Insert, delete and replace steps in an (already declared) design object.
Usage
insert_step(design, new_step, before, after)
delete_step(design, step)
replace_step(design, step, new_step)Arguments
- design
A design object, usually created using the + operator,
expand_design, or the design library.- new_step
The new step; Either a function or a partial call.
- before
The step before which to add steps.
- after
The step after which to add steps.
- step
The quoted label of the step to be deleted or replaced.
Examples
my_model <-
declare_model(
N = 100,
U = rnorm(N),
Y_Z_0 = U,
Y_Z_1 = U + rnorm(N, mean = 2, sd = 2)
)
my_assignment <- declare_assignment(Z = complete_ra(N, m = 50))
my_assignment_2 <- declare_assignment(Z = complete_ra(N, m = 25))
design <- my_model + my_assignment
draw_data(design)
#> ID U Y_Z_0 Y_Z_1 Z
#> 1 001 1.0557648146 1.0557648146 4.13068330 0
#> 2 002 -1.2041543007 -1.2041543007 -2.98083209 0
#> 3 003 -0.2219172322 -0.2219172322 3.23219208 0
#> 4 004 -0.9824607144 -0.9824607144 3.02613674 1
#> 5 005 1.3135059845 1.3135059845 6.45042007 1
#> 6 006 0.5235882557 0.5235882557 3.76438618 0
#> 7 007 -0.4563831020 -0.4563831020 4.45130623 1
#> 8 008 -0.1658107398 -0.1658107398 -0.31709837 0
#> 9 009 -1.2863207110 -1.2863207110 1.74769872 0
#> 10 010 0.7133780515 0.7133780515 3.31770331 1
#> 11 011 0.0651739025 0.0651739025 2.59339297 0
#> 12 012 1.2841444299 1.2841444299 4.98466342 0
#> 13 013 0.4786947723 0.4786947723 3.21542743 0
#> 14 014 -0.4259549131 -0.4259549131 0.52767185 1
#> 15 015 -0.0297104064 -0.0297104064 -0.65361147 0
#> 16 016 1.0686731368 1.0686731368 6.41289894 0
#> 17 017 0.7769835878 0.7769835878 1.39412884 1
#> 18 018 0.2776454850 0.2776454850 0.15881726 0
#> 19 019 -0.1173885488 -0.1173885488 3.96181609 0
#> 20 020 0.2262515622 0.2262515622 2.80765195 1
#> 21 021 0.5999789111 0.5999789111 3.32275532 1
#> 22 022 -0.6974498544 -0.6974498544 0.47544151 1
#> 23 023 1.5282643664 1.5282643664 6.45869039 1
#> 24 024 -0.0458642623 -0.0458642623 4.80402790 1
#> 25 025 0.2967305713 0.2967305713 2.49828096 0
#> 26 026 0.0004382916 0.0004382916 2.59904044 1
#> 27 027 -0.6568342182 -0.6568342182 2.74069317 0
#> 28 028 1.0400107880 1.0400107880 3.98587165 1
#> 29 029 -0.3681429804 -0.3681429804 1.56088870 1
#> 30 030 1.1670996966 1.1670996966 0.24335657 1
#> 31 031 -2.0916406802 -2.0916406802 -1.65563713 1
#> 32 032 -0.3038961909 -0.3038961909 3.07610092 1
#> 33 033 0.8885799915 0.8885799915 1.01757250 1
#> 34 034 -0.7540059583 -0.7540059583 0.19090951 1
#> 35 035 0.7559153706 0.7559153706 1.37274293 1
#> 36 036 -0.0962283242 -0.0962283242 -1.13645286 1
#> 37 037 -1.1775877854 -1.1775877854 -1.23555125 0
#> 38 038 1.1511887415 1.1511887415 2.91560256 1
#> 39 039 -1.4373844673 -1.4373844673 -0.32268694 1
#> 40 040 -0.3127468323 -0.3127468323 6.15977351 0
#> 41 041 -0.6644142282 -0.6644142282 1.97792063 1
#> 42 042 -0.9401360644 -0.9401360644 3.68808687 0
#> 43 043 1.6551135156 1.6551135156 5.77058040 1
#> 44 044 0.5499690331 0.5499690331 0.44247041 0
#> 45 045 -0.3623054563 -0.3623054563 0.22577873 0
#> 46 046 0.6448137987 0.6448137987 2.24451533 0
#> 47 047 0.4657680266 0.4657680266 4.53784931 0
#> 48 048 -0.1274661864 -0.1274661864 4.18879884 0
#> 49 049 -1.2420892054 -1.2420892054 0.24296173 0
#> 50 050 -2.1967179128 -2.1967179128 0.07982102 1
