other/Building and testing functions.R
In MikkelVembye/POMADE: Power for Meta-Analysis of Dependent Effects
# Building and testing
#library(usethis)
#library(devtools)
library(dplyr)
library(stringr)
#library(furrr)
#library(tictoc)
options(pillar.sigfig = 4) # ensure tibble include 4 digits
options(tibble.width = Inf)
options(dplyr.print_min = 310)
options(scipen = 10)
options(dplyr.summarise.inform = FALSE)
#Sys.setenv(LANG = "en")
# Creates new coding script
#use_r("power_MADE")
#use_r("MDES_MADE")
#use_r("find_J_MADE")
#load_all()
#install()
#
#library(POMADE)
#
#?POMADE::power_MADE()
#############
# plotting
#############
set.seed(10052510)
sigma2_dist <- rgamma(100, shape = 5, rate = 10)
n_ES_dist <- 1 + stats::rpois(100, 5.5 - 1)
power_dat <-
power_MADE(
J = seq(40, 60, 5),
mu = 0.1,
tau = c(0.05, 0.1, 0.2),
omega = c(0.1, 0.2),
rho = c(0.2, 0.7),
alpha = c(0.01, 0.05),
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
model = c("CHE"),
var_df = c("Satt", "RVE"),
iterations = 5,
seed = 10052510
)
power_dat_test <-
power_dat |>
mutate(shape = recode(model, "CE-RVE" = "circle", "CHE-Model+Satt" = "triangle",
"CHE-RVE" = "diamond"))
power_dat2 <-
power_MADE(
J = seq(40, 60, 5),
mu = 0.1,
tau = c(0.05, 0.1, 0.2),
omega = c(0.1, 0.2),
rho = c(0.2, 0.7),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#model = c("CHE", "MLMA", "CE"),
#var_df = c("Model", "Satt", "RVE"),
iterations = 5,
seed = 10052510
)
plot_MADE_engine(
data = power_dat2,
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = rho,
shape = rho,
linetype = rho,
color_lab = "Cor",
shape_lab = "Cor",
line_lab = "Cor",
#assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_dat %>%
filter(rho == 0.7, alpha == 0.05) %>%
plot_MADE_engine(
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = model,
shape = model,
linetype = model,
color_lab = "Model",
shape_lab = "Model",
line_lab = "Model",
shape_scale = "model"
#assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_example <-
plot_MADE(
data = power_dat2,
power_min = 0.5,
expected_studies = c(45, 55),
warning = FALSE,
caption = TRUE,
color = TRUE,
model_comparison = FALSE,
#traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_example
plot_MADE(
data = power_dat2,
power_min = 0.8,
expected_studies = c(45, 55),
warning = FALSE,
caption = FALSE,
color = TRUE,
model_comparison = FALSE,
traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected"),
color_blind = TRUE
)
power_dat3 <-
power_dat2 |>
rename(cor = rho) |>
mutate(cor = as.factor(cor))
plot_MADE_engine(
power_dat3,
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = cor,
shape = cor,
linetype = cor,
color_lab = "Cor",
shape_lab = "Cor",
line_lab = "Cor",
h_lines = 0.8,
v_line = 50,
v_shade = c(45, 55),
x_lab = "Number of Studies (J)",
y_lab = "Power",
caption = "Test",
grid_labs = TRUE,
y_breaks = seq(0,1,0.2),
y_limits = c(0,1)
)
#plot_MADE(
#
#)
#plot_MADE(
# power_dat,
# expected_studies = c(45, 55),
# power_min = 0.7,
# color = TRUE,
# caption = TRUE,
# #breaks = seq(40, 60, 2)
# #numbers = FALSE
#)
POMADE::min_studies_MADE(
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
