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fix examples #309

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Oct 8, 2023
Merged

fix examples #309

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8563d15
fix examples
strengejacke Oct 7, 2023
a23ac81
fix vignette
strengejacke Oct 7, 2023
ab8076b
verbose = FALSE
strengejacke Oct 7, 2023
48c7d9e
remove annoying example
strengejacke Oct 7, 2023
9d8c776
lints
IndrajeetPatil Oct 7, 2023
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4 changes: 0 additions & 4 deletions R/data_plot.R
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Expand Up @@ -67,10 +67,6 @@
#' x <- hdi(model)
#' plot(x) + theme_modern()
#'
#' data <- rnorm(1000, 1)
#' x <- p_direction(data)
#' plot(x)
#'
#' x <- p_direction(model, verbose = FALSE)
#' plot(x)
#'
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14 changes: 7 additions & 7 deletions R/plot.check_outliers.R
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Expand Up @@ -53,7 +53,7 @@
colors = colors
)
} else {
if (length(methods == 1)) {

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file=R/plot.check_outliers.R,line=56,col=9,[length_test_linter] Checking the length of a logical vector is likely a mistake. Did you mean `length(methods) == 1`?
.plot_diag_outliers(x, show_labels = show_labels, size_text = size_text, rescale_distance = rescale_distance)
} else {
.plot_outliers_multimethod(x, rescale_distance)
Expand Down Expand Up @@ -97,13 +97,13 @@
d$Id[d$Outliers == "0"] <- NA

method <- switch(attr(x, "method", exact = TRUE),
"cook" = "Cook's Distance",
"pareto" = "Pareto",
"mahalanobis" = "Mahalanobis Distance",
"ics" = "Invariant Coordinate Selection",
"mcd" = "Minimum Covariance Determinant",
"optics" = "OPTICS",
"iforest" = "Isolation Forest",
cook = "Cook's Distance",
pareto = "Pareto",
mahalanobis = "Mahalanobis Distance",
ics = "Invariant Coordinate Selection",
mcd = "Minimum Covariance Determinant",
optics = "OPTICS",
iforest = "Isolation Forest",
"Cook's Distance"
)

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2 changes: 1 addition & 1 deletion R/plot.check_outliers_new.R
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Expand Up @@ -44,7 +44,7 @@
se = FALSE,
color = colors[1]
) +
scale_colour_manual(values = c("OK" = colors[2], "Influential" = colors[3])) +
scale_colour_manual(values = c(OK = colors[2], Influential = colors[3])) +
(if (isTRUE(show_labels)) {
if (requireNamespace("ggrepel", quietly = TRUE)) {
ggrepel::geom_text_repel(
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8 changes: 4 additions & 4 deletions R/plot.compare_performance.R
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Expand Up @@ -30,7 +30,7 @@
}

# remove indices with missing value, comparison makes no sense here
x <- x[vapply(x, function(.x) !anyNA(.x), logical(1))]

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file=R/plot.compare_performance.R,line=33,col=20,[unnecessary_lambda_linter] Pass anyNA directly as a symbol to vapply() instead of wrapping it in an unnecessary anonymous function. For example, prefer lapply(DF, sum) to lapply(DF, function(x) sum(x)).

x <- .reshape_to_long(x, names_to = "name", columns = 2:ncol(x))
x$name <- factor(x$name, levels = unique(x$name))
Expand All @@ -40,10 +40,10 @@
dataplot$values[dataplot$values > 1] <- 1

attr(dataplot, "info") <- list(
"xlab" = "",
"ylab" = "",
"title" = "Comparison of Model Indices",
"legend_color" = "Models"
xlab = "",
ylab = "",
title = "Comparison of Model Indices",
legend_color = "Models"
)

class(dataplot) <- c("data_plot", "see_compare_performance", "data.frame")
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2 changes: 1 addition & 1 deletion R/plot.describe_distribution.R
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Expand Up @@ -137,7 +137,7 @@
}
}

if (!is.null(x$highlight)) {

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file=R/plot.describe_distribution.R,line=140,col=7,[if_not_else_linter] In a simple if/else statement, prefer `if (A) x else y` to the less-readable `if (!A) y else x`.
p <- ggplot(x, aes(x = .data$x, fill = highlight))
} else {
p <- ggplot(x, aes(x = .data$x))
Expand Down Expand Up @@ -199,7 +199,7 @@
}

names(highlight_color) <- highlight
highlight_color <- c(highlight_color, "no_highlight" = "grey70")
highlight_color <- c(highlight_color, no_highlight = "grey70")

p <- p +
scale_fill_manual(values = highlight_color) +
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12 changes: 6 additions & 6 deletions R/plot.estimate_contrasts.R
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Expand Up @@ -16,9 +16,9 @@ data_plot.estimate_contrasts <- function(x, data = NULL, ...) {
dataplot <- .data_contrasts_and_means(x, means, x_name = x_name, y_name = y_name)

attr(dataplot, "info") <- list(
"xlab" = x_name,
"ylab" = y_name,
"title" = paste0("Estimated ", y_name, "s and Contrasts")
xlab = x_name,
ylab = y_name,
title = paste0("Estimated ", y_name, "s and Contrasts")
)

class(dataplot) <- c("data_plot", "see_estimate_contrasts", class(dataplot))
Expand All @@ -45,9 +45,9 @@ data_plot.estimate_contrasts <- function(x, data = NULL, ...) {
polygons <- merge(polygons, data_means[c("Level2", "Mean2")], by = "Level2")

