Nothing
## ----setup, include=FALSE-----------------------------------------------------
is_check <- ("CheckExEnv" %in% search()) ||
any(c("_R_CHECK_TIMINGS_", "_R_CHECK_LICENSE_") %in% names(Sys.getenv())) ||
!file.exists("../models/Introduction/fit_3.RDS")
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = !is_check,
dev = "png")
if(.Platform$OS.type == "windows"){
knitr::opts_chunk$set(dev.args = list(type = "cairo"))
}
print_conditional <- function(fit, coef = "delta"){
with(fit,
sprintf("%1$s, 95%% CI [%2$s, %3$s]",
format(round(mean(RoBTT$posterior[[coef]]), 2), nsmall = 3),
format(round(quantile(RoBTT$posterior[[coef]], 0.025), 2), nsmall = 2),
format(round(quantile(RoBTT$posterior[[coef]], 0.975), 2), nsmall = 2)
))
}
## ----include = FALSE, eval = FALSE--------------------------------------------
# # pre-fit all models (easier to update the code on package update)
# library(RoBTT)
#
# data("fertilization", package = "RoBTT")
#
# fit_01 <- RoBTT(
# x1 = fertilization$Self,
# x2 = fertilization$Crossed,
# parallel = TRUE,
# prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
# prior_rho = NULL, # this indicates no prior on the variance allocation factor -> equal variance test
# prior_nu = NULL, # this indicates no prior on the degrees of freedom -> normal distribution test
# seed = 0
# )
# fit_10 <- RoBTT(
# x1 = fertilization$Self,
# x2 = fertilization$Crossed,
# parallel = TRUE,
# prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
# prior_rho = prior("beta", list(3, 3)), #prior on variance allocation
# prior_rho_null = NULL, # remove models assuming equal variance
# prior_nu = NULL,
# seed = 0
# )
# fit_1 <- RoBTT(
# x1 = fertilization$Self,
# x2 = fertilization$Crossed,
# parallel = TRUE,
# prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
# prior_rho = prior("beta", list(3, 3)),
# prior_nu = NULL,
# seed = 1,
# control = set_control(adapt_delta = 0.95)
# )
# fit_2 <- RoBTT(
# x1 = fertilization$Self,
# x2 = fertilization$Crossed,
# parallel = TRUE,
# prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
# prior_rho = prior("beta", list(3, 3)),
# prior_nu = prior("exp", list(1)), # prior on degrees of freedom
# seed = 2
# )
# fit_3 <- RoBTT(
# x1 = fertilization$Self,
# x2 = fertilization$Crossed,
# parallel = TRUE,
# prior_delta = prior("cauchy", list(0, 1/sqrt(2)), list(0, Inf)),
# prior_rho = prior("beta", list(3, 3)),
# prior_nu = prior("exp", list(1)),
# prior_delta_null = prior("normal", list(0, 0.15), list(0, Inf)), #prior distribution and truncation
# seed = 3
# )
#
# saveRDS(fit_01, file = "../models/Introduction/fit_01.RDS")
# saveRDS(fit_10, file = "../models/Introduction/fit_10.RDS")
# saveRDS(fit_1, file = "../models/Introduction/fit_1.RDS")
# saveRDS(fit_2, file = "../models/Introduction/fit_2.RDS")
# saveRDS(fit_3, file = "../models/Introduction/fit_3.RDS")
## ----include = FALSE----------------------------------------------------------
# pre-load the fitted models to save time on compilation
library(RoBTT)
fit_01 <- readRDS(file = "../models/Introduction/fit_01.RDS")
fit_10 <- readRDS(file = "../models/Introduction/fit_10.RDS")
fit_1 <- readRDS(file = "../models/Introduction/fit_1.RDS")
fit_2 <- readRDS(file = "../models/Introduction/fit_2.RDS")
fit_3 <- readRDS(file = "../models/Introduction/fit_3.RDS")
## -----------------------------------------------------------------------------
library(RoBTT)
data("fertilization", package = "RoBTT")
head(fertilization)
## -----------------------------------------------------------------------------
# get overall summary
summary(fit_01)
## -----------------------------------------------------------------------------
# get individual model summaries
summary(fit_01, type = "models")
## -----------------------------------------------------------------------------
summary(fit_01, conditional = TRUE)
## -----------------------------------------------------------------------------
summary(fit_10)
## -----------------------------------------------------------------------------
summary(fit_10, type = "models")
## -----------------------------------------------------------------------------
summary(fit_1)
## -----------------------------------------------------------------------------
summary(fit_1, type = "models")
## -----------------------------------------------------------------------------
summary(fit_2, type = "models")
## -----------------------------------------------------------------------------
summary(fit_3, type = "models")
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