target_power = 0.8,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
)
J_obj <-
min_studies_MADE(
mu = c(0.1, 0.15, 0.2),
tau = c(0.1, 0.2),
omega = c(0, 0.1, 0.2, 0.3),
rho = c(0.2, 0.7),
target_power = c(0.2, .8),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
); J_obj
CHE_J_1 <- J_obj |> filter(str_detect(model, "CHE-RVE"), mu == 0.1)
CHE_J <- J_obj |> filter(str_detect(model, "CHE"), omega == 0.25)
plot_MADE(
data = J_obj,
traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected", "likely"))
tic()
min_studies_MADE(
mu = 0.2,
tau = c(0.1, 0.2),
omega = c(0.05, 0.15),
rho = c(0.2, 0.7),
target_power = .8,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
) |>
plot_MADE(y_breaks = seq(0, 20, 2))
toc()
min_studies_MADE_engine(
mu = 0.1,
tau = 0.1,
omega = 0.25,
rho = 0.7,
target_power = .8,
#model = "CHE",
#var_df = "Satt",
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
seed = 10052510
)
tic()
studies_needed <-
min_studies_MADE(
mu = 0.2,
tau = c(0.1, 0.2),
omega = c(0.05, 0.1),
rho = 0.7,
target_power = .8,
alpha = 0.05,
model = "CHE", # default
var_df = "RVE", # default
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
studies_needed
toc()
library(future)
multisession(multisession, workers = future::availableCores()-1)
tic()
mdes <- mdes_MADE(
J = c(40, 60),
tau = 0.2,
omega = 0.1,
rho = 0.7,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
mdes
toc()
#tic()
MDES_dat <-
mdes_MADE(
J = seq(60, 90, 10),
tau = c(0, 0.25),
omega = c(0, 0.1),
rho = c(0.2, 0.7),
target_power = .8,
alpha = 0.05,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
)
#toc()
mdes_dat1 <- MDES_dat |> dplyr::filter(model == "CHE-RVE", target_power == 0.8, alpha == 0.05)
plot_MADE(data = MDES_dat, expected_studies = c(70, 80))
tic()
mdes_MADE(
J = c(40, 50, 60),
tau = c(0, 0.2),
omega = c(0, 0.1),
rho = c(0.2, 0.7),
target_power = .8,
alpha = 0.05,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
) |>
plot_MADE(expected_studies = c(45, 55), numbers_ynudge = 0.139)
toc()
#test_dat2 <-
# test_dat |>
# unnest(cols = c(data))
#plot_MADE(
# MDES_dat,
# expected_studies = c(45, 55),
# MDES_min = 0.1,
# color = TRUE,
# caption = TRUE,
# #breaks = seq(40, 60, 2)
# #numbers = FALSE
#)
mdes_obj <-
mdes_MADE(
J = c(40),
tau = 0.2,
omega = 0.1,
rho = 0.7,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
iterations = 5,
seed = 10052510
); mdes_obj
mdes_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
rho = 0.7,
target_power = .8,
model = "CHE",
var_df = "Satt",
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
power_obj <-
power_MADE(
J = c(40),
mu = 0.1,
tau = c(0.1, 0.2),
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
#model = c("CHE", "MLMA", "CE"),
#var_df = "Satt",
iterations = 100,
alpha = .05,
seed = 10052510,
average_power = TRUE,
warning = TRUE
); power_obj
power_MADE(
J = c(40, 60),
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = .05,
seed = 10052510,
average_power = TRUE
)
power_MADE(
J = c(40),
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
#model = c("CHE", "MLMA", "CE"),
#var_df = c("Model", "Satt", "RVE"),
