library(rstan)
## Loading required package: StanHeaders
##
## rstan version 2.32.6 (Stan version 2.32.2)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
## change `threads_per_chain` option:
## rstan_options(threads_per_chain = 1)
## Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file
library(loo)
## This is loo version 2.7.0
## - Online documentation and vignettes at mc-stan.org/loo
## - As of v2.0.0 loo defaults to 1 core but we recommend using as many as possible. Use the 'cores' argument or set options(mc.cores = NUM_CORES) for an entire session.
## - Windows 10 users: loo may be very slow if 'mc.cores' is set in your .Rprofile file (see https://github.com/stan-dev/loo/issues/94).
##
## Attaching package: 'loo'
## The following object is masked from 'package:rstan':
##
## loo
library(rstantools)
## This is rstantools version 2.4.0
library(rstanarm)
## Loading required package: Rcpp
## This is rstanarm version 2.32.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())
##
## Attaching package: 'rstanarm'
## The following object is masked from 'package:rstan':
##
## loo
library(shinystan)
## Loading required package: shiny
##
## This is shinystan version 2.6.0
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(bayesplot)
## This is bayesplot version 1.11.1
## - Online documentation and vignettes at mc-stan.org/bayesplot
## - bayesplot theme set to bayesplot::theme_default()
## * Does _not_ affect other ggplot2 plots
## * See ?bayesplot_theme_set for details on theme setting
library(Bayesrel)
RIASEC42 <- read.csv2("D:\\OneDrive - UMP\\R - lenh 2016\\RIASEC1308.csv", comment.char = "#", stringsAsFactors = TRUE)
RIASEC42 <- subset(RIASEC42, select = -c(id, gioi, lop))
res_bayes <- bomegas(RIASEC42, n.factors = 6, missing = "impute",
interval = .95,
n.iter = 1e4, n.burnin = 2e3, thin = 2)
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pOmegas(res_bayes, cutoff.t = .9, cutoff.h = .8)
## omega_t>0.9 omega_h>0.8
## prior_prob 0.085 0.056
## posterior_prob 1.000 1.000
set.seed(123)
# Phân tích bằng hàm bomegas với tham số trước thông tin chi tiết hơn
res_adj_prior <- bomegas(RIASEC42, n.factors = 6, missing = "impute",
interval = 0.95, n.iter = 1e4, n.burnin = 2e3, thin = 2,
l0 = 2, beta0 = 2, a0 = 10, b0 = 6, c0 = 10, d0 = 6)
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res_adj_prior
## Call:
## bomegas(data = RIASEC42, n.factors = 6, n.iter = 10000, n.burnin = 2000,
## thin = 2, interval = 0.95, missing = "impute", a0 = 10, b0 = 6,
## l0 = 2, c0 = 10, d0 = 6, beta0 = 2)
##
## posterior mean 95% CI lower 95% CI upper
## omega_t 0.92560 0.91973 0.93133
## omega_h 0.84683 0.83444 0.85932