Perform MR of multiple exposures and multiple outcomes. This plots the results.

forest_plot(
mr_res,
exponentiate = FALSE,
single_snp_method = "Wald ratio",
multi_snp_method = "Inverse variance weighted",
group_single_categories = TRUE,
by_category = TRUE,
in_columns = FALSE,
threshold = NULL,
xlab = "",
xlim = NULL,
trans = "identity",
ao_slc = T,
priority = "Cardiometabolic"
)

## Arguments

mr_res Results from mr(). Convert effects to OR? Default is FALSE. Which of the single SNP methosd to use when only 1 SNP was used to estimate the causal effect? The default is "Wald ratio". Which of the multi-SNP methods to use when there was more than 1 SNPs used to estimate the causal effect? The default is "Inverse variance weighted". If there are categories with only one outcome, group them together into an "Other" group. The default is TRUE. Separate the results into sections by category? The default is TRUE. Separate the exposures into different columns. The default is FALSE. p-value threshold to use for colouring points by significance level. If NULL (default) then colour layer won't be applied. x-axis label. If in_columns=TRUE then the exposure values are appended to the end of xlab. e.g. if xlab="Effect of" then x-labels will read "Effect of exposure1", "Effect of exposure2" etc. Otherwise will be printed as is. limit x-axis range. Provide vector of length 2, with lower and upper bounds. The default is NULL. Transformation to apply to x-axis. e.g. "identity", "log2", etc. The default is "identity". retrive sample size and subcategory from available_outcomes(). If set to FALSE then mr_res must contain the following additional columns: sample_size and subcategory. The default behaviour is to use available_outcomes() to retrieve sample size and subcategory. Name of category to prioritise at the top of the forest plot. The default is "Cardiometabolic".

grid plot object