alpha = c(0.01,0.05),
seed = 10052510,
average_power = TRUE
)
power_MADE_single(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2j = rgamma(40, shape = 5, rate = 10),
kj = 5.5,
#model = "CE",
#var_df = "RVE",
alpha = c(0.01, 0.05),
d = 0
)
sigma2_dist <- rgamma(100, shape = 5, rate = 10)
n_ES_dist <- 1 + stats::rpois(100, 5.5 - 1)
power_MADE_engine(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
model = c("CHE"),
var_df = c("Satt")
)
power_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
average_power = TRUE
)
power_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE")
)
tic()
power <- power_MADE(
J = c(40, 60),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = .05,
seed = 10052510
)
power
toc()
####################
# Precison analysis
####################
#
#precision_MADE_single(
# J = 40,
# mu = 0.1,
# tau = 0.2,
# omega = 0.1,
# rho = 0.7,
# sigma2j = sigma2_dist,
# kj = n_ES_dist,
# #model = "CHE",
# #var_df = c("RVE"),
# level = c(0.9, 0.95)
#)
#
#precision_MADE_engine(
# J = 40,
# tau = 0.2,
# omega = 0.1,
# mu = 0.1,
# rho = 0.7,
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# level = c(0.95),
# #model = c("CHE", "MLMA", "CE"),
# #var_df = c("Model", "Satt", "RVE"),
# average_precision = TRUE,
# iterations = 100
#)
#
#precison_dat <-
# precision_MADE(
# J = c(40),
# tau = 0.2,
# omega = 0.1,
# mu = 0.1,
# rho = 0.7,
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# model = c("CHE", "MLMA", "CE"),
# var_df = c("Model", "Satt", "RVE"),
# level = c(0.90, 0.95),
# seed = 10052510,
# average_precision = TRUE
#); precison_dat
#
#ci_lower(level = c(0.9, 0.95), df = 40 - 1, mu = 0.1, se = 0.03)
#ci_upper(level = c(0.9, 0.95), df = 40 - 1, mu = 0.1, se = 0.03)
#
#power_t(df = 40 - 1, lambda = 2.2, alpha = c(0.01, 0.05))
#
#precision <-
# precision_MADE(
# J = c(40, 60),
# mu = 0.1,
# tau = 0.2,
# omega = 0.1,
# rho = 0.7,
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
# model = c("CHE", "MLMA", "CE"),
# var_df = c("Model", "Satt", "RVE"),
# level = .95,
# seed = 10052510
# )
#
#precision
#
#min_studies_width_MADE_engine(
# mu = 0.1,
# tau = 0.1,
# omega = 0.1,
# rho = 0.7,
# level = 0.95,
# target_width = 0.1,
# model = "CHE",
# var_df = "RVE",
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# seed = 10052510
#)
#
#
#
#min_studies_width_MADE(
# mu = 0.15,
# tau = c(0.1, 0.2),
# omega = 0.1,
# rho = c(0.2, 0.7),
# level = c(0.9, 0.95),
# target_width = 0.3,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#
# iterations = 5, # default = 100
# seed = 10052510,
# warning = TRUE,
# upper = 100
#)
#
#width_above_x_MADE_engine(
# mu = 0.2,
# tau = 0.1,
# omega = 0.1,
# rho = 0.7,
# level = 0.95,
# x = 0.05,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
#
#)
#
#
#width_exceed_null <-
# width_above_x_MADE(
#
# mu = 0.1,
# tau = c(0.1, 0.2),
# omega = 0.1,
# rho = c(0.2, 0.7),
# level = 0.95,
# x = 0,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#
# iterations = 5, # defualt = 100
# seed = 10052510,
# warning = TRUE,
# upper = 100
#
# )
#
#width_exceed_null
MikkelVembye/POMADE documentation built on June 15, 2024, 7:15 p.m.
