Generates diagnosands from a design or simulations of a design. Speed gains can be achieved by running diagnose_design in parallel, see Examples.
Usage
diagnose_design(
...,
diagnosands = NULL,
sims = 500,
bootstrap_sims = 100,
future.seed = requireNamespace("future", quietly = TRUE),
make_groups = NULL,
add_grouping_variables = NULL
)
diagnose_designs(
...,
diagnosands = NULL,
sims = 500,
bootstrap_sims = 100,
future.seed = requireNamespace("future", quietly = TRUE),
make_groups = NULL,
add_grouping_variables = NULL
)
vars(...)Arguments
- ...
A design or set of designs typically created using the + operator, or a
data.frameof simulations, typically created bysimulate_design.- diagnosands
A set of diagnosands created by
declare_diagnosands. By default, these include bias, root mean-squared error, power, frequentist coverage, the mean and standard deviation of the estimate(s), the "type S" error rate (Gelman and Carlin 2014), and the mean of the inquiry(s). Users can alternatively provide names of diagnosands from the extended diagnosand list.- sims
The number of simulations, defaulting to 500. sims may also be a vector indicating the number of simulations for each step in a design, as described for
simulate_design- bootstrap_sims
Number of bootstrap replicates for the diagnosands to obtain the standard errors of the diagnosands, defaulting to
100. Set to FALSE to turn off bootstrapping.- future.seed
Option for parallel diagnosis via the function future_lapply. A logical or an integer (of length one or seven), or a list of length(X) with pre-generated random seeds. By default, this is set to TRUE if the
futureandfuture.applypackages are installed, and FALSE if not. For details, see ?future_lapply.- make_groups
Add group variables within which diagnosand values will be calculated. New variables can be created or variables already in the simulations data frame selected. Type name-value pairs within the function
vars, i.e.vars(significant = p.value <= 0.05).- add_grouping_variables
Deprecated. Please use make_groups instead. Variables used to generate groups of simulations for diagnosis. Added to default list: c("design", "estimand_label", "estimator", "outcome", "term")
Value
a list with a data frame of simulations, a data frame of diagnosands, a vector of diagnosand names, and if calculated, a data frame of bootstrap replicates.
Details
If the diagnosand function contains a group_by attribute, it will be used to split-apply-combine diagnosands rather than the intersecting column names.
If sims is named, or longer than one element, a fan-out strategy is created and used instead.
If the packages future and future.apply are installed, you can set plan to run multiple simulations in parallel.
Examples
# Two-arm randomized experiment
n <- 500
design <-
declare_model(
N = 1000,
gender = rbinom(N, 1, 0.5),
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = n)) +
declare_assignment(Z = complete_ra(N = N, m = n/2)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
if (FALSE) { # \dontrun{
# Diagnose design using default diagnosands
diagnosis <- diagnose_design(design)
diagnosis
# Use tidy to produce data.frame with bootstrapped standard
# errors and confidence intervals for each diagnosand
diagnosis_df <- tidy(diagnosis)
diagnosis_df
# Use sims argument to change the number of simulations used
# to calculate diagnosands, and bootstrap_sims to change how
# many bootstraps are uses to calculate standard errors.
diagnosis <- diagnose_design(design,
sims = 100,
bootstrap_sims = 100)
tidy(diagnosis)
# You may also run diagnose_design in parallel using
# the future package on a personal computer with multiple
# cores or on high performance computing clusters.
library(future)
options(parallelly.fork.enable = TRUE) # required for use in RStudio
plan(multicore) # note other plans are possible, see future
diagnose_design(design)
# Select specific diagnosands
reshape_diagnosis(diagnosis, select = "Power")
# Use your own diagnosands
my_diagnosands <-
declare_diagnosands(median_bias = median(estimate - estimand),
absolute_error = mean(abs(estimate - estimand)))
diagnosis <- diagnose_design(design, diagnosands = my_diagnosands)
diagnosis
get_diagnosands(diagnosis)
get_simulations(diagnosis)
# Diagnose using an existing data frame of simulations
simulations <- simulate_design(design, sims = 100)
diagnosis <- diagnose_design(simulations_df = simulations)
