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plot_type_slope <- function(model,
terms,
rm.terms,
ci.lvl,
colors,
show.data,
jitter,
facets,
axis.title,
case,
useResiduals,
...) {
alpha <- .2
show.loess <- TRUE
if (missing(facets)) facets <- TRUE
# additional arguments?
add.args <- lapply(match.call(expand.dots = F)$`...`, function(x) x)
if ("alpha" %in% names(add.args)) alpha <- eval(add.args[["alpha"]])
if ("show.loess" %in% names(add.args)) show.loess <- eval(add.args[["show.loess"]])
# set color defaults
colors <- col_check2(colors, if (isTRUE(show.loess)) 3 else 2)
if (isTRUE(show.loess)) {
lineColor <- colors[1]
loessLineColor <- colors[2]
pointColor <- colors[3]
} else {
lineColor <- colors[1]
pointColor <- colors[2]
}
# retrieve amount of predictor variables and
# retrieve column names of dataset so we can identify in which
# column the data for each predictor is.
model_data <- insight::get_data(model, verbose = FALSE)
depvar.label <- sjlabelled::get_label(model_data[[1]], def.value = insight::find_response(model), case = case)
predvars <- insight::find_predictors(model, component = "conditional", flatten = TRUE)
# tell user that interaction terms are not supported by this method
if (sjmisc::str_contains(deparse(stats::formula(model)), c(":", "*"), logic = "or")) {
warning("Interaction terms are not supported by this plot type. Output for interaction terms may be inappropriate.", call. = F)
}
# remove estimates?
if (!is.null(rm.terms)) {
remcols <- match(rm.terms, predvars)
if (!sjmisc::is_empty(remcols))
predvars <- predvars[-remcols]
}
# select specific setimates?
if (!is.null(terms)) {
remcols <- match(terms, predvars)
if (!sjmisc::is_empty(remcols))
predvars <- predvars[remcols]
}
# retrieve name of dependent variable
response <- ifelse(isTRUE(useResiduals), "residuals", depvar.label)
# iterate all predictors
mydat <- suppressWarnings(purrr::map_df(predvars, function(p_v) {
# make sure we have the variable in our data. for mixed
# models, we might have some random effects which were not
# in the model frame
if (obj_has_name(model_data, p_v)) {
if (useResiduals) {
data_frame(
x = sjlabelled::as_numeric(model_data[[p_v]]),
y = stats::residuals(model),
group = sjlabelled::get_label(model_data[[p_v]], def.value = p_v, case = case)
)
} else {
data_frame(
x = sjlabelled::as_numeric(model_data[[p_v]]),
y = insight::get_response(model),
group = sjlabelled::get_label(model_data[[p_v]], def.value = p_v, case = case)
)
}
}
}))
# facets, all in one plot
if (facets) {
p <- ggplot(mydat, aes(x = .data$x, y = .data$y)) +
stat_smooth(
method = "lm", se = !is.na(ci.lvl), colour = lineColor,
fill = lineColor, alpha = alpha, level = ci.lvl
)
if (isTRUE(show.loess))
p <- p + stat_smooth(method = "loess", colour = loessLineColor, se = FALSE)
# plot raw data if requested
if (show.data) {
if (!is.null(jitter))
p <- p + geom_jitter(alpha = .2, colour = pointColor, shape = 16, width = jitter)
else
p <- p + geom_point(alpha = .2, colour = pointColor, shape = 16)
}
p <- p + facet_wrap(~group, scales = "free")
# set plot labs
p <- p + labs(x = NULL, y = response)
} else {
p <- list()
for (p_v in unique(mydat$group)) {
dat <- dplyr::filter(mydat, .data$group == !! p_v)
pl <- ggplot(dat, aes(x = .data$x, y = .data$y)) +
stat_smooth(
method = "lm", se = !is.na(ci.lvl), colour = lineColor,
fill = lineColor, alpha = alpha, level = ci.lvl
)
if (isTRUE(show.loess))
pl <- pl + stat_smooth(method = "loess", colour = loessLineColor, se = FALSE)
# plot raw data if requested
if (show.data)
pl <- pl + geom_point(alpha = .2, colour = pointColor, shape = 16)
# set plot labs. check if we have custom axis titles
if (!is.null(axis.title)) {
if (is.list(axis.title)) {
xt <- axis.title[[length(p) + 1]][1]
yt <- axis.title[[length(p) + 1]][2]
} else {
xt <- axis.title[1]
yt <- axis.title[2]
}
} else {
xt <- p_v
yt <- response
}
pl <- pl +
labs(x = xt, y = yt)
# add plot object to list
p[[length(p) + 1]] <- pl
}
}
p
}
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