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plot_point_estimates <- function(model,
dat,
tf,
title,
axis.labels,
axis.title,
axis.lim,
grid.breaks,
show.values,
value.offset,
geom.size,
line.size,
geom.colors,
bpe.style,
bpe.color,
vline.color,
value.size,
facets,
ci.style,
...) {
# some defaults...
size.inner <- .07
spacing <- .4
width <- if (is.stan(model)) .06 else 0
# check additional arguments, for stan-geoms
add.args <- lapply(match.call(expand.dots = F)$`...`, function(x) x)
if ("size.inner" %in% names(add.args)) size.inner <- eval(add.args[["size.inner"]])
if ("width" %in% names(add.args)) width <- eval(add.args[["width"]])
if ("spacing" %in% names(add.args)) spacing <- eval(add.args[["spacing"]])
# need some additional data, for stan-geoms
dat$xpos <- sjlabelled::as_numeric(dat$term, start.at = 1)
dat$xmin <- dat$xpos - (geom.size * size.inner)
dat$xmax <- dat$xpos + (geom.size * size.inner)
# set default for empty titles/labels
if (sjmisc::is_empty(title)) title <- NULL
if (sjmisc::is_empty(axis.labels)) axis.labels <- attributes(dat)$pretty_names
if (sjmisc::is_empty(axis.title)) axis.title <- NULL
# if we have non-estimable coefficients (i.e. missings)
# remove them here
no_coefficient <- which(is.na(dat$estimate))
if (length(no_coefficient) > 0) {
dat <- dat[-no_coefficient, ]
}
# axis limits and tick breaks for y-axis
axis.scaling <- axis_limits_and_ticks(
axis.lim = axis.lim,
min.val = min(dat$conf.low),
max.val = max(dat$conf.high),
grid.breaks = grid.breaks,
exponentiate = isTRUE(tf == "exp"),
min.est = min(dat$estimate),
max.est = max(dat$estimate)
)
# based on current ggplot theme, highlights vertical default line
yintercept = dplyr::if_else(isTRUE(tf == "exp"), 1, 0)
layer_vertical_line <- geom_intercept_line(yintercept, axis.scaling, vline.color)
# check whether we have a multinomial log. reg. model
multinomial <- obj_has_name(dat, "response.level")
# basis aes mapping
if (multinomial)
p <- ggplot(dat, aes_string(x = "term", y = "estimate", colour = "response.level", fill = "response.level"))
else
p <- ggplot(dat, aes_string(x = "term", y = "estimate", colour = "group", fill = "group"))
if (is.stan(model)) {
if (ci.style == "whisker") {
hdi_alpha <- 1
dot.fac <- 1.2
} else {
hdi_alpha <- .5
dot.fac <- 3
}
# special setup for rstan-models
p <- p + layer_vertical_line
if (ci.style == "whisker")
p <- p + geom_errorbar(aes_string(ymin = "conf.low", ymax = "conf.high"), size = line.size, width = width)
else
p <- p + geom_rect(aes_string(ymin = "conf.low", ymax = "conf.high", xmin = "xmin", xmax = "xmax"), alpha = hdi_alpha, colour = "white", size = .5)
# only add inner region if requested
if (size.inner > 0) {
p <- p +
geom_rect(aes_string(ymin = "conf.low50", ymax = "conf.high50", xmin = "xmin", xmax = "xmax"), alpha = hdi_alpha, colour = "white", size = .5)
}
# define style for Bayesian point estimate
if (bpe.style == "line") {
if (is.null(bpe.color)) {
p <- p +
geom_segment(aes_string(x = "xmin", xend = "xmax", y = "estimate", yend = "estimate"), size = geom.size * .9)
} else {
p <- p +
geom_segment(aes_string(x = "xmin", xend = "xmax", y = "estimate", yend = "estimate"), colour = bpe.color, size = geom.size * .9)
}
} else if (is.null(bpe.color)) {
p <- p +
geom_point(aes_string(y = "estimate"), fill = "white", size = geom.size * dot.fac)
} else {
p <- p +
geom_point(aes_string(y = "estimate"), fill = "white", colour = bpe.color, size = geom.size * dot.fac)
}
} else {
# setup base plot
p <- p + layer_vertical_line
if (multinomial) {
p <- p +
geom_point(size = geom.size, position = position_dodge(width = spacing)) +
geom_errorbar(aes_string(ymin = "conf.low", ymax = "conf.high"), position = position_dodge(width = spacing), width = width, size = line.size)
} else {
p <- p +
geom_point(size = geom.size) +
geom_errorbar(aes_string(ymin = "conf.low", ymax = "conf.high"), width = width, size = line.size)
}
}
# set up base aes, either with or w/o groups
p <- p + coord_flip()
if (multinomial) {
col.len <- dplyr::n_distinct(dat$response.level)
# remove legend
p <- p + guides(fill = "none")
} else {
col.len <- dplyr::n_distinct(dat$group)
# remove legend
p <- p + guides(colour = "none", fill = "none")
}
# add value labels
if (show.values) p <- p +
geom_text(
aes_string(label = "p.label"),
nudge_x = value.offset,
show.legend = FALSE,
size = value.size
)
# set axis labels
if (!is.null(axis.labels))
p <- p + scale_x_discrete(labels = axis.labels)
# we need transformed scale for exponentiated estimates
has_zeroinf <- (obj_has_name(dat, "wrap.facet") && dplyr::n_distinct(dat$wrap.facet, na.rm = TRUE) > 1)
if (isTRUE(tf == "exp")) {
if (has_zeroinf) {
p <- p + scale_y_continuous(trans = "log10")
} else {
p <- p + scale_y_continuous(
trans = "log10",
limits = axis.scaling$axis.lim,
breaks = axis.scaling$ticks,
labels = prettyNum
)
}
} else {
if (has_zeroinf) {
} else {
p <- p + scale_y_continuous(
limits = axis.scaling$axis.lim,
breaks = axis.scaling$ticks,
labels = axis.scaling$ticks
)
}
}
# set colors
p <- p +
scale_colour_manual(values = col_check2(geom.colors, col.len)) +
scale_fill_manual(values = col_check2(geom.colors, col.len))
# facets?
if (obj_has_name(dat, "facet") && dplyr::n_distinct(dat$facet, na.rm = TRUE) > 1)
p <- p +
facet_grid(~facet)
else if (has_zeroinf)
p <- p +
facet_wrap(~wrap.facet, ncol = 1, scales = "free")
# set axis and plot titles
if (length(axis.title) > 1) axis.title <- axis.title[1]
p <-
p + labs(
x = NULL,
y = axis.title,
title = title
)
# for multinomial models, set response variable name as name for legend
if (multinomial) p <- p + labs(colour = insight::find_response(model))
p
}
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