polygons <- rbind(
cbind(polygons, data.frame("x" = polygons$Level1, "y" = polygons$Mean1)),
cbind(polygons, data.frame("x" = polygons$Level2, "y" = polygons$Mean1 - polygons$CI_low)),
cbind(polygons, data.frame("x" = polygons$Level2, "y" = polygons$Mean1 - polygons$CI_high))
cbind(polygons, data.frame(x = polygons$Level1, y = polygons$Mean1)),
cbind(polygons, data.frame(x = polygons$Level2, y = polygons$Mean1 - polygons$CI_low)),
cbind(polygons, data.frame(x = polygons$Level2, y = polygons$Mean1 - polygons$CI_high))
)

list(
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10 changes: 5 additions & 5 deletions R/plot.estimate_density.R
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Expand Up @@ -51,11 +51,11 @@ data_plot.estimate_density <- function(x,

attr(dataplot, "summary") <- summary
attr(dataplot, "info") <- list(
"xlab" = "Values",
"ylab" = "Density",
"legend_fill" = "Parameter",
"legend_color" = "Parameter",
"title" = "Estimated Density Function"
xlab = "Values",
ylab = "Density",
legend_fill = "Parameter",
legend_color = "Parameter",
title = "Estimated Density Function"
)

class(dataplot) <- c("data_plot", "see_estimate_density", class(dataplot))
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8 changes: 4 additions & 4 deletions R/plot.hdi.R
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Expand Up @@ -32,7 +32,7 @@
plot_title <- "Credible Interval (CI)"
}

if (!is.null(parms)) {

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file=R/plot.hdi.R,line=35,col=7,[if_not_else_linter] In a simple if/else statement, prefer `if (A) x else y` to the less-readable `if (!A) y else x`.
params <- parms
} else {
params <- NULL
Expand All @@ -59,13 +59,13 @@
data <- data[levels_order]
dataplot <- data.frame()
for (i in names(data)) {
if (!is.null(params)) {

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file=R/plot.hdi.R,line=62,col=11,[if_not_else_linter] In a simple if/else statement, prefer `if (A) x else y` to the less-readable `if (!A) y else x`.
dataplot <- rbind(
dataplot,
cbind(
.compute_densities_hdi(data[[i]], hdi = as.data.frame(x[x$Parameter == i, ]), name = i),
"Effects" = params$Effects[params$Parameter == i],

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file=R/plot.hdi.R,line=67,col=13,[keyword_quote_linter] Only quote named arguments to functions if necessary, i.e., if the name is not a valid R symbol (see ?make.names).
"Component" = params$Component[params$Parameter == i]

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file=R/plot.hdi.R,line=68,col=13,[keyword_quote_linter] Only quote named arguments to functions if necessary, i.e., if the name is not a valid R symbol (see ?make.names).
)
)
} else {
Expand Down Expand Up @@ -121,10 +121,10 @@
dataplot <- .fix_facet_names(dataplot)

attr(dataplot, "info") <- list(
"xlab" = "Possible parameter values",
"ylab" = ylab,
"legend_fill" = legend_title,
"title" = plot_title
xlab = "Possible parameter values",
ylab = ylab,
legend_fill = legend_title,
title = plot_title
)

class(dataplot) <- c("data_plot", "see_hdi", class(dataplot))
Expand All @@ -140,8 +140,8 @@

out <- .as.data.frame_density(stats::density(x))

out$HDI_low <- sapply(out$x, .classify_hdi, hdi$CI_low, c(100, 100 * hdi$CI))

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file=R/plot.hdi.R,line=143,col=18,[undesirable_function_linter] Function "sapply" is undesirable.
out$HDI_high <- sapply(out$x, .classify_hdi, rev(hdi$CI_high), c(rev(100 * hdi$CI), 100))

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file=R/plot.hdi.R,line=144,col=19,[undesirable_function_linter] Function "sapply" is undesirable.
out$fill <- as.factor(pmax(out$HDI_low, out$HDI_high))
out$height <- out$y
out$y <- name
Expand All @@ -149,7 +149,7 @@
levels(out$fill) <- sprintf("%s%%", levels(out$fill))

# normalize
out$height <- as.vector((out$height - min(out$height, na.rm = TRUE)) / diff(range(out$height, na.rm = TRUE), na.rm = TRUE))

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file=R/plot.hdi.R,line=152,col=121,[line_length_linter] Lines should not be more than 120 characters. This line is 125 characters.

out
}
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4 changes: 0 additions & 4 deletions man/data_plot.Rd
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12 changes: 9 additions & 3 deletions vignettes/bayestestR.Rmd
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Expand Up @@ -66,10 +66,10 @@ theme_set(theme_modern())
```{r}
set.seed(123)
# model with fixed effects only
model <- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0)
model <<- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0)

# model with fixed and random effects as well as zero-inflation component
model2 <- insight::download_model("brms_zi_3")
model2 <<- insight::download_model("brms_zi_3")
```

## Density Estimation
Expand Down Expand Up @@ -335,7 +335,13 @@ plot(result) +
```

```{r fig.width=11, fig.height=9}
result <- equivalence_test(model2, ci = c(0.89, 0.95), effects = "all", component = "all")
result <- equivalence_test(
model2,
ci = c(0.89, 0.95),
effects = "all",
component = "all",
verbose = FALSE
)

result

Expand Down
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