diagnosis
} # }
# If you do not specify diagnosands, the function default_diagnosands() is used,
# which is reproduced below.
alpha <- 0.05
default_diagnosands <-
declare_diagnosands(
mean_estimand = mean(estimand),
mean_estimate = mean(estimate),
bias = mean(estimate - estimand),
sd_estimate = sqrt(pop.var(estimate)),
rmse = sqrt(mean((estimate - estimand) ^ 2)),
power = mean(p.value <= alpha),
coverage = mean(estimand <= conf.high & estimand >= conf.low)
)
if (FALSE) { # \dontrun{
diagnose_design(
design,
diagnosands = default_diagnosands
)
} # }
# Here is a longer list of useful diagnosands:
extended_diagnosands <-
declare_diagnosands(
mean_estimand = mean(estimand),
mean_estimate = mean(estimate),
bias = mean(estimate - estimand),
sd_estimate = sd(estimate),
rmse = sqrt(mean((estimate - estimand) ^ 2)),
power = mean(p.value <= alpha),
coverage = mean(estimand <= conf.high & estimand >= conf.low),
mean_se = mean(std.error),
type_s_rate = mean((sign(estimate) != sign(estimand))[p.value <= alpha]),
exaggeration_ratio = mean((estimate/estimand)[p.value <= alpha]),
var_estimate = pop.var(estimate),
mean_var_hat = mean(std.error^2),
prop_pos_sig = mean(estimate > 0 & p.value <= alpha),
mean_ci_length = mean(conf.high - conf.low)
)
if (FALSE) { # \dontrun{
# Users can use select_diagnosands to quickly select diagnosands of interest.
diagnose_design(design,
diagnosands = select_diagnosands("prop_pos_sig", "mean_se"),
sims = 100)
diagnose_design(design,
diagnosands = select_diagnosands("prop_pos_sig", "mean_se", alpha = .001),
sims = 100)
diagnose_design(
design,
diagnosands = extended_diagnosands
)
# Adding a group for within group diagnosis:
diagnosis <- diagnose_design(design,
make_groups = vars(significant = p.value <= 0.05),
)
diagnosis
n <- 500
design <-
declare_model(
N = 1000,
gender = rbinom(N, 1, 0.5),
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ rnorm(1) * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = n)) +
declare_assignment(Z = complete_ra(N = N, m = n/2)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
diagnosis <- diagnose_design(design,
make_groups =
vars(effect_size =
cut(estimand, quantile(estimand, (0:4)/4),
include.lowest = TRUE)),
)
diagnosis
# redesign can be used in conjunction with diagnose_designs
# to optimize the design for specific diagnosands
design_vary_N <- redesign(design, n = c(100, 500, 900))
diagnose_designs(design_vary_N)
# Calculate and plot the power of a design over a range of
# effect sizes
design <-
declare_model(
N = 200,
U = rnorm(N),
potential_outcomes(Y ~ runif(1, 0.0, 0.5) * Z + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_assignment(Z = complete_ra(N)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
library(tidyverse)
simulations_df <-
diagnose_design(design) |>
get_simulations() |>
mutate(significant = if_else(p.value <= 0.05, 1, 0))
ggplot(simulations_df) +
stat_smooth(
aes(estimand, significant),
method = 'loess',
color = "#3564ED",
fill = "#72B4F3",
formula = 'y ~ x'
) +
geom_hline(
yintercept = 0.8, color = "#C6227F", linetype = "dashed") +
annotate("text", x = 0, y = 0.85,
label = "Conventional power threshold = 0.8",
hjust = 0, color = "#C6227F") +
scale_y_continuous(breaks = seq(0, 1, 0.2)) +
coord_cartesian(ylim = c(0, 1)) +
theme(legend.position = "none") +
labs(x = "Model parameter: true effect size",
y = "Diagnosand: statistical power") +
theme_minimal()
} # }