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# Building and testing
#library(usethis)
#library(devtools)
library(dplyr)
library(stringr)
#library(furrr)
#library(tictoc)
options(pillar.sigfig = 4) # ensure tibble include 4 digits
options(tibble.width = Inf)
options(dplyr.print_min = 310)
options(scipen = 10)
options(dplyr.summarise.inform = FALSE)
#Sys.setenv(LANG = "en")
# Creates new coding script
#use_r("power_MADE")
#use_r("MDES_MADE")
#use_r("find_J_MADE")
#load_all()
#install()
#
#library(POMADE)
#
#?POMADE::power_MADE()
#############
# plotting
#############
set.seed(10052510)
sigma2_dist <- rgamma(100, shape = 5, rate = 10)
n_ES_dist <- 1 + stats::rpois(100, 5.5 - 1)
power_dat <-
power_MADE(
J = seq(40, 60, 5),
mu = 0.1,
tau = c(0.05, 0.1, 0.2),
omega = c(0.1, 0.2),
rho = c(0.2, 0.7),
alpha = c(0.01, 0.05),
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
model = c("CHE"),
var_df = c("Satt", "RVE"),
iterations = 5,
seed = 10052510
)
power_dat_test <-
power_dat |>
mutate(shape = recode(model, "CE-RVE" = "circle", "CHE-Model+Satt" = "triangle",
"CHE-RVE" = "diamond"))
power_dat2 <-
power_MADE(
J = seq(40, 60, 5),
mu = 0.1,
tau = c(0.05, 0.1, 0.2),
omega = c(0.1, 0.2),
rho = c(0.2, 0.7),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#model = c("CHE", "MLMA", "CE"),
#var_df = c("Model", "Satt", "RVE"),
iterations = 5,
seed = 10052510
)
plot_MADE_engine(
data = power_dat2,
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = rho,
shape = rho,
linetype = rho,
color_lab = "Cor",
shape_lab = "Cor",
line_lab = "Cor",
#assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_dat %>%
filter(rho == 0.7, alpha == 0.05) %>%
plot_MADE_engine(
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = model,
shape = model,
linetype = model,
color_lab = "Model",
shape_lab = "Model",
line_lab = "Model",
shape_scale = "model"
#assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_example <-
plot_MADE(
data = power_dat2,
power_min = 0.5,
expected_studies = c(45, 55),
warning = FALSE,
caption = TRUE,
color = TRUE,
model_comparison = FALSE,
#traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected")
)
power_example
plot_MADE(
data = power_dat2,
power_min = 0.8,
expected_studies = c(45, 55),
warning = FALSE,
caption = FALSE,
color = TRUE,
model_comparison = FALSE,
traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected"),
color_blind = TRUE
)
power_dat3 <-
power_dat2 |>
rename(cor = rho) |>
mutate(cor = as.factor(cor))
plot_MADE_engine(
power_dat3,
x = J,
y = power,
x_grid = omega,
y_grid = tau,
color = cor,
shape = cor,
linetype = cor,
color_lab = "Cor",
shape_lab = "Cor",
line_lab = "Cor",
h_lines = 0.8,
v_line = 50,
v_shade = c(45, 55),
x_lab = "Number of Studies (J)",
y_lab = "Power",
caption = "Test",
grid_labs = TRUE,
y_breaks = seq(0,1,0.2),
y_limits = c(0,1)
)
#plot_MADE(
#
#)
#plot_MADE(
# power_dat,
# expected_studies = c(45, 55),
# power_min = 0.7,
# color = TRUE,
# caption = TRUE,
# #breaks = seq(40, 60, 2)
# #numbers = FALSE
#)
POMADE::min_studies_MADE(
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
target_power = 0.8,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
)
J_obj <-
min_studies_MADE(
mu = c(0.1, 0.15, 0.2),
tau = c(0.1, 0.2),
omega = c(0, 0.1, 0.2, 0.3),
