358581
#> 30 041 0.26241323 0.26241323 1.26241323 1 1 1.26241323
#> 31 042 1.84437511 1.84437511 2.84437511 1 1 2.84437511
#> 32 043 -2.47534728 -2.47534728 -1.47534728 1 0 -2.47534728
#> 33 044 -0.68735283 -0.68735283 0.31264717 1 0 -0.68735283
#> 34 045 -0.44285711 -0.44285711 0.55714289 1 1 0.55714289
#> 35 046 2.45387378 2.45387378 3.45387378 1 1 3.45387378
#> 36 047 -1.48795178 -1.48795178 -0.48795178 1 1 -0.48795178
#> 37 048 -0.78527243 -0.78527243 0.21472757 1 1 0.21472757
#> 38 049 0.05395408 0.05395408 1.05395408 1 0 0.05395408
#> 39 051 0.95892093 0.95892093 1.95892093 1 0 0.95892093
#> 40 054 -0.86143675 -0.86143675 0.13856325 1 1 0.13856325
#> 41 055 0.47508450 0.47508450 1.47508450 1 1 1.47508450
#> 42 057 -1.18066157 -1.18066157 -0.18066157 1 1 -0.18066157
#> 43 058 -0.58613612 -0.58613612 0.41386388 1 1 0.41386388
#> 44 059 0.08472062 0.08472062 1.08472062 1 1 1.08472062
#> 45 060 1.05731264 1.05731264 2.05731264 1 1 2.05731264
#> 46 061 -0.81324435 -0.81324435 0.18675565 1 0 -0.81324435
#> 47 063 1.43409212 1.43409212 2.43409212 1 1 2.43409212
#> 48 065 0.47050078 0.47050078 1.47050078 1 1 1.47050078
#> 49 066 1.10809924 1.10809924 2.10809924 1 1 2.10809924
#> 50 068 -0.55411463 -0.55411463 0.44588537 1 1 0.44588537
#> 51 069 -0.90599266 -0.90599266 0.09400734 1 0 -0.90599266
#> 52 070 0.27220105 0.27220105 1.27220105 1 1 1.27220105
#> 53 071 -0.53867014 -0.53867014 0.46132986 1 1 0.46132986
#> 54 072 0.01809364 0.01809364 1.01809364 1 1 1.01809364
#> 55 073 -0.68065585 -0.68065585 0.31934415 1 1 0.31934415
#> 56 074 -0.88351718 -0.88351718 0.11648282 1 1 0.11648282
#> 57 075 1.41977248 1.41977248 2.41977248 1 1 2.41977248
#> 58 077 0.90638178 0.90638178 1.90638178 1 1 1.90638178
#> 59 078 1.33567074 1.33567074 2.33567074 1 0 1.33567074
#> 60 079 -0.28253191 -0.28253191 0.71746809 1 1 0.71746809
#> 61 080 0.83229523 0.83229523 1.83229523 1 0 0.83229523
#> 62 081 1.05937523 1.05937523 2.05937523 1 1 2.05937523
#> 63 082 0.30755516 0.30755516 1.30755516 1 1 1.30755516
#> 64 083 1.57401786 1.57401786 2.57401786 1 1 2.57401786
#> 65 084 -0.71774125 -0.71774125 0.28225875 1 1 0.28225875
#> 66 085 -0.09386292 -0.09386292 0.90613708 1 0 -0.09386292
#> 67 086 0.84433451 0.84433451 1.84433451 1 0 0.84433451
#> 68 087 1.94287858 1.94287858 2.94287858 1 0 1.94287858
#> 69 090 -2.34526633 -2.34526633 -1.34526633 1 0 -2.34526633
#> 70 091 0.72967957 0.72967957 1.72967957 1 0 0.72967957
#> 71 092 -1.78544053 -1.78544053 -0.78544053 1 1 -0.78544053
#> 72 093 1.75044200 1.75044200 2.75044200 1 1 2.75044200
#> 73 095 0.22435566 0.22435566 1.22435566 1 0 0.22435566
#> 74 097 -1.16753042 -1.16753042 -0.16753042 1 0 -1.16753042
#> 75 099 -1.07929394 -1.07929394 -0.07929394 1 0 -1.07929394
get_estimates(design, data = dat)
#> estimator term estimate std.error statistic p.value conf.low conf.high df
#> 1 estimator Z 1.383365 0.2661538 5.197614 1.76778e-06 0.852921 1.913809 73
#> outcome inquiry
#> 1 Y ATE