mc_heatmap | R Documentation |
metaconfoundr()
summariesmc_heatmap()
and mc_trafficlight()
visualize the results of
metaconfoundr()
, summarizing the quality of confounder control in each
study.
mc_heatmap( .df, legend_title = "control quality", sort = FALSE, by_group = FALSE, score = c("adequate", "sum", "controlled"), non_confounders = FALSE ) mc_trafficlight( .df, size = 8, legend_title = "control quality", sort = FALSE, by_group = FALSE, score = c("adequate", "sum", "controlled"), non_confounders = FALSE )
.df |
A data frame, usually the result of |
legend_title |
The legend title |
sort |
Logical. Sort by confounder score? Calculated by |
by_group |
Logical. If sorted, sort within domain? |
score |
The approach used to calculate the score. |
non_confounders |
Logical. Include non-confounders? Default is |
size |
The size of the points in the traffic light plot |
a ggplot
Other plots:
facet_constructs()
,
geom_cochrane()
,
scale_fill_cochrane()
,
theme_mc()
ipi %>% metaconfoundr() %>% dplyr::mutate(variable = stringr::str_wrap(variable, 10)) %>% mc_heatmap() + theme_mc() + facet_constructs() + ggplot2::guides(x = ggplot2::guide_axis(n.dodge = 2)) ipi %>% metaconfoundr() %>% mc_trafficlight() + geom_cochrane() + facet_constructs() + scale_fill_cochrane() + theme_mc() + ggplot2::guides(x = ggplot2::guide_axis(n.dodge = 2))
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