Harmonise exposure and outcome for multivariable MR

Usage

mv_harmonise_data(exposure_dat, outcome_dat, harmonise_strictness = 2)

exposure_dat: Output from mv_extract_exposures().
outcome_dat: Output from extract_outcome_data(exposure_dat$SNP, id_output).
harmonise_strictness: See the action option of harmonise_data(). The default is 2.

List of vectors and matrices required for mv analysis.

exposure_beta: a matrix of beta coefficients, in which rows correspond to SNPs and columns correspond to exposures.
exposure_se: is the same as exposure_beta, but for standard errors.
exposure_pval: the same as exposure_beta, but for p-values.
expname: A data frame with two variables, id.exposure and exposure which are character strings.
outcome_beta: an array of effects for the outcome, corresponding to the SNPs in exposure_beta.
outcome_se: an array of standard errors for the outcome.
outcome_pval: an array of p-values for the outcome.
outname: A data frame with two variables, id.outcome and outcome which are character strings.