#> 51 051 -0.5707402446 -0.5707402446 3.29749704 0
#> 52 052 -0.2028381752 -0.2028381752 3.35230088 0
#> 53 053 0.2509295680 0.2509295680 4.27803847 0
#> 54 054 0.5828057745 0.5828057745 2.50635983 0
#> 55 055 -0.5523831301 -0.5523831301 -3.34686818 0
#> 56 056 -0.3648312517 -0.3648312517 1.67841326 1
#> 57 057 -2.3391057131 -2.3391057131 0.05889155 0
#> 58 058 0.6400764074 0.6400764074 2.50815835 1
#> 59 059 -0.0567857904 -0.0567857904 4.14359353 0
#> 60 060 -0.3722907022 -0.3722907022 1.41192149 1
#> 61 061 -0.6799643957 -0.6799643957 3.44865738 1
#> 62 062 0.2906825064 0.2906825064 3.01049964 0
#> 63 063 0.2167898196 0.2167898196 4.84215148 0
#> 64 064 0.2276709711 0.2276709711 3.56708341 1
#> 65 065 1.5194823942 1.5194823942 6.28545260 1
#> 66 066 -0.7222060877 -0.7222060877 2.01387276 0
#> 67 067 0.4007120451 0.4007120451 0.31479961 1
#> 68 068 -1.7211768856 -1.7211768856 2.30845480 1
#> 69 069 -0.0138401780 -0.0138401780 1.98340505 1
#> 70 070 -0.4231698457 -0.4231698457 -0.81502385 0
#> 71 071 -1.1754862392 -1.1754862392 -2.77679271 0
#> 72 072 -0.7380578765 -0.7380578765 3.31294716 0
#> 73 073 -1.0106253654 -1.0106253654 3.18460925 1
#> 74 074 -0.2318845978 -0.2318845978 -0.07105635 0
#> 75 075 1.2957412047 1.2957412047 7.56867198 0
#> 76 076 2.3680950871 2.3680950871 3.66256063 0
#> 77 077 -1.2381115002 -1.2381115002 0.07596947 1
#> 78 078 0.4269229195 0.4269229195 0.45330241 1
#> 79 079 -0.6883743016 -0.6883743016 1.54062677 0
#> 80 080 0.4803652725 0.4803652725 1.87656159 1
#> 81 081 -0.5058851451 -0.5058851451 0.64588485 1
#> 82 082 -1.4828512797 -1.4828512797 1.16690463 0
#> 83 083 0.5914922360 0.5914922360 -1.70216630 0
#> 84 084 1.8114201513 1.8114201513 3.58411836 1
#> 85 085 -0.6898119794 -0.6898119794 2.48937464 1
#> 86 086 -1.5977899714 -1.5977899714 -1.23905301 0
#> 87 087 -0.9995042992 -0.9995042992 0.57725845 0
#> 88 088 -0.6030947483 -0.6030947483 1.36870757 0
#> 89 089 -1.1249083217 -1.1249083217 0.60676849 1
#> 90 090 0.8687806713 0.8687806713 0.21452787 0
#> 91 091 -0.3616779625 -0.3616779625 1.47919012 0
#> 92 092 -0.8556794358 -0.8556794358 0.64135429 1
#> 93 093 2.1354465506 2.1354465506 5.07245071 1
#> 94 094 0.5137863186 0.5137863186 -0.39639117 0
#> 95 095 -0.5924127920 -0.5924127920 3.01705808 1
#> 96 096 -0.2033279395 -0.2033279395 3.61301498 0
#> 97 097 -0.7535537044 -0.7535537044 -0.31364833 1
#> 98 098 -0.5915549915 -0.5915549915 2.04160977 1
#> 99 099 -0.6836084903 -0.6836084903 3.55529877 1
#> 100 100 -0.0535903234 -0.0535903234 5.49658410 1
design_modified <- replace_step(design, 2, my_assignment_2)
draw_data(design)
#> ID U Y_Z_0 Y_Z_1 Z
#> 1 001 -0.005390775 -0.005390775 -0.887400181 1
#> 2 002 -0.203131043 -0.203131043 2.340872106 1
#> 3 003 -2.518741475 -2.518741475 -1.903311413 1
#> 4 004 1.138341886 1.138341886 1.975354938 1
#> 5 005 -0.452576398 -0.452576398 -1.242422037 0
#> 6 006 -0.962793721 -0.962793721 2.317896618 0
#> 7 007 -1.067573816 -1.067573816 -3.690913289 1
#> 8 008 -0.006817742 -0.006817742 2.909691089 1
#> 9 009 -0.987444655 -0.987444655 -0.286810044 1
#> 10 010 2.621672876 2.621672876 2.195890088 0
#> 11 011 -1.089103689 -1.089103689 -1.114919063 1
#> 12 012 1.281289189 1.281289189 2.107286106 1
#> 13 013 1.898194419 1.898194419 2.957156396 0
#> 14 014 0.101060886 0.101060886 3.472473050 1
