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
Usage
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 0.0882089238 0.0882089238 1.08820892 1 1 1.0882089238
#> 2 002 -1.0292766857 -1.0292766857 -0.02927669 1 0 -1.0292766857
#> 3 003 1.2779934244 1.2779934244 2.27799342 1 1 2.2779934244
#> 4 004 0.7105235432 0.7105235432 1.71052354 1 0 0.7105235432
#> 5 005 0.7914846285 0.7914846285 1.79148463 1 1 1.7914846285
#> 6 007 1.6603131711 1.6603131711 2.66031317 1 1 2.6603131711
#> 7 008 0.3047936432 0.3047936432 1.30479364 1 1 1.3047936432
#> 8 009 -0.5500670345 -0.5500670345 0.44993297 1 0 -0.5500670345
#> 9 010 1.3277120533 1.3277120533 2.32771205 1 1 2.3277120533
#> 10 013 -1.7236784285 -1.7236784285 -0.72367843 1 0 -1.7236784285
#> 11 014 -0.1503354177 -0.1503354177 0.84966458 1 0 -0.1503354177
#> 12 015 1.8756216335 1.8756216335 2.87562163 1 0 1.8756216335
#> 13 016 -0.0541494824 -0.0541494824 0.94585052 1 1 0.9458505176
#> 14 017 1.0358508443 1.0358508443 2.03585084 1 0 1.0358508443
#> 15 020 1.1596702795 1.1596702795 2.15967028 1 1 2.1596702795
#> 16 023 0.4234695792 0.4234695792 1.42346958 1 1 1.4234695792
#> 17 026 0.3215289594 0.3215289594 1.32152896 1 0 0.3215289594
#> 18 027 -0.5407272392 -0.5407272392 0.45927276 1 1 0.4592727608
#> 19 028 0.3747787693 0.3747787693 1.37477877 1 0 0.3747787693
#> 20 029 0.9089614553 0.9089614553 1.90896146 1 1 1.9089614553
#> 21 032 -0.2657090705 -0.2657090705 0.73429093 1 1 0.7342909295
#> 22 034 1.5162468592 1.5162468592 2.51624686 1 1 2.5162468592
#> 23 035 0.4324264148 0.4324264148 1.43242641 1 1 1.4324264148
#> 24 037 1.1543223901 1.1543223901 2.15432239 1 0 1.1543223901
#> 25 038 -0.3519515680 -0.3519515680 0.64804843 1 1 0.6480484320
#> 26 039 0.0904071634 0.0904071634 1.09040716 1 0 0.0904071634
#> 27 041 -0.4797596627 -0.4797596627 0.52024034 1 0 -0.4797596627
#> 28 042 -0.6064035999 -0.6064035999 0.39359640 1 1 0.3935964001
#> 29 043 1.4200548842 1.4200548842 2.42005488 1 0 1.4200548842
#> 30 044 -0.4343053081 -0.4343053081 0.56569469 1 1 0.5656946919
#> 31 045 0.2083944993 0.2083944993 1.20839450 1 1 1.2083944993
#> 32 046 -0.6046266729 -0.6046266729 0.39537333 1 0 -0.6046266729
#> 33 047 -0.5251420547 -0.5251420547 0.47485795 1 1 0.4748579453
#> 34 048 -1.7963713260 -1.7963713260 -0.79637133 1 1 -0.7963713260
#> 35 050 -0.7929924917 -0.7929924917 0.20700751 1 1 0.2070075083
#> 36 051 1.3886685138 1.3886685138 2.38866851 1 0 1.3886685138
#> 37 052 -0.5736266555 -0.5736266555 0.42637334 1 1 0.4263733445
#> 38 053 0.9699459434 0.9699459434 1.96994594 1 1 1.9699459434
#> 39 054 0.3289058652 0.3289058652 1.32890587 1 1 1.3289058652
#> 40 055 0.1484392313 0.1484392313 1.14843923 1 1 1.1484392313
#> 41 056 0.2829642480 0.2829642480 1.28296425 1 1 1.2829642480
#> 42 057 0.3647237515 0.3647237515 1.36472375 1 0 0.3647237515
#> 43 058 -0.0061179292 -0.0061179292 0.99388207 1 1 0.9938820708
#> 44 060 -0.8674028300 -0.8674028300 0.13259717 1 1 0.1325971700
#> 45 062 -1.1129214590 -1.1129214590 -0.11292146 1 0 -1.1129214590
#> 46 063 -2.5013238759 -2.5013238759 -1.50132388 1 1 -1.5013238759
#> 47 064 0.6974536625 0.6974536625 1.69745366 1 1 1.6974536625
#> 48 065 0.7607386805 0.7607386805 1.76073868 1 1 1.7607386805
#> 49 066 -1.3474703481 -1.3474703481 -0.34747035 1 1 -0.3474703481
#> 50 067 0.0558961754 0.0558961754 1.05589618 1 1 1.0558961754
#> 51 068 1.7986213913 1.7986213913 2.79862139 1 0 1.7986213913
#> 52 070 0.8221474598 0.8221474598 1.82214746 1 0 0.8221474598
#> 53 073 -0.0347925819 -0.0347925819 0.96520742 1 0 -0.0347925819
#> 54 074 -0.1676471288 -0.1676471288 0.83235287 1 1 0.8323528712
#> 55 075 0.5451575935 0.5451575935 1.54515759 1 0 0.5451575935
#> 56 077 -0.4246700888 -0.4246700888 0.57532991 1 1 0.5753299112
#> 57 078 -2.1414031275 -2.1414031275 -1.14140313 1 1 -1.1414031275
#> 58 079 -0.0008089723 -0.0008089723 0.99919103 1 0 -0.0008089723
#> 59 080 0.5878388495 0.5878388495 1.58783885 1 1 1.5878388495
#> 60 081 0.5804638019 0.5804638019 1.58046380 1 0 0.5804638019
#> 61 082 -0.2474545353 -0.2474545353 0.75254546 1 1 0.7525454647
#> 62 083 -0.8051049410 -0.8051049410 0.19489506 1 1 0.1948950590
#> 63 086 -0.5907518497 -0.5907518497 0.40924815 1 1 0.4092481503
#> 64 087 0.1248311383 0.1248311383 1.12483114 1 1 1.1248311383
#> 65 088 0.7860616174 0.7860616174 1.78606162 1 0 0.7860616174
#> 66 090 0.0636519345 0.0636519345 1.06365193 1 1 1.0636519345
#> 67 091 0.5927793409 0.5927793409 1.59277934 1 0 0.5927793409
#> 68 092 -1.2490324652 -1.2490324652 -0.24903247 1 1 -0.2490324652
#> 69 094 -0.0877122098 -0.0877122098 0.91228779 1 1 0.9122877902
#> 70 095 -0.5420908691 -0.5420908691 0.45790913 1 1 0.4579091309
#> 71 096 -0.6760862950 -0.6760862950 0.32391371 1 1 0.3239137050
#> 72 097 -0.4438990479 -0.4438990479 0.55610095 1 1 0.5561009521
#> 73 098 -0.2305011819 -0.2305011819 0.76949882 1 1 0.7694988181
#> 74 099 -1.3615372386 -1.3615372386 -0.36153724 1 1 -0.3615372386
#> 75 100 0.9221624282 0.9221624282 1.92216243 1 1 1.9221624282
get_estimates(design, data = dat)
#> estimator term estimate std.error statistic p.value conf.low conf.high
#> 1 estimator Z 1.113085 0.2146903 5.184607 1.860258e-06 0.6852075 1.540962
#> df outcome inquiry
#> 1 73 Y ATE