Compare two designs
compare_designs(
design1,
design2,
format = "ansi8",
pager = "off",
context = -1L,
rmd = FALSE
)
compare_design_code(
design1,
design2,
format = "ansi256",
mode = "sidebyside",
pager = "off",
context = -1L,
rmd = FALSE
)
compare_design_summaries(
design1,
design2,
format = "ansi256",
mode = "sidebyside",
pager = "off",
context = -1L,
rmd = FALSE
)
compare_design_data(
design1,
design2,
format = "ansi256",
mode = "sidebyside",
pager = "off",
context = -1L,
rmd = FALSE
)
compare_design_estimates(
design1,
design2,
format = "ansi256",
mode = "auto",
pager = "off",
context = -1L,
rmd = FALSE
)
compare_design_inquiries(
design1,
design2,
format = "ansi256",
mode = "sidebyside",
pager = "off",
context = -1L,
rmd = FALSE
)Arguments
- design1
A design object, typically created using the + operator
- design2
A design object, typically created using the + operator
- format
Format (in console or HTML) options from
diffobj::diffChr- pager
Pager option from
diffobj::diffChr- context
Context option from
diffobj::diffChrwhich sets the number of lines around differences that are printed. By default, all lines of the two objects are shown. To show only the lines that are different, setcontext = 0; to get one line around differences for context, set to 1.- rmd
Set to
TRUEuse in Rmarkdown HTML output. NB: will not work with LaTeX, Word, or other .Rmd outputs.- mode
Mode options from
diffobj::diffChr
Examples
design1 <- 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")
design2 <- declare_model(N = 200, U = rnorm(N),
potential_outcomes(Y ~ 0.5*Z + U)) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N, n = 100)) +
declare_assignment(Z = complete_ra(N, m = 25)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, model = lm_robust, inquiry = "ATE")
compare_designs(design1, design2)
#> Loading required namespace: diffobj
#> Research design comparison
#>
#>
#>
#> # Compare design code -----------------------------------------------------------
#>
#> < str(design1) > str(design2)
#> @@ 1,2 @@ @@ 1,2 @@
#> < Named chr [1:6] "declare_model(N = 1 > Named chr [1:6] "declare_model(N = 2
#> : 00, u = rnorm(N), potential_outcomes( : 00, U = rnorm(N), potential_outcomes(
#> : Y ~ Z + u))" ... : Y ~ 0.5 * Z + U))" ...
#> - attr(*, "names")= chr [1:6] "model - attr(*, "names")= chr [1:6] "model
#> " "ATE" "sampling" "assignment" ... " "ATE" "sampling" "assignment" ...
#>
#>
#> # Compare draw_data(design) -----------------------------------------------------
#>
#> < str(design1) > str(design2)
#> @@ 1,8 @@ @@ 1,8 @@
#> < 'data.frame': 75 obs. of 7 variabl > 'data.frame': 100 obs. of 7 variab
#> : es: : les:
#> < $ ID : chr "001" "002" "004" "005 > $ ID : chr "001" "002" "003" "005
#> : " ... : " ...
#> < $ u : num 0.622 0.957 -1.26 -0.7 > $ U : num 0.622 0.957 -1.233 -0.
#> : 09 -1.011 ... : 709 0.683 ...
#> < $ Y_Z_0: num 0.622 0.957 -1.26 -0.7 > $ Y_Z_0: num 0.622 0.957 -1.233 -0.
#> : 09 -1.011 ... : 709 0.683 ...
#> < $ Y_Z_1: num 1.622 1.9575 -0.2598 0 > $ Y_Z_1: num 1.122 1.457 -0.733 -0.
#> : .2909 -0.0109 ... : 209 1.183 ...
#> $ S : num 1 1 1 1 1 1 1 1 1 1 .. $ S : num 1 1 1 1 1 1 1 1 1 1 ..
#> . .
#> < $ Z : int 1 1 1 1 1 1 1 1 1 1 .. > $ Z : int 0 0 1 0 0 0 0 0 0 0 ..
#> : . : .
#> < $ Y : num 1.622 1.9575 -0.2598 0 > $ Y : num 0.622 0.957 -0.733 -0.
#> : .2909 -0.0109 ... : 709 0.683 ...
