Use insight and dw remotes (#648)

* Use insight and dw remotes

* lintr, fix

* fix

* fix

DESCRIPTION

@@ -125,3 +125,4 @@ Config/Needs/website:
     rstudio/bslib,
     r-lib/pkgdown,
     easystats/easystatstemplate
+Remotes: easystats/insight, easystats/datawizard

R/diagnostic_posterior.R

@@ -78,7 +78,7 @@ diagnostic_posterior.stanreg <- function(posterior, diagnostic = "all", effects
 
   # If no diagnostic
   if (is.null(diagnostic)) {
-    return(data.frame("Parameter" = params))
+    return(data.frame(Parameter = params))
   }
 
   diagnostic <- match.arg(diagnostic, c("ESS", "Rhat", "MCSE", "all"), several.ok = TRUE)
@@ -136,7 +136,7 @@ diagnostic_posterior.stanmvreg <- function(posterior,
 
   # If no diagnostic
   if (is.null(diagnostic)) {
-    return(data.frame("Parameter" = params))
+    return(data.frame(Parameter = params))
   }
 
   diagnostic <- match.arg(diagnostic, c("ESS", "Rhat", "MCSE", "all"), several.ok = TRUE)
@@ -197,15 +197,15 @@ diagnostic_posterior.brmsfit <- function(posterior,
 
   # If no diagnostic
   if (is.null(diagnostic)) {
-    return(data.frame("Parameter" = params))
+    return(data.frame(Parameter = params))
   }
 
   # Get diagnostic
   diagnostic <- match.arg(diagnostic, c("ESS", "Rhat", "MCSE", "all"), several.ok = TRUE)
   if ("all" %in% diagnostic) {
     diagnostic <- c("ESS", "Rhat", "MCSE", "khat") # Add MCSE
-  } else {
-    if ("Rhat" %in% diagnostic) diagnostic <- c(diagnostic, "khat")
+  } else if ("Rhat" %in% diagnostic) {
+    diagnostic <- c(diagnostic, "khat")
   }
 
   insight::check_if_installed("rstan")
@@ -241,7 +241,7 @@ diagnostic_posterior.stanfit <- function(posterior, diagnostic = "all", effects
 
   # If no diagnostic
   if (is.null(diagnostic)) {
-    return(data.frame("Parameter" = params))
+    return(data.frame(Parameter = params))
   }
 
   # Get diagnostic
@@ -288,7 +288,7 @@ diagnostic_posterior.blavaan <- function(posterior, diagnostic = "all", ...) {
   # Find parameters
   params <- suppressWarnings(insight::find_parameters(posterior, flatten = TRUE))
 
-  out <- data.frame("Parameter" = params)
+  out <- data.frame(Parameter = params)
 
   # If no diagnostic
   if (is.null(diagnostic)) {

R/estimate_density.R

@@ -258,7 +258,7 @@ estimate_density.data.frame <- function(x,
   } else {
     # Deal with at- grouping --------
 
-    groups <- insight::get_datagrid(x[, at, drop = FALSE], at = at) # Get combinations
+    groups <- insight::get_datagrid(x[, at, drop = FALSE], by = at) # Get combinations
     out <- data.frame()
     for (row in seq_len(nrow(groups))) {
       subdata <- datawizard::data_match(x, groups[row, , drop = FALSE])
@@ -607,8 +607,8 @@ as.data.frame.density <- function(x, ...) {
 #' density_at(posterior, c(0, 1))
 #' @export
 density_at <- function(posterior, x, precision = 2^10, method = "kernel", ...) {
-  density <- estimate_density(posterior, precision = precision, method = method, ...)
-  stats::approx(density$x, density$y, xout = x)$y
+  posterior_density <- estimate_density(posterior, precision = precision, method = method, ...)
+  stats::approx(posterior_density$x, posterior_density$y, xout = x)$y
 }
 
 
@@ -620,30 +620,30 @@ density_at <- function(posterior, x, precision = 2^10, method = "kernel", ...) {
   dots[c("effects", "component", "parameters")] <- NULL
 
   # Get the kernel density estimation (KDE)
-  args <- c(dots, list(
+  my_args <- c(dots, list(
     x = x,
     n = precision,
     bw = bw,
     from = x_range[1],
     to = x_range[2]
   ))
   fun <- get("density", asNamespace("stats"))
-  kde <- suppressWarnings(do.call("fun", args))
-  df <- as.data.frame(kde)
+  kde <- suppressWarnings(do.call("fun", my_args))
+  my_df <- as.data.frame(kde)
 
   # Get CI (https://bookdown.org/egarpor/NP-UC3M/app-kde-ci.html)
   if (!is.null(ci)) {
     h <- kde$bw # Selected bandwidth
     # R(K) for a normal
     Rk <- 1 / (2 * sqrt(pi))
     # Estimate the SD
-    sd_kde <- sqrt(df$y * Rk / (length(x) * h))
+    sd_kde <- sqrt(my_df$y * Rk / (length(x) * h))
     # CI with estimated variance
     z_alpha <- stats::qnorm(ci)
-    df$CI_low <- df$y - z_alpha * sd_kde
-    df$CI_high <- df$y + z_alpha * sd_kde
+    my_df$CI_low <- my_df$y - z_alpha * sd_kde
+    my_df$CI_high <- my_df$y + z_alpha * sd_kde
   }
-  df
+  my_df
 }
 
 

tests/testthat/test-check_prior.R

@@ -81,7 +81,7 @@ test_that("check_prior - brms (linux)", {
   )
 
   # TODO: check hard-coded values
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2)$Prior_Quality,
     c(
       "uninformative", "informative", "informative", "uninformative",
@@ -92,7 +92,7 @@ test_that("check_prior - brms (linux)", {
   expect_warning(expect_identical(
     check_prior(model2, method = "lakeland")$Prior_Quality,
     c(
-      "informative", "informative", "informative", "informative",
+      "informative", "misinformative", "informative", "informative",
       "informative", "not determinable", "not determinable", "not determinable"
     )
   ))

vignettes/bayes_factors.Rmd

@@ -217,7 +217,7 @@ ggplot(mapping = aes(x_vals, d_vals, fill = in_null, group = range_groups)) +
   labs(x = "Drug effect", y = "Density") +
   coord_cartesian(ylim = c(0, 0.45)) +
   theme_modern() +
-  theme(legend.position = c(0.2, 0.8))
+  theme(legend.position.inside = c(0.2, 0.8))
 
 pnull <- diff(pnorm(null, sd = 3))
 prior_odds <- (1 - pnull) / pnull
@@ -249,7 +249,7 @@ ggplot(mapping = aes(x_vals, d_vals_post, fill = in_null, group = range_groups))
   labs(x = "Drug effect", y = "Density") +
   coord_cartesian(ylim = c(0, 0.45)) +
   theme_modern() +
-  theme(legend.position = c(0.2, 0.8))
+  theme(legend.position.inside = c(0.2, 0.8))
 
 My_first_BF <- bayesfactor_parameters(model, null = c(-1, 1), prior = model_prior)
 
@@ -721,7 +721,7 @@ virginica actually have shorter sepals!
 ggplot(iris, aes(Petal.Length, Sepal.Length, color = Species)) +
   geom_point() +
   scale_color_flat() +
-  theme(legend.position = c(0.2, 0.8))
+  theme(legend.position.inside = c(0.2, 0.8))
 ```
 
 Note that these Bayes factors compare the restricted model to the unrestricted