rho = c(0.2, 0.7),
target_power = c(0.2, .8),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
); J_obj
CHE_J_1 <- J_obj |> filter(str_detect(model, "CHE-RVE"), mu == 0.1)
CHE_J <- J_obj |> filter(str_detect(model, "CHE"), omega == 0.25)
plot_MADE(
data = J_obj,
traffic_light_assumptions = c("unlikely", "likely", "expected", "likely", "expected", "likely"))
tic()
min_studies_MADE(
mu = 0.2,
tau = c(0.1, 0.2),
omega = c(0.05, 0.15),
rho = c(0.2, 0.7),
target_power = .8,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
) |>
plot_MADE(y_breaks = seq(0, 20, 2))
toc()
min_studies_MADE_engine(
mu = 0.1,
tau = 0.1,
omega = 0.25,
rho = 0.7,
target_power = .8,
#model = "CHE",
#var_df = "Satt",
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
seed = 10052510
)
tic()
studies_needed <-
min_studies_MADE(
mu = 0.2,
tau = c(0.1, 0.2),
omega = c(0.05, 0.1),
rho = 0.7,
target_power = .8,
alpha = 0.05,
model = "CHE", # default
var_df = "RVE", # default
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
studies_needed
toc()
library(future)
multisession(multisession, workers = future::availableCores()-1)
tic()
mdes <- mdes_MADE(
J = c(40, 60),
tau = 0.2,
omega = 0.1,
rho = 0.7,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
mdes
toc()
#tic()
MDES_dat <-
mdes_MADE(
J = seq(60, 90, 10),
tau = c(0, 0.25),
omega = c(0, 0.1),
rho = c(0.2, 0.7),
target_power = .8,
alpha = 0.05,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
seed = 10052510,
iterations = 5
)
#toc()
mdes_dat1 <- MDES_dat |> dplyr::filter(model == "CHE-RVE", target_power == 0.8, alpha == 0.05)
plot_MADE(data = MDES_dat, expected_studies = c(70, 80))
tic()
mdes_MADE(
J = c(40, 50, 60),
tau = c(0, 0.2),
omega = c(0, 0.1),
rho = c(0.2, 0.7),
target_power = .8,
alpha = 0.05,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
) |>
plot_MADE(expected_studies = c(45, 55), numbers_ynudge = 0.139)
toc()
#test_dat2 <-
# test_dat |>
# unnest(cols = c(data))
#plot_MADE(
# MDES_dat,
# expected_studies = c(45, 55),
# MDES_min = 0.1,
# color = TRUE,
# caption = TRUE,
# #breaks = seq(40, 60, 2)
# #numbers = FALSE
#)
mdes_obj <-
mdes_MADE(
J = c(40),
tau = 0.2,
omega = 0.1,
rho = 0.7,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
iterations = 5,
seed = 10052510
); mdes_obj
mdes_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
rho = 0.7,
target_power = .8,
model = "CHE",
var_df = "Satt",
sigma2_dist = 4/100,
n_ES_dist = 5.5,
seed = 10052510
)
power_obj <-
power_MADE(
J = c(40),
mu = 0.1,
tau = c(0.1, 0.2),
omega = 0.1,
rho = 0.7,
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
#model = c("CHE", "MLMA", "CE"),
#var_df = "Satt",
iterations = 100,
alpha = .05,
seed = 10052510,
average_power = TRUE,
warning = TRUE
); power_obj
power_MADE(
J = c(40, 60),
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = .05,
seed = 10052510,
average_power = TRUE
)
power_MADE(
J = c(40),
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
#model = c("CHE", "MLMA", "CE"),
#var_df = c("Model", "Satt", "RVE"),
alpha = c(0.01,0.05),
seed = 10052510,
average_power = TRUE
)
power_MADE_single(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2j = rgamma(40, shape = 5, rate = 10),
kj = 5.5,
#model = "CE",
#var_df = "RVE",
alpha = c(0.01, 0.05),
d = 0
)