#>
#>
#> # Compare draw_estimands(design) ------------------------------------------------
#>
#> < design1 > design2
#> @@ 1,2 @@ @@ 1,2 @@
#> inquiry estimand inquiry estimand
#> < 1 ATE 1 > 1 ATE 0.5
#>
#>
#> # Compare draw_estimates(design) ------------------------------------------------
#>
#> < design1
#> > design2
#> @@ 1,4 / 1,4 @@
#> < estimator term estimate std.error statistic p.value conf.low
#> > estimator term estimate std.error statistic p.value conf.low conf.high
#> < 1 estimator Z 0.8951884 0.1889807 4.736931 1.040763e-05 0.5185505
#> > 1 estimator Z 0.550775 0.2439062 2.258143 0.02615435 0.06675111 1.034799
#> < conf.high df outcome inquiry
#> > df outcome inquiry
#> < 1 1.271826 73 Y ATE
#> > 1 98 Y ATE
compare_design_code(design1, design2)
#> < str(design1) > str(design2)
#> @@ 1,2 @@ @@ 1,2 @@
#> < Named chr [1:6] "declare_model(N = 1 > Named chr [1:6] "declare_model(N = 2
#> : 00, u = rnorm(N), potential_outcomes( : 00, U = rnorm(N), potential_outcomes(
#> : Y ~ Z + u))" ... : Y ~ 0.5 * Z + U))" ...
#> - attr(*, "names")= chr [1:6] "model - attr(*, "names")= chr [1:6] "model
#> " "ATE" "sampling" "assignment" ... " "ATE" "sampling" "assignment" ...
compare_design_summaries(design1, design2)
#> < design1 > design2
#> @@ 1,97 @@ @@ 1,99 @@
#>
#> Design Summary Design Summary
#>
#> < Step 1 (model): declare_model(N = 100 > Step 1 (model): declare_model(N = 200
#> : , u = rnorm(N), potential_outcomes(Y : , U = rnorm(N), potential_outcomes(Y
#> : ~ Z + u)) : ~ 0.5 * Z + U))
#>
#> < N = 100 > N = 200
#>
#> Added variable: ID Added variable: ID
#> N_missing N_unique class N_missing N_unique class
#> < 0 100 character > 0 200 character
#>
#> < Added variable: u > Added variable: U
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -1.86 -0.06 0.04 1.89 0.91 > -2.11 -0.06 0.05 2.95 0.93
#> : 0 100 : 0 200
#>
#> Added variable: Y_Z_0 Added variable: Y_Z_0
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -1.86 -0.06 0.04 1.89 0.91 > -2.11 -0.06 0.05 2.95 0.93
#> : 0 100 : 0 200
#>
#> Added variable: Y_Z_1 Added variable: Y_Z_1
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -0.86 0.94 1.04 2.89 0.91 > -1.61 0.44 0.55 3.45 0.93
#> : 0 100 : 0 200
#>
#> Step 2 (inquiry): declare_inquiry(ATE Step 2 (inquiry): declare_inquiry(ATE
#> = mean(Y_Z_1 - Y_Z_0)) ------------- = mean(Y_Z_1 - Y_Z_0)) -------------
#> ------ ------
#>
#> A single draw of the inquiry: A single draw of the inquiry:
#> inquiry estimand inquiry estimand
#> < ATE 1 > ATE 0.5
#>
#> < Step 3 (sampling): declare_sampling(S > Step 3 (sampling): declare_sampling(S
#> : = complete_rs(N, n = 75)) ---------- : = complete_rs(N, n = 100)) ---------
#> : ------ : ------
#>
#> < N = 75 (25 subtracted) > N = 100 (100 subtracted)
#>
#> Added variable: S Added variable: S
#> 1 1
#> < 75 > 100
#> 1.00 1.00
#>
#> Altered variable: ID Altered variable: ID
#> Before: Before:
#> N_missing N_unique class N_missing N_unique class
#> < 0 100 character > 0 200 character
#>
#> After: After:
#> N_missing N_unique class N_missing N_unique class
#> < 0 75 character > 0 100 character
#>
#> < Altered variable: u > Altered variable: U
#> Before: Before:
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -1.86 -0.06 0.04 1.89 0.91 > -2.11 -0.06 0.05 2.95 0.93
#> : 0 100 : 0 200
#>
#> After: After:
#> min median mean max sd N_missin min median mean max sd N_missi
#> g N_unique ng N_unique
#> < -1.86 -0.06 0.03 1.89 0.93 > -2.11 -0.21 -0.08 2.75 0.94
#> : 0 75 : 0 100
#>
#> Altered variable: Y_Z_0 Altered variable: Y_Z_0
#> Before: Before:
