This function takes the results from `mr()`

and is particularly useful
if the MR has been applied using multiple exposures and multiple outcomes.
It creates a new data frame with the following:

Variables: exposure, outcome, category, outcome sample size, effect, upper ci, lower ci, pval, nsnp

only one estimate for each exposure-outcome

exponentiated effects if required

format_mr_results(
mr_res,
exponentiate = FALSE,
single_snp_method = "Wald ratio",
multi_snp_method = "Inverse variance weighted",
ao_slc = T,
priority = "Cardiometabolic"
)

## Arguments

mr_res |
Results from `mr()` . |

exponentiate |
Convert effects to OR? The default is `FALSE` . |

single_snp_method |
Which of the single SNP methods to use when only 1 SNP was used to estimate the causal effect? The default is `"Wald ratio"` . |

multi_snp_method |
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"` . |

ao_slc |
Logical; retrieve sample size and subcategory using `available_outcomes()` . If set to `FALSE` `mr_res` must contain the following additional columns: `subcategory` and `sample_size` . |

priority |
Name of category to prioritise at the top of the forest plot. The default is `"Cardiometabolic"` . |

## Value

data frame.

## Details

By default it uses the `available_outcomes()`

function to retrieve the study level characteristics for the outcome trait,
including sample size and outcome category.
This assumes the MR analysis was performed using outcome GWAS(s) contained in MR-Base.

If `ao_slc`

is set to `TRUE`

then the user must supply their own study level characteristics.
This is useful when the user has supplied their own outcome GWAS results (i.e. they are not in MR-Base).