diagnose_design {DeclareDesign}

Diagnose the design

Description

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.frame of simulations, typically created by simulate_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).

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 future and future.apply packages 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")

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.

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.

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")

## Not run: 
# 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


## End(Not run)

# 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)
  )
  
diagnose_design(
  design, 
  diagnosands = default_diagnosands
)

# A longer list of useful diagnosands might include:

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)
  )
  
## Not run: 
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()

## End(Not run)