Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = TRUE
)
## ----setup--------------------------------------------------------------------
library(bayesplay)
## -----------------------------------------------------------------------------
norm_mod <- likelihood(family = "normal", mean = 5.5, sd = 32.35)
norm_mod
## -----------------------------------------------------------------------------
plot(norm_mod)
## -----------------------------------------------------------------------------
t_mod <- likelihood(family = "student_t", mean = 10, sd = 5, df = 15)
t_mod
## -----------------------------------------------------------------------------
plot(t_mod)
## -----------------------------------------------------------------------------
point_prior <- prior(family = "point", point = 0)
plot(point_prior)
## -----------------------------------------------------------------------------
uniform_prior <- prior(family = "uniform", min = 10, max = 20)
plot(uniform_prior)
## -----------------------------------------------------------------------------
normal_prior <- prior(family = "normal", mean = 10, sd = 10)
plot(normal_prior)
## -----------------------------------------------------------------------------
half_normal_prior <- prior(family = "normal", mean = 10, sd = 10, range = c(10, Inf))
plot(half_normal_prior)
## -----------------------------------------------------------------------------
library(ggplot2)
## -----------------------------------------------------------------------------
plot(t_mod) + labs(x = "data", y = "likelihood", title = "t likelihood")
## -----------------------------------------------------------------------------
plot(uniform_prior) + xlim(-100,100)
## -----------------------------------------------------------------------------
plot(norm_mod) +
labs(title = "normal likelihood") +
theme_linedraw()
## -----------------------------------------------------------------------------
data_model <- likelihood("noncentral_d", d = 0, n = 20)
d_model1 <- extract_predictions(data_model * prior("cauchy", 0, .707))
d_model2 <- extract_predictions(data_model * prior("point", 0))
visual_compare(d_model1, d_model2)
## -----------------------------------------------------------------------------
plot(extract_posterior(data_model * prior("cauchy", 0, .707)), add_prior = TRUE)
## -----------------------------------------------------------------------------
visual_compare(d_model1, d_model2) +
scale_colour_manual(values = c("green", "blue"),
labels = c("d_model1", "d_model2"), name = "Model")
## -----------------------------------------------------------------------------
plot(extract_posterior(data_model * prior("cauchy", 0, .707)), add_prior = TRUE) +
scale_colour_manual(values = c("green", "blue"),
labels = c("posterior", "prior"), name = NULL)
## -----------------------------------------------------------------------------
visual_compare(d_model1, d_model2) +
scale_linetype_manual(values = c(1, 2),
labels = c("d_model1", "d_model2"), name = "Model")
## -----------------------------------------------------------------------------
plot(extract_posterior(data_model * prior("cauchy", 0, .707)), add_prior = TRUE) +
scale_linetype_manual(values = c(1, 2),
labels = c("posterior", "prior"), name = NULL)
## -----------------------------------------------------------------------------
visual_compare(d_model1, d_model2) +
scale_linetype_manual(values = c(1, 2),
labels = c("d_model1", "d_model2"), name = NULL) +
scale_colour_manual(values = c("grey", "black"),
labels = c("d_model1", "d_model2"), name = NULL)
## -----------------------------------------------------------------------------
plot(extract_posterior(data_model * prior("cauchy", 0, .707)), add_prior = TRUE) +
scale_linetype_manual(values = c(1, 2),
labels = c("posterior", "prior"), name = NULL) +
scale_colour_manual(values = c("black", "black"),
labels = c("posterior", "prior"), name = NULL)
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