#> 15 015 -0.882328716 -0.882328716 0.569333745 1
#> 16 016 0.532369401 0.532369401 2.512259992 1
#> 17 017 0.287178087 0.287178087 1.395760507 0
#> 18 018 1.923649433 1.923649433 5.625713032 0
#> 19 019 -0.653623117 -0.653623117 -2.776767021 1
#> 20 020 0.235884818 0.235884818 3.020238160 1
#> 21 021 -0.097388741 -0.097388741 1.085434588 1
#> 22 022 -0.020985348 -0.020985348 1.155378661 1
#> 23 023 1.257036956 1.257036956 4.753048019 1
#> 24 024 0.086122440 0.086122440 4.484682354 1
#> 25 025 -0.237735634 -0.237735634 1.159073069 1
#> 26 026 0.549312241 0.549312241 0.712011368 0
#> 27 027 -0.446672620 -0.446672620 5.283934780 1
#> 28 028 -0.710892756 -0.710892756 0.440399670 1
#> 29 029 0.420807033 0.420807033 8.405692692 1
#> 30 030 0.759226888 0.759226888 1.180361035 0
#> 31 031 0.955231964 0.955231964 4.036797915 0
#> 32 032 -1.196881983 -1.196881983 -4.628752459 1
#> 33 033 0.775033246 0.775033246 -0.000647647 0
#> 34 034 0.174844648 0.174844648 4.294892885 1
#> 35 035 -0.695422040 -0.695422040 3.364604599 0
#> 36 036 -0.222536586 -0.222536586 -3.432179891 0
#> 37 037 -0.632694526 -0.632694526 4.323653662 0
#> 38 038 0.407235674 0.407235674 3.701049525 0
#> 39 039 -0.648210797 -0.648210797 2.479294374 1
#> 40 040 0.436791930 0.436791930 7.353343191 1
#> 41 041 -0.082176451 -0.082176451 5.875837641 0
#> 42 042 0.017807959 0.017807959 5.284303661 1
#> 43 043 1.365421873 1.365421873 3.896120620 1
#> 44 044 1.042770770 1.042770770 0.502354364 0
#> 45 045 0.673398731 0.673398731 4.157736059 1
#> 46 046 0.084206704 0.084206704 1.741523072 0
#> 47 047 0.315010076 0.315010076 2.146434708 0
#> 48 048 0.080343593 0.080343593 1.369495234 1
#> 49 049 -0.991117668 -0.991117668 0.438234659 1
#> 50 050 -0.349930135 -0.349930135 3.559115206 1
#> 51 051 -0.683567045 -0.683567045 1.520235733 1
#> 52 052 -0.014281046 -0.014281046 -0.214642871 0
#> 53 053 -0.614937652 -0.614937652 1.168790666 0
#> 54 054 0.196932210 0.196932210 4.697957942 0
#> 55 055 0.842169406 0.842169406 1.049893495 1
#> 56 056 0.459826987 0.459826987 3.789702055 0
#> 57 057 0.425710108 0.425710108 3.384444446 0
#> 58 058 -1.877763012 -1.877763012 0.981588037 1
#> 59 059 2.017194353 2.017194353 3.691516774 0
#> 60 060 -0.975002667 -0.975002667 0.956854011 0
#> 61 061 0.485431668 0.485431668 3.779121326 0
#> 62 062 0.653330681 0.653330681 2.994897964 0
#> 63 063 -0.752953453 -0.752953453 2.396555667 0
#> 64 064 0.397994089 0.397994089 3.892846329 0
#> 65 065 0.499298129 0.499298129 2.499571482 1
#> 66 066 0.161834044 0.161834044 5.897124570 0
#> 67 067 -1.255932976 -1.255932976 4.263330081 1
#> 68 068 0.465746076 0.465746076 2.310192806 1
#> 69 069 0.004170127 0.004170127 1.725703401 1
#> 70 070 0.152160094 0.152160094 2.773151299 0
#> 71 071 -0.420789742 -0.420789742 1.558084941 0
#> 72 072 -0.181145083 -0.181145083 4.629003654 0
#> 73 073 0.945305169 0.945305169 1.480160481 1
#> 74 074 -0.558223549 -0.558223549 1.659136095 1
#> 75 075 0.227361215 0.227361215 3.292424017 0
#> 76 076 -0.528083856 -0.528083856 0.113752239 1
#> 77 077 -0.401479245 -0.401479245 2.252437149 1
#> 78 078 -0.419249667 -0.419249667 3.195038989 0
#> 79 079 -0.280384254 -0.280384254 2.031389268 1
#> 80 080 -0.044008863 -0.044008863 2.697321297 0
#> 81 081 -0.313848163 -0.313848163 5.243729908 0
#> 82 082 0.007211932 0.007211932 5.319172185 1
