Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(dplyr)
library(ggplot2)
library(purrr)
library(tibble)
library(tidyr)
library(flipr)
load("../R/sysdata.rda")
## ----setup, message=FALSE-----------------------------------------------------
ngrid_in <- 10
ngrid_out <- 100
nperms <- 100000
n1 <- 30
n2 <- 30
set.seed(1301)
x1 <- rnorm(n1, mean = 0, sd = 1)
x2 <- rnorm(n2, mean = 3, sd = 1)
y1 <- rnorm(n1, mean = 0, sd = 1)
y2 <- rnorm(n2, mean = 0, sd = 2)
z1 <- rnorm(n1, mean = 0, sd = 1)
z2 <- rnorm(n2, mean = 3, sd = 2)
## ---- eval=FALSE--------------------------------------------------------------
# null_spec <- function(y, parameters) {
# map(y, ~ .x - parameters)
# }
# stat_functions <- list(stat_t)
# stat_assignments <- list(delta = 1)
#
# pf <- PlausibilityFunction$new(
# null_spec = null_spec,
# stat_functions = stat_functions,
# stat_assignments = stat_assignments,
# x1, x2,
# seed = 1234
# )
# pf$set_nperms(nperms)
# pf$set_point_estimate(mean(x2) - mean(x1))
# pf$set_parameter_bounds(
# point_estimate = pf$point_estimate,
# conf_level = pf$max_conf_level
# )
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_in
# )
#
# pf$set_alternative("two_tail")
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- rename(pf$grid, two_tail = pvalue)
#
# pf$set_alternative("left_tail")
# pf$grid$pvalue <- NULL
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- bind_rows(
# df,
# rename(pf$grid, left_tail = pvalue)
# )
#
# pf$set_alternative("right_tail")
# pf$grid$pvalue <- NULL
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- bind_rows(
# df,
# rename(pf$grid, right_tail = pvalue)
# )
#
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_out
# )
#
# df_mean <- tibble(
# delta = pf$grid$delta,
# two_tail = approx(df$delta, df$two_tail, delta)$y,
# left_tail = approx(df$delta, df$left_tail, delta)$y,
# right_tail = approx(df$delta, df$right_tail, delta)$y,
# ) %>%
# pivot_longer(-delta)
## ---- fig.asp=0.8, fig.width=6, out.width="97%", dpi=300----------------------
df_mean %>%
ggplot(aes(delta, value, color = name)) +
geom_line() +
labs(
title = "P-value function for the mean",
subtitle = "t-statistic",
x = expression(delta),
y = "p-value",
color = "Type"
) +
geom_hline(
yintercept = 0.05,
color = "black",
linetype = "dashed"
) +
geom_vline(
xintercept = mean(x2) - mean(x1),
color = "black"
) +
geom_vline(
xintercept = stats::t.test(x2, x1, var.equal = TRUE)$conf.int,
color = "black",
linetype = "dashed"
) +
scale_y_continuous(breaks = seq(0, 1, by = 0.05), limits = c(0, 1))
## ---- eval=FALSE--------------------------------------------------------------
# null_spec <- function(y, parameters) {
# map(y, ~ .x / parameters)
# }
# stat_functions <- list(stat_f)
# stat_assignments <- list(rho = 1)
#
# pf <- PlausibilityFunction$new(
# null_spec = null_spec,
# stat_functions = stat_functions,
# stat_assignments = stat_assignments,
# y1, y2,
# seed = 1234
# )
# pf$set_nperms(nperms)
# pf$set_point_estimate(sd(y2) / sd(y1))
# pf$set_parameter_bounds(
# point_estimate = pf$point_estimate,
# conf_level = pf$max_conf_level
# )
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_in
# )
#
# pf$set_alternative("two_tail")
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- rename(pf$grid, two_tail = pvalue)
#
# pf$set_alternative("left_tail")
# pf$grid$pvalue <- NULL
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- bind_rows(
# df,
# rename(pf$grid, left_tail = pvalue)
# )
#
# pf$set_alternative("right_tail")
# pf$grid$pvalue <- NULL
# pf$evaluate_grid(
# grid = pf$grid,
# ncores = 1
# )
# df <- bind_rows(
# df,
# rename(pf$grid, right_tail = pvalue)
# )
#
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_out
