Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(BayesLN)
## -----------------------------------------------------------------------------
# Load dataset
data("EPA09")
# Bayes estimator under relative quadratic loss and optimal prior setting
LN_Mean(x = EPA09, x_transf = FALSE, method = "optimal", CI = FALSE)
## -----------------------------------------------------------------------------
LN_Mean(x = EPA09, x_transf = FALSE, method = "weak_inf", alpha_CI = 0.05, type_CI = "UCL")
## -----------------------------------------------------------------------------
LN_Quant(x = EPA09, x_transf = FALSE, quant = 0.95, method = "optimal", CI = FALSE)
## -----------------------------------------------------------------------------
# Load data
data("fatigue")
# Design matrices
Xtot <- cbind(1, log(fatigue$stress), log(fatigue$stress)^2)
X <- Xtot[-c(1,13,22),]
y <- fatigue$cycle[-c(1,13,22)]
Xtilde <- Xtot[c(1,13,22),] # units to predict
#Estimation
LN_MeanReg(y = y,
X = X, Xtilde = Xtilde,
method = "weak_inf", y_transf = FALSE)
## -----------------------------------------------------------------------------
# Load the dataset included in the package
data("ReadingTime")
# Define data.frame containing the covariate patterns to investigate
data_pred_new <- expand.grid(so=c(-1,1), subj=factor(12), item=factor(8))
# Model estimation
Mod_est_RT <- LN_hierarchical(formula_lme = log_rt ~ so +(1|subj)+(1|item),
data_lme = ReadingTime, data_pred = data_pred_new,
functional = c("Marginal", "Subject"),
nsamp = 25000, burnin = 5000, n_thin = 5)
## -----------------------------------------------------------------------------
# Prior parameters
Mod_est_RT$par_prior
## ---- fig.width = 6.5---------------------------------------------------------
# coda package
library(coda)
# Traceplots model parameters
oldpar <- par(mfrow=c(2,3))
traceplot(Mod_est_RT$samples$par[, 1:6])
par(oldpar)
## -----------------------------------------------------------------------------
# Posterior summaries
Mod_est_RT$summaries$marg
Mod_est_RT$summaries$subj
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.