#> 83 083 -0.280806508 -0.280806508 -0.233946326 0
#> 84 084 0.475873057 0.475873057 3.273039671 0
#> 85 085 -0.116402194 -0.116402194 -1.884753118 0
#> 86 086 0.849828175 0.849828175 4.686505554 0
#> 87 087 -1.340510884 -1.340510884 -0.292468981 1
#> 88 088 -1.357123048 -1.357123048 1.884131981 0
#> 89 089 0.699354497 0.699354497 2.529953909 0
#> 90 090 -0.450414570 -0.450414570 -2.277878744 0
#> 91 091 0.613040203 0.613040203 5.379965882 0
#> 92 092 0.659108565 0.659108565 5.517129719 0
#> 93 093 -3.031273247 -3.031273247 -4.478948241 1
#> 94 094 0.304154325 0.304154325 4.595724863 1
#> 95 095 -2.676150686 -2.676150686 -1.760395563 1
#> 96 096 1.959778323 1.959778323 5.907112444 0
#> 97 097 0.759083998 0.759083998 2.788143369 1
#> 98 098 1.743942112 1.743942112 2.471450619 0
#> 99 099 -1.050069639 -1.050069639 1.874968611 0
#> 100 100 -0.117426314 -0.117426314 3.341807892 0
if (FALSE) {
design <-
declare_model(
N = 100,
U = rnorm(N),
potential_outcomes(Y ~ 0.20 * Z + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_assignment(Z = complete_ra(N, m = N/2)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
insert_step(design, declare_sampling(S = complete_rs(N, n = 50)),
after = 1)
# If you are using a design created by a designer, for example from
# the DesignLibrary package, you will not have access to the step
# objects. Instead, you can always use the label of the step.
design <- DesignLibrary::two_arm_designer()
# get the labels for the steps
names(design)
insert_step(design,
declare_sampling(S = complete_rs(N, n = 50)),
after = "potential_outcomes")
}
design <-
declare_model(
N = 100,
U = rnorm(N),
potential_outcomes(Y ~ 0.20 * Z + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_assignment(Z = complete_ra(N, m = N/2)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
delete_step(design, step = 5)
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 100, U = rnorm(N), potential_outcomes(Y ~ 0.2 * Z + U))
#>
#> Step 2 (inquiry): declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) -------------------
#>
#> Step 3 (assignment): declare_assignment(Z = complete_ra(N, m = N/2)) -----------
#>
#> Step 4 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Run of the design:
#>
#> inquiry estimand
#> ATE 0.2
#>
design <-
declare_model(
N = 100,
U = rnorm(N),
potential_outcomes(Y ~ 0.20 * Z + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_assignment(Z = complete_ra(N, m = N/2)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
replace_step(
design,
step = 3,
new_step = declare_assignment(Z = simple_ra(N, prob = 0.5)))
#>
#> Research design declaration summary
#>
#> Step 1 (model): declare_model(N = 100, U = rnorm(N), potential_outcomes(Y ~ 0.2 * Z + U))
#>
#> Step 2 (inquiry): declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) -------------------
#>
#> Step 3 (assignment): declare_assignment(Z = simple_ra(N, prob = 0.5)) ----------
#>
#> Step 4 (measurement): declare_measurement(Y = reveal_outcomes(Y ~ Z)) ----------
#>
#> Step 5 (estimator): declare_estimator(Y ~ Z, inquiry = "ATE") ------------------
#>
#> Run of the design:
#>
#> inquiry estimand estimator term estimate std.error statistic p.value conf.low
#> ATE 0.2 estimator Z 0.311 0.199 1.56 0.122 -0.0845
#> conf.high df outcome
#> 0.707 98 Y
#>