# )
#
# df_sd <- tibble(
# rho = pf$grid$rho,
# two_tail = approx(df$rho, df$two_tail, rho)$y,
# left_tail = approx(df$rho, df$left_tail, rho)$y,
# right_tail = approx(df$rho, df$right_tail, rho)$y,
# ) %>%
# pivot_longer(-rho)
## ---- fig.asp=0.8, fig.width=6, out.width="97%", dpi=300----------------------
df_sd %>%
ggplot(aes(rho, value, color = name)) +
geom_line() +
labs(
title = "P-value function for the standard deviation",
subtitle = "F-statistic",
x = expression(rho),
y = "p-value",
color = "Type"
) +
geom_hline(
yintercept = 0.05,
color = "black",
linetype = "dashed"
) +
geom_vline(
xintercept = sqrt(stats::var.test(y2, y1)$statistic),
color = "black"
) +
geom_vline(
xintercept = sqrt(stats::var.test(y2, y1)$conf.int),
color = "black",
linetype = "dashed"
) +
scale_y_continuous(breaks = seq(0, 1, by = 0.05), limits = c(0, 1))
## ---- eval=FALSE--------------------------------------------------------------
# null_spec <- function(y, parameters) {
# map(y, ~ (.x - parameters[1]) / parameters[2])
# }
# stat_functions <- list(stat_t, stat_f)
# stat_assignments <- list(delta = 1, rho = 2)
#
# pf <- PlausibilityFunction$new(
# null_spec = null_spec,
# stat_functions = stat_functions,
# stat_assignments = stat_assignments,
# z1, z2,
# seed = 1234
# )
# pf$set_nperms(nperms)
# pf$set_point_estimate(c(
# mean(z2) - sd(z2) / sd(z1) * mean(z1),
# sd(z2) / sd(z1)
# ))
# pf$set_parameter_bounds(
# point_estimate = pf$point_estimate,
# conf_level = pf$max_conf_level
# )
#
# # Fisher combining function
# pf$set_aggregator("fisher")
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_in
# )
# pf$evaluate_grid(grid = pf$grid, ncores = 1)
# grid_in <- pf$grid
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_out
# )
# if (requireNamespace("interp", quietly = TRUE)) {
# Zout <- interp::interp(
# x = grid_in$delta,
# y = grid_in$log_rho,
# z = grid_in$pvalue,
# xo = sort(unique(pf$grid$delta)),
# yo = sort(unique(pf$grid$log_rho))
# )
# pf$grid$pvalue <- as.numeric(Zout$z)
# } else
# pf$grid$pvalue <- rep(NA, nrow(pf$grid))
#
# df_fisher <- pf$grid
#
# # Tippett combining function
# pf$set_aggregator("tippett")
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_in
# )
# pf$evaluate_grid(grid = pf$grid, ncores = 1)
# grid_in <- pf$grid
# pf$set_grid(
# parameters = pf$parameters,
# npoints = ngrid_out
# )
# if (requireNamespace("interp", quietly = TRUE)) {
# Zout <- interp::interp(
# x = grid_in$delta,
# y = grid_in$log_rho,
# z = grid_in$pvalue,
# xo = sort(unique(pf$grid$delta)),
# yo = sort(unique(pf$grid$log_rho))
# )
# pf$grid$pvalue <- as.numeric(Zout$z)
# } else
# pf$grid$pvalue <- rep(NA, nrow(pf$grid))
#
# df_tippett <- pf$grid
## ---- fig.asp=0.85, fig.width=9, out.width="97%", dpi=300---------------------
df_fisher %>%
ggplot(aes(delta, log_rho, z = pvalue)) +
geom_contour_filled(binwidth = 0.05) +
labs(
title = "Contour plot of the p-value surface",
subtitle = "Using Fisher's non-parametric combination",
x = expression(delta),
y = expression(log(rho)),
fill = "p-value"
) +
theme_minimal()
## ---- fig.asp=0.85, fig.width=9, out.width="97%", dpi=300---------------------
df_tippett %>%
ggplot(aes(delta, log_rho, z = pvalue)) +
geom_contour_filled(binwidth = 0.05) +
labs(
title = "Contour plot of the p-value surface",
subtitle = "Using Tippett's non-parametric combination",
x = expression(delta),
y = expression(log(rho)),
fill = "p-value"
) +
theme_minimal()
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.