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -1.86 -0.06 0.04 1.89 0.91 > -2.11 -0.06 0.05 2.95 0.93
#> : 0 100 : 0 200
#>
#> After: After:
#> min median mean max sd N_missin min median mean max sd N_missi
#> g N_unique ng N_unique
#> < -1.86 -0.06 0.03 1.89 0.93 > -2.11 -0.21 -0.08 2.75 0.94
#> : 0 75 : 0 100
#>
#> Altered variable: Y_Z_1 Altered variable: Y_Z_1
#> Before: Before:
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -0.86 0.94 1.04 2.89 0.91 > -1.61 0.44 0.55 3.45 0.93
#> : 0 100 : 0 200
#>
#> After: After:
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -0.86 0.94 1.03 2.89 0.93 > -1.61 0.29 0.42 3.25 0.94
#> : 0 75 : 0 100
#>
#> < Step 4 (assignment): declare_assignme > Step 4 (assignment): declare_assignme
#> : nt(Z = complete_ra(N, m = 50)) ------ : nt(Z = complete_ra(N, m = 25)) ------
#> : ------ : ------
#>
#> Added variable: Z Added variable: Z
#> 0 1 0 1
#> < 25 50 > 75 25
#> < 0.33 0.67 > 0.75 0.25
#>
#> Step 5 (measurement): declare_measure Step 5 (measurement): declare_measure
#> ment(Y = reveal_outcomes(Y ~ Z)) ---- ment(Y = reveal_outcomes(Y ~ Z)) ----
#> ------ ------
#>
#> Added variable: Y Added variable: Y
#> min median mean max sd N_missin min median mean max sd N_missin
#> g N_unique g N_unique
#> < -1.39 0.74 0.69 2.89 0.95 > -1.86 -0.04 0.05 3.25 0.97
#> : 0 75 : 0 100
#>
#> < Step 6 (estimator): declare_estimator > Step 6 (estimator): declare_estimator
#> : (Y ~ Z, inquiry = "ATE") ------------ : (Y ~ Z, model = lm_robust, inquiry =
#> : ------ : "ATE")
#>
#> Formula: Y ~ Z Formula: Y ~ Z
#> ~ >
#> ~ > Model: lm_robust
#>
#> A single draw of the estimator: A single draw of the estimator:
#> estimator term estimate std.error s estimator term estimate std.error st
#> tatistic p.value conf.low conf.hi atistic p.value conf.low conf.hi
#> gh df gh df
#> < estimator Z 0.6047391 0.2313945 > estimator Z 0.550775 0.2439062 2
#> : 2.613455 0.01087875 0.1435706 1.0659 : .258143 0.02615435 0.06675111 1.0347
#> : 08 73 : 99 98
#> outcome inquiry outcome inquiry
#> Y ATE Y ATE
#>
compare_design_data(design1, design2)
#> < str(design1) > str(design2)
#> @@ 1,8 @@ @@ 1,8 @@
#> < 'data.frame': 75 obs. of 7 variabl > 'data.frame': 100 obs. of 7 variab
#> : es: : les:
#> < $ ID : chr "001" "002" "003" "004 > $ ID : chr "001" "002" "003" "005
#> : " ... : " ...
#> < $ u : num 0.4298 0.9584 -0.0161 > $ U : num 0.622 0.957 -1.233 -0.
#> : 1.93 0.9866 ... : 709 0.683 ...
#> < $ Y_Z_0: num 0.4298 0.9584 -0.0161 > $ Y_Z_0: num 0.622 0.957 -1.233 -0.
#> : 1.93 0.9866 ... : 709 0.683 ...
#> < $ Y_Z_1: num 1.43 1.958 0.984 2.93 > $ Y_Z_1: num 1.122 1.457 -0.733 -0.
#> : 1.987 ... : 209 1.183 ...
#> $ S : num 1 1 1 1 1 1 1 1 1 1 .. $ S : num 1 1 1 1 1 1 1 1 1 1 ..
#> . .
#> < $ Z : int 1 1 1 1 0 1 1 0 1 1 .. > $ Z : int 0 0 1 0 0 0 0 0 0 0 ..
#> : . : .
#> < $ Y : num 1.43 1.958 0.984 2.93 > $ Y : num 0.622 0.957 -0.733 -0.
#> : 0.987 ... : 709 0.683 ...
compare_design_estimates(design1, design2)
#> < design1
#> > design2
#> @@ 1,4 / 1,4 @@
#> < estimator term estimate std.error statistic p.value conf.low
#> > estimator term estimate std.error statistic p.value conf.low conf.high
#> < 1 estimator Z 0.8951884 0.1889807 4.736931 1.040763e-05 0.5185505
#> > 1 estimator Z 0.550775 0.2439062 2.258143 0.02615435 0.06675111 1.034799
#> < conf.high df outcome inquiry
#> > df outcome inquiry
#> < 1 1.271826 73 Y ATE
#> > 1 98 Y ATE
compare_design_inquiries(design1, design2)
#> < design1 > design2
#> @@ 1,2 @@ @@ 1,2 @@
#> inquiry estimand inquiry estimand
#> < 1 ATE 1 > 1 ATE 0.5