sigma2_dist <- rgamma(100, shape = 5, rate = 10)
n_ES_dist <- 1 + stats::rpois(100, 5.5 - 1)
power_MADE_engine(
J = 40,
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = 4/100,
n_ES_dist = 5.5,
model = c("CHE"),
var_df = c("Satt")
)
power_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
average_power = TRUE
)
power_MADE_engine(
J = 40,
tau = 0.2,
omega = 0.1,
mu = 0.1,
rho = 0.7,
sigma2_dist = sigma2_dist,
n_ES_dist = n_ES_dist,
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE")
)
tic()
power <- power_MADE(
J = c(40, 60),
mu = 0.1,
tau = 0.2,
omega = 0.1,
rho = 0.7,
sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
model = c("CHE", "MLMA", "CE"),
var_df = c("Model", "Satt", "RVE"),
alpha = .05,
seed = 10052510
)
power
toc()
####################
# Precison analysis
####################
#
#precision_MADE_single(
# J = 40,
# mu = 0.1,
# tau = 0.2,
# omega = 0.1,
# rho = 0.7,
# sigma2j = sigma2_dist,
# kj = n_ES_dist,
# #model = "CHE",
# #var_df = c("RVE"),
# level = c(0.9, 0.95)
#)
#
#precision_MADE_engine(
# J = 40,
# tau = 0.2,
# omega = 0.1,
# mu = 0.1,
# rho = 0.7,
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# level = c(0.95),
# #model = c("CHE", "MLMA", "CE"),
# #var_df = c("Model", "Satt", "RVE"),
# average_precision = TRUE,
# iterations = 100
#)
#
#precison_dat <-
# precision_MADE(
# J = c(40),
# tau = 0.2,
# omega = 0.1,
# mu = 0.1,
# rho = 0.7,
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# model = c("CHE", "MLMA", "CE"),
# var_df = c("Model", "Satt", "RVE"),
# level = c(0.90, 0.95),
# seed = 10052510,
# average_precision = TRUE
#); precison_dat
#
#ci_lower(level = c(0.9, 0.95), df = 40 - 1, mu = 0.1, se = 0.03)
#ci_upper(level = c(0.9, 0.95), df = 40 - 1, mu = 0.1, se = 0.03)
#
#power_t(df = 40 - 1, lambda = 2.2, alpha = c(0.01, 0.05))
#
#precision <-
# precision_MADE(
# J = c(40, 60),
# mu = 0.1,
# tau = 0.2,
# omega = 0.1,
# rho = 0.7,
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
# model = c("CHE", "MLMA", "CE"),
# var_df = c("Model", "Satt", "RVE"),
# level = .95,
# seed = 10052510
# )
#
#precision
#
#min_studies_width_MADE_engine(
# mu = 0.1,
# tau = 0.1,
# omega = 0.1,
# rho = 0.7,
# level = 0.95,
# target_width = 0.1,
# model = "CHE",
# var_df = "RVE",
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
# seed = 10052510
#)
#
#
#
#min_studies_width_MADE(
# mu = 0.15,
# tau = c(0.1, 0.2),
# omega = 0.1,
# rho = c(0.2, 0.7),
# level = c(0.9, 0.95),
# target_width = 0.3,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#
# iterations = 5, # default = 100
# seed = 10052510,
# warning = TRUE,
# upper = 100
#)
#
#width_above_x_MADE_engine(
# mu = 0.2,
# tau = 0.1,
# omega = 0.1,
# rho = 0.7,
# level = 0.95,
# x = 0.05,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = sigma2_dist,
# n_ES_dist = n_ES_dist,
#
#)
#
#
#width_exceed_null <-
# width_above_x_MADE(
#
# mu = 0.1,
# tau = c(0.1, 0.2),
# omega = 0.1,
# rho = c(0.2, 0.7),
# level = 0.95,
# x = 0,
#
# model = "CHE",
# var_df = "RVE",
#
# sigma2_dist = \(x) rgamma(x, shape = 5, rate = 10),
# n_ES_dist = \(x) 1 + stats::rpois(x, 5.5 - 1),
#
# iterations = 5, # defualt = 100
# seed = 10052510,
# warning = TRUE,
# upper = 100
#
# )
#
#width_exceed_null
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