diff --git a/.Rhistory b/.Rhistory
index c2643fe..243847d 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,512 +1,512 @@
-rpois(n, lambda) + rbinom(n, size = 1, prob = phi)
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-n <- 1000
-phi <- 0.2
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <- anova_bnp_berpoi(yvec, Xmat, iter = 20000L, warmup = 10000L)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-devtools::load_all(".")
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
-ANOVABNPTestR::setup()
-options(download.file.method = "wininet")
-ANOVABNPTestR::setup()
-1+1
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
-options(download.file.method = "wininet")
-ANOVABNPTestR::setup()
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+setup()
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+setup()
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
-options(download.file.method = "wininet")
-ANOVABNPTestR::setup()
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
 devtools::load_all(".")
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
-ANOVABNPTestR::setup()
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
-# Probability mass function Bernoulli Poisson distribution
-dberpo <- function(x, phi, lambda) {
-phi * dpois(x - 1, lambda) + (1 - phi) * dpois(x, lambda)
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
 }
-# Generates random draws from a Bernoulli Poisson distribution
-rberpo <- function(n, phi, lambda) {
-rpois(n, lambda) + rbinom(n, size = 1, prob = phi)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should be a straight line.
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
 }
-n <- 1000
-phi <- 0.2
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <- anova_bnp_berpoi(yvec, Xmat, iter = 20000L, warmup = 10000L)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-n <- 1000
-phi <- 0.8
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <- anova_bnp_berpoi(yvec, Xmat, iter = 20000L, warmup = 10000L)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-n <- 2000
-phi <- 0.8
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <- anova_bnp_berpoi(yvec, Xmat, iter = 20000L, warmup = 10000L)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-n <- 10000
-phi <- 0.8
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <- anova_bnp_berpoi(yvec, Xmat, iter = 20000L, warmup = 10000L)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-library(ANOVABNPTestR)
-library(dplyr)
-library(ggplot2)
-library(tidyr)
-# Probability mass function Bernoulli Poisson distribution
-dberpo <- function(x, phi, lambda) {
-phi * dpois(x - 1, lambda) + (1 - phi) * dpois(x, lambda)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should be a straight line.
+shift_plot(fit, group = 3) # this should look like an inverted tan().
+predictive_plot_simple(fit)
+predictive_plot_simple(fit, 2)
+predictive_plot_simple(fit, 3)
+predictive_plot_simple(fit, 1)
+predictive_plot_simple(fit, 4)
+predictive_plot_simple(fit, 1)
+predictive_plot_simple(fit, 2)
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
 }
-# Generates random draws from a Bernoulli Poisson distribution
-rberpo <- function(n, phi, lambda) {
-rpois(n, lambda) + rbinom(n, size = 1, prob = phi)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should be a straight line.
+shift_plot(fit, group = 3) # this should look like an inverted tan().
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should be a straight line.
+devtools::load_all(".")
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
 }
-n <- 10000
-phi <- 0.8
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = mean(yvec),
-b1 = 1,
-alpha0 = 1.0,
-beta0 = 1.0,
-lb = 0,
-ub = 10
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = mean(yvec),
-b1 = 1,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-n <- 10000
-phi <- 0.8
-lambda <- 0.5
-yvec <- rberpo(n, phi, lambda)
-Xmat <- matrix(1L, n, 1)
-hist(yvec)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = numeric(mean(yvec)),
-b1 = numeric(1),
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = numeric(mean(yvec)) + 1,
-b1 = numeric(1),
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = numeric(mean(yvec)) + 1,
-b1 = numeric(1),
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-?anova_bnp_berpoi
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 1,
-b = 1,
-a1 = numeric(mean(yvec)) + 1,
-b1 = numeric(1),
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 1,
-b = 1,
-a1 = round(mean(yvec)) + 1,
-b1 = 1L,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 1,
-b = 1,
-a1 = mean(yvec),
-b1 = 1,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 1.0,
-b = 1.0,
-a1 = 1.0,
-b1 = 1.0,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = 1.0,
-b1 = 1.0,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
-fit$f_post |>
-dplyr::rename(fh = f) |>
-dplyr::mutate(f0 = dberpo(y, phi, lambda)) |>
-tidyr::pivot_longer(cols = fh:f0) |>
-ggplot2::ggplot(
-ggplot2::aes(x = y, color = name, y = value)
-) +
-ggplot2::geom_line()
-fit <-
-anova_bnp_berpoi(
-yvec,
-Xmat,
-iter = 2000L,
-warmup = 1000L,
-rho = 1.0,
-a = 0.1,
-b = 0.1,
-a1 = mean(yvec),
-b1 = 1.0,
-alpha0 = 1.0,
-beta0 = 1.0,
-)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
-setup()
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+# yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+yvec <- rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-hypothesis_post_simple(fit)
-hypothesis_post_interaction(fit)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-hypothesis_post_simple(fit)
-yvec <- rnorm(N)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-fit$shift_post
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+shift_post(fit)
+devtools::load_all(".")
