Get 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  0.67033735  0.67033735  1.67033735 1 1  1.67033735
#> 2  002  1.01731683  1.01731683  2.01731683 1 0  1.01731683
#> 3  003  0.08793546  0.08793546  1.08793546 1 0  0.08793546
#> 4  004 -0.27601149 -0.27601149  0.72398851 1 0 -0.27601149
#> 5  005 -0.83322655 -0.83322655  0.16677345 1 1  0.16677345
#> 6  006 -0.50491973 -0.50491973  0.49508027 1 1  0.49508027
#> 7  007 -1.79710805 -1.79710805 -0.79710805 1 1 -0.79710805
#> 8  008 -0.82787857 -0.82787857  0.17212143 1 1  0.17212143
#> 9  009  0.74771881  0.74771881  1.74771881 1 0  0.74771881
#> 10 012  0.60094252  0.60094252  1.60094252 1 1  1.60094252
#> 11 013  2.46234388  2.46234388  3.46234388 1 1  3.46234388
#> 12 014 -0.13645424 -0.13645424  0.86354576 1 1  0.86354576
#> 13 015 -0.81537736 -0.81537736  0.18462264 1 1  0.18462264
#> 14 016  0.64036276  0.64036276  1.64036276 1 0  0.64036276
#> 15 018  0.51070713  0.51070713  1.51070713 1 1  1.51070713
#> 16 021 -1.17875822 -1.17875822 -0.17875822 1 0 -1.17875822
#> 17 023  0.32653886  0.32653886  1.32653886 1 0  0.32653886
#> 18 024 -1.06011600 -1.06011600 -0.06011600 1 1 -0.06011600
#> 19 025  0.88580210  0.88580210  1.88580210 1 1  1.88580210
#> 20 026 -1.10715956 -1.10715956 -0.10715956 1 0 -1.10715956
#> 21 029 -2.37315928 -2.37315928 -1.37315928 1 1 -1.37315928
#> 22 030  0.15957162  0.15957162  1.15957162 1 0  0.15957162
#> 23 032 -1.04832492 -1.04832492 -0.04832492 1 1 -0.04832492
#> 24 033 -1.07909560 -1.07909560 -0.07909560 1 0 -1.07909560
#> 25 035 -0.79381971 -0.79381971  0.20618029 1 1  0.20618029
#> 26 037 -0.03271025 -0.03271025  0.96728975 1 1  0.96728975
#> 27 038  0.09970543  0.09970543  1.09970543 1 1  1.09970543
#> 28 039  0.70075440  0.70075440  1.70075440 1 0  0.70075440
#> 29 040 -0.96503062 -0.96503062  0.03496938 1 0 -0.96503062
#> 30 041 -1.17862736 -1.17862736 -0.17862736 1 0 -1.17862736
#> 31 043  1.69189785  1.69189785  2.69189785 1 1  2.69189785
#> 32 044 -0.53331351 -0.53331351  0.46668649 1 1  0.46668649
#> 33 045  0.47301486  0.47301486  1.47301486 1 1  1.47301486
#> 34 046  0.58247997  0.58247997  1.58247997 1 0  0.58247997
#> 35 047  1.33049012  1.33049012  2.33049012 1 1  2.33049012
#> 36 048  0.31226340  0.31226340  1.31226340 1 1  1.31226340
#> 37 049  0.92385184  0.92385184  1.92385184 1 1  1.92385184
#> 38 052  0.85737818  0.85737818  1.85737818 1 0  0.85737818
#> 39 053  0.61031138  0.61031138  1.61031138 1 0  0.61031138
#> 40 054 -1.88221603 -1.88221603 -0.88221603 1 1 -0.88221603
#> 41 055  0.26341142  0.26341142  1.26341142 1 1  1.26341142
#> 42 056  0.46509957  0.46509957  1.46509957 1 1  1.46509957
#> 43 057 -0.63323200 -0.63323200  0.36676800 1 1  0.36676800
#> 44 058  0.08867908  0.08867908  1.08867908 1 0  0.08867908
#> 45 060  0.58619256  0.58619256  1.58619256 1 1  1.58619256
#> 46 061 -0.71312727 -0.71312727  0.28687273 1 1  0.28687273
#> 47 063  1.61571678  1.61571678  2.61571678 1 1  2.61571678
#> 48 064 -1.99193744 -1.99193744 -0.99193744 1 1 -0.99193744
#> 49 066  0.06486975  0.06486975  1.06486975 1 0  0.06486975
#> 50 067 -0.62197749 -0.62197749  0.37802251 1 0 -0.62197749
#> 51 068  0.46169039  0.46169039  1.46169039 1 1  1.46169039
#> 52 070  0.03062108  0.03062108  1.03062108 1 0  0.03062108
#> 53 073 -0.69586801 -0.69586801  0.30413199 1 0 -0.69586801
#> 54 074 -0.81781782 -0.81781782  0.18218218 1 1  0.18218218
#> 55 077  0.60848328  0.60848328  1.60848328 1 0  0.60848328
#> 56 078 -0.80747180 -0.80747180  0.19252820 1 1  0.19252820
#> 57 079  0.50982608  0.50982608  1.50982608 1 1  1.50982608
#> 58 080  0.78167699  0.78167699  1.78167699 1 1  1.78167699
#> 59 081  1.45460854  1.45460854  2.45460854 1 1  2.45460854
#> 60 082 -0.51655873 -0.51655873  0.48344127 1 1  0.48344127
#> 61 083  0.21142519  0.21142519  1.21142519 1 1  1.21142519
#> 62 084  1.24699610  1.24699610  2.24699610 1 1  2.24699610
#> 63 085 -0.72571466 -0.72571466  0.27428534 1 1  0.27428534
#> 64 086 -0.95462530 -0.95462530  0.04537470 1 1  0.04537470
#> 65 087 -0.69128681 -0.69128681  0.30871319 1 1  0.30871319
#> 66 089 -0.60342589 -0.60342589  0.39657411 1 1  0.39657411
#> 67 091 -3.15729308 -3.15729308 -2.15729308 1 1 -2.15729308
#> 68 092  1.82644050  1.82644050  2.82644050 1 1  2.82644050
#> 69 093 -2.16882046 -2.16882046 -1.16882046 1 0 -2.16882046
#> 70 094 -1.00999874 -1.00999874 -0.00999874 1 1 -0.00999874
#> 71 095 -0.26104840 -0.26104840  0.73895160 1 1  0.73895160
#> 72 096  0.79876922  0.79876922  1.79876922 1 0  0.79876922
#> 73 097  1.32881677  1.32881677  2.32881677 1 1  2.32881677
#> 74 099  0.82571476  0.82571476  1.82571476 1 1  1.82571476
#> 75 100 -0.61598390 -0.61598390  0.38401610 1 0 -0.61598390

get_estimates(design, data = dat)
#>   estimator term estimate std.error statistic      p.value  conf.low conf.high
#> 1 estimator    Z 1.124539 0.2730184  4.118913 9.918114e-05 0.5804143  1.668664
#>   df outcome inquiry
#> 1 73       Y     ATE