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plot_model_estimates <- function(model,
dat,
tf,
se,
terms,
group.terms,
rm.terms,
sort.est,
title,
axis.title,
axis.labels,
axis.lim,
grid.breaks,
show.intercept,
show.values,
show.p,
value.offset,
digits,
geom.colors,
geom.size,
line.size,
bpe.style,
bpe.color,
term.order,
vline.color,
value.size,
facets,
p.threshold,
ci.style,
...) {
# remove intercept(s) from output
if (!show.intercept) {
ints1 <- string_contains("(Intercept", x = dat$term)
ints2 <- string_contains("b_Intercept", x = dat$term)
ints3 <- string_contains("b_zi_Intercept", x = dat$term)
ints4 <- which(dat$term %in% "Intercept")
ints <- c(ints1, ints2, ints3, ints4)
if (!sjmisc::is_empty(ints))
dat <- dplyr::slice(dat, !! -ints)
}
# remove non-coefficients
noncoef <- string_contains("Log(theta)", x = dat$term)
if (!sjmisc::is_empty(noncoef)) dat <- dplyr::slice(dat, !! -noncoef)
# exponentiation
if (!is.null(tf) && !is.stan(model)) {
# no transformation if standard errors should be reported
# instead of conf. int.
if (isTRUE(se)) {
message("If standard errors are requested, no transformation is applied to estimates.")
tf <- NULL
} else {
funtrans <- match.fun(tf)
dat[["estimate"]] <- funtrans(dat[["estimate"]])
dat[["conf.low"]] <- funtrans(dat[["conf.low"]])
dat[["conf.high"]] <- funtrans(dat[["conf.high"]])
}
}
# use standard error instead of ci's?
if (isTRUE(se)) {
dat[["conf.low"]] <- dat[["estimate"]] - dat[["std.error"]]
dat[["conf.high"]] <- dat[["estimate"]] + dat[["std.error"]]
}
# remove further estimates
terms <- parse_terms(terms)
filter.remove <- dat$term %in% terms
if (!is.null(terms)) dat <- dplyr::filter(dat, !! filter.remove)
# or select further estimates
rm.terms <- parse_terms(rm.terms)
filter.remove <- !(dat$term %in% rm.terms)
if (!is.null(rm.terms)) dat <- dplyr::filter(dat, !! filter.remove)
# add p-asterisks to data
dat$p.stars <- get_p_stars(dat$p.value, p.threshold)
dat$p.label <- sprintf("%.*f", digits, dat$estimate)
if (show.p) dat$p.label <- sprintf("%s %s", dat$p.label, dat$p.stars)
# create default grouping, depending on the effect:
# split positive and negative associations with outcome
# into different groups
treshold <- dplyr::if_else(isTRUE(tf == "exp"), 1, 0)
dat$group <- dplyr::if_else(dat$estimate > treshold, "pos", "neg")
# group estimates?
if (!is.null(group.terms)) {
if (length(group.terms) == nrow(dat)) {
dat$group <- as.character(group.terms)
} else {
warning("Length of `group.terms` does not equal number of model coefficients. Ignoring this argument.", call. = F)
group.terms <- NULL
}
}
# make term name categorical, for axis labelling
dat$term <- as.factor(dat$term)
# does user want a specific order for terms?
ordered.terms <- FALSE
if (!is.null(term.order)) {
if (length(term.order) == nrow(dat)) {
dat$term <- factor(dat$term, levels = unique(dat$term)[rev(term.order)])
sort.est <- FALSE
ordered.terms <- TRUE
} else {
message("Number of values in `order.terms` does not match number of terms. Terms are not sorted.")
}
}
# sort estimates by effect size
if (isTRUE(sort.est)) {
if (!is.null(group.terms))
dat$term <- factor(dat$term, levels = unique(dat$term[order(dat$group)]))
else
dat$term <- factor(dat$term, levels = unique(dat$term[order(dat$estimate)]))
} else if (!ordered.terms) {
dat$term <- factor(dat$term, levels = rev(unique(dat$term)))
}
# set default colors. for grouped predictors we need more color values
if (is.null(geom.colors)) geom.colors <- dplyr::if_else(is.null(group.terms), "grey30", "Set1")
# for brms multilevel with multiple random intercepts, we need
# special handling
if (is.stan(model) && stan.has.multiranef(dat)) {
# split data, create data frame for each random intercept
dat <- purrr::map(split(dat, f = dat$facet), ~ sjmisc::remove_var(.x, "facet"))
# random intercept names are default titles
ri.titles <- names(dat)
# create plots
purrr::map2(
dat,
1:length(dat),
function(x, y) {
# now we need a named vector, in order
# to match labels and term order at axis
labs <- as.character(x$term)
names(labs) <- labs
# sort terms
if (!is.null(sort.est)) {
reihe <- order(x$estimate)
} else {
reihe <- 1:nrow(x)
}
x$reihe <- order(reihe)
x$term <- factor(x$term, levels = unique(x$term[order(x$reihe)]))
# plot title
if (sjmisc::is_empty(title)) {
ptitle <- ri.titles[y]
} else {
if (length(title) >= y)
ptitle <- title[y]
else
ptitle <- title
}
# plot random effects
plot_point_estimates(
model = model,
dat = x,
tf = tf,
title = ptitle,
axis.labels = labs,
axis.title = NULL,
axis.lim = axis.lim,
grid.breaks = grid.breaks,
show.values = show.values,
value.offset = value.offset,
geom.size = geom.size,
line.size = line.size,
geom.colors = geom.colors,
vline.color = vline.color,
value.size = value.size,
facets = facets,
bpe.style = bpe.style,
bpe.color = bpe.color,
ci.style = ci.style,
...
)
}
)
} else {
if (obj_has_name(dat, "wrap.facet") && dplyr::n_distinct(dat$wrap.facet, na.rm = TRUE) > 1 && !facets) {
dat <- purrr::map(split(dat, f = dat$wrap.facet), ~ sjmisc::remove_var(.x, "wrap.facet"))
if (length(axis.title) == 1) axis.title <- c(axis.title, "Odds Ratios")
purrr::pmap(list(dat, axis.title, names(dat)), function(.x, .y, .z) {
plot_point_estimates(
model = model,
dat = .x,
tf = tf,
title = paste0(title, " (", .z, ")"),
axis.labels = axis.labels,
axis.title = .y,
axis.lim = NULL,
grid.breaks = NULL,
show.values = show.values,
value.offset = value.offset,
geom.size = geom.size,
line.size = line.size,
geom.colors = geom.colors,
bpe.style = bpe.style,
bpe.color = bpe.color,
vline.color = vline.color,
value.size = value.size,
facets = facets,
ci.style = ci.style,
...
)
})
} else {
plot_point_estimates(
model = model,
dat = dat,
tf = tf,
title = title,
axis.labels = axis.labels,
axis.title = axis.title,
axis.lim = axis.lim,
grid.breaks = grid.breaks,
show.values = show.values,
value.offset = value.offset,
geom.size = geom.size,
line.size = line.size,
geom.colors = geom.colors,
bpe.style = bpe.style,
bpe.color = bpe.color,
vline.color = vline.color,
value.size = value.size,
facets = facets,
ci.style = ci.style,
...
)
}
}
}
|