+devtools::load_all(".")
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-tbl_shift <- shift_post(fit)
-tbl_shift
-tbl_shift
-# tbl_shift |>
-group_codes(fit)
-# tbl_shift |>
-tbl_shift |>
-dplyr::mutate(group = paste("group:", group))
-# tbl_shift |>
-group <- 2
-tbl_shift |>
-dplyr::filter(group == {{ group }}) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-predictive_plot_simple(fit, 1)
-tbl_shift |>
-dplyr::filter(group == {{ group }}) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-x <- sample(1:3, N, replace = TRUE)
-x
-shift_plot <- function(fit, group) {
-tbl_shift |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes_string(x = "y", y = "shift")) +
-ggplot2::geom_line()
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot <- function(fit, group) {
-tbl_shift |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes_string(x = "y", y = "shift")) +
-ggplot2::geom_line()
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
-shift_plot <- function(fit, group) {
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes_string(x = "y", y = "shift")) +
-ggplot2::geom_line()
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
-shift_plot <- function(fit, group) {
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
-a <- shift_plot(fit, group = 2)
-a
-a
-shift_plot <- function(fit, group) {
-out <-
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-return(out)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
-shift_plot <- function(fit, group) {
-out <-
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-return(out)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) #|>
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) #+
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-a <- fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-a
-a <- fit |>
-shift_post() |>
-dplyr::filter(group == 2) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-a
-fit |>
-shift_post() |>
-dplyr::filter(group == 2) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-fit$shift_post |>
-dplyr::filter(group == 2) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
 devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-tbl_shift <- shift_post(fit)
-hypothesis_post_simple(fit)
-hypothesis_post_interaction(fit)
-# tbl_shift |>
-group <- 2
-tbl_shift |>
-dplyr::filter(group == {{ group }}) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-fit$shift_post |>
-dplyr::filter(group == 2) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-shift_plot <- function(fit, group) {
-out <-
-fit |>
-shift_post() |>
-dplyr::filter(.data$group == group) |>
-ggplot2::ggplot(ggplot2::aes(x = y, y = shift)) +
-ggplot2::geom_line()
-return(out)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Simulate a sample
+{
+N <- 1000
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
+Xmat <- matrix(x, nrow = N, ncol = 2)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
 }
-shift_plot(fit, group = 2)
+# Fit our ANOVA BNP model
+fit <- anova_bnp_normal(yvec, Xmat)
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
 devtools::load_all(".")
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+devtools::load_all(".")
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
 devtools::load_all(".")
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+# Simulate a sample
+{
 N <- 1000
-x <- as.integer(1 + (runif(2 * N) < 0.5))
+d <- sample(c(-1, +1), 1 * N, replace = TRUE, prob = c(0.3, 0.7))
+x <- sample(c(1L, 2L), 2 * N, replace = TRUE)
 Xmat <- matrix(x, nrow = N, ncol = 2)
-yvec <- rnorm(N)
+gvec1 <- Xmat[, 1]
+gvec2 <- Xmat[, 2]
+yvec <- 1.2 * d * (gvec1 == 1) * (gvec2 == 2) + rnorm(N) / 2
+}
+# Fit our ANOVA BNP model
 fit <- anova_bnp_normal(yvec, Xmat)
-shift_plot(fit, group = 2)
-# Deploy locally
-devtools::document()
-pkgdown::build_site()
-library(ANOVABNPTestR)
-# Deploy locally
-devtools::document()
-pkgdown::build_site()
-# Deploy locally
-devtools::document()
-pkgdown::build_site()
+# Plot the shift function for the second cell
+shift_plot(fit, group = 2) # this should look like an inverted tan().
+shift_plot(fit, group = 3) # this should be a straight line.
diff --git a/extra/demo_normal.R b/extra/demo_normal.R
index f419d04..c49e44c 100644
--- a/extra/demo_normal.R
+++ b/extra/demo_normal.R
@@ -13,5 +13,6 @@
 fit <- anova_bnp_normal(yvec, Xmat)
 
 # Plot the shift function for the second cell
-shift_plot(fit, group = 2) # this should look like a tan().
+shift_plot(fit, group = 2) # this should look like an sigmoid
 shift_plot(fit, group = 3) # this should be a straight line.
+