Nothing
## ---- setup, echo = F---------------------------------------------------------
knitr::opts_chunk$set(fig.width = 6, fig.height = 6, eval = FALSE, echo = TRUE)
## ---- echo = FALSE------------------------------------------------------------
# set.seed(2020)
## ---- eval = TRUE-------------------------------------------------------------
dt <- bsem::simdata(
paths = NULL,
exogenous = NULL
)
## ---- eval = TRUE-------------------------------------------------------------
dt$blocks
## -----------------------------------------------------------------------------
# cfa <- bsem::sem(
# data = dt$data,
# blocks = dt$blocks,
# cores = 1
# )
## -----------------------------------------------------------------------------
# cfa
## -----------------------------------------------------------------------------
# plot(cfa)
## -----------------------------------------------------------------------------
# gridExtra::grid.arrange(
# bsem::arrayplot(cfa$mean_lambda, main = "estimates", -3,3),
# bsem::arrayplot(dt$real$lambda, main = "lambda (scores)", -3,3)
# )
#
# gridExtra::grid.arrange(
# bsem::arrayplot(cfa$mean_alpha, main = "estimates", -3,3),
# bsem::arrayplot(dt$real$alpha, main = "alpha (loadings)", -3,3),
# layout_matrix = matrix(c(1, 1, 2, 2), ncol = 2)
# )
## ---- eval = TRUE-------------------------------------------------------------
dt <- bsem::simdata()
## -----------------------------------------------------------------------------
# sem <- bsem::sem(
# data = dt$data,
# blocks = dt$blocks,
# paths = dt$paths,
# exogenous = dt$exogenous
# )
## -----------------------------------------------------------------------------
# plot(sem)
## -----------------------------------------------------------------------------
# names(cfa$posterior)
# dim(cfa$posterior$alpha)
## -----------------------------------------------------------------------------
# lnames <- rownames(cfa$mean_alpha)
#
# find <- paste0("alpha[", which(lnames %in% unlist(cfa$blocks[1:length(cfa$blocks)])), ",",
# rep(1:length(cfa$blocks), lengths(cfa$blocks)), "]"
# )
# bayesplot::mcmc_trace(cfa$posterior$alpha[, , find]
# )
## -----------------------------------------------------------------------------
# lnames <- rownames(cfa$mean_alpha)
#
# find <- paste0("alpha[", which(lnames %in% unlist(cfa$blocks[1:length(cfa$blocks)])), ",",
# rep(1:length(cfa$blocks), lengths(cfa$blocks)), "]"
# )
# bayesplot::mcmc_dens(cfa$posterior$alpha[, , find]
# )
## -----------------------------------------------------------------------------
# names(cfa$credint)
## -----------------------------------------------------------------------------
# library("ggplot2")
# library("tidybayes")
## -----------------------------------------------------------------------------
# dt <- data.frame(
# li = cfa$credint$alpha[, 1],
# lu = cfa$credint$alpha[, 2],
# m = c(cfa$mean_alpha)
# )
## -----------------------------------------------------------------------------
# lnames <- rownames(cfa$mean_alpha)
# snames <- rownames(cfa$mean_lambda)
## -----------------------------------------------------------------------------
# find <- paste0("alpha[", which(lnames %in% unlist(cfa$blocks)), ",", rep(1:length(cfa$blocks), lengths(cfa$blocks)), "]")
#
# dt <- dt[find, ]
## -----------------------------------------------------------------------------
# ggplot(aes(y = find, x = m, xmin = li, xmax = lu), data = dt) +
# geom_pointintervalh() +
# theme_classic() +
# labs(
# title = paste("Latent variable", colnames(cfa$mean_alpha)[3]),
# x = "Highest posterior density interval",
# y = "variable"
# )
## -----------------------------------------------------------------------------
# library("bayesplot")
## -----------------------------------------------------------------------------
# find <- paste0("alpha[", which(lnames %in% unlist(cfa$blocks)), ",",
# rep(1:length(cfa$blocks), lengths(cfa$blocks)), "]")
#
# dt <- dt[find, ]
## -----------------------------------------------------------------------------
# gridExtra::grid.arrange(mcmc_areas(cfa$posterior$alpha[, , find]),
# mcmc_intervals(cfa$posterior$alpha[, , find]),
# layout_matrix = matrix(c(1, 1, 2, 2), ncol = 2)
# )
## -----------------------------------------------------------------------------
